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Abir Chaabani

Informations générales

Abir Chaabani
Grade

Maître Assistant Habilité

Biographie courte

Abir Chaabani received the B.Sc., M.Sc., Ph.D., and Habilitation degrees in Computer Science with Business from the University of Tunis, ISG-Tunis, in 2011, 2013, 2017, and 2023, respectively. She is currently an Assistant Professor of Business Computing at ENICarthage, University of Carthage, and a research member of SMART Lab at ISG Tunis, University of Tunis.

Her current research focuses on advanced optimization methods, particularly bi-level optimization, multi-objective optimization, evolutionary computation, and machine learning. With more than 26 publications and growing recognition in her field, Dr. Chaabani actively contributes to both the theoretical development of optimization algorithms and their practical applications in logistics, supply chain management, scheduling, and home health care.

She has published several scientific articles in top indexed conferences such as the Genetic and Evolutionary Computation Conference and the IEEE Congress on Evolutionary Computation. In addition, she has published in high-impact journals including IEEE Transactions on Cybernetics, Engineering Optimization, Soft Computing, and Applied Intelligence.

Dr. Chaabani also serves as a reviewer for several international journals, such as IEEE Transactions on Evolutionary Computation, Swarm and Evolutionary Computation, and Soft Computing, as well as for international conferences including the IEEE Congress on Evolutionary Computation. Moreover, she has been a member of the program and organizing committees of several international conferences.

 

Équipes

Axes de recherche

Evolutionary optimization
Multi-Objective Optimization
Artificial Intelligence

Publications

  • 2025
    Hamida Labidi, Abir Chaabani, Nadia Ben Azzouna

    Hybrid Genetic Algorithm for Solving an Online Vehicle Routing Problem with Time Windows and Heterogeneous Fleet

    This paper proposes a hybrid genetic algorithm to address an online vehicle routing problem with time windows and a heterogeneous fleet, presented at Hybrid Intelligent Systems (HIS 2023)., 2025

    Résumé

    The Vehicle Routing Problem (VRP) is a well-known optimization problem in which we aim traditionally to minimize transportation costs while satisfying customer demands. In fact, most logistics companies use a heterogeneous fleet with varying capacities and costs, presenting a more complex variant known as Rich VRP (RVRP). In this paper, we present a mathematical formulation of the RVRP, considering both hard time windows and dynamically changing requests to be as close as possible to real-life logistics scenarios. To solve this challenging problem, we propose a Hybrid Genetic Algorithm (HGA). The experimental study highlights the out-performance of our proposal when evaluated alongside other algorithms on the same benchmark problems. Additionally, we conduct a sensitivity analysis to illustrate how resilient the algorithm is when problem parameters are altered.

  • Abir Chaabani, Lamjed Ben Said

    Solving Hierarchical Production–Distribution Problem Based on MDVRP Under Flexibility Depot Resources in Supply Chain Management

    In: Alharbi, I., Ben Ncir, CE., Alyoubi, B., Ben-Romdhane, H. (eds) Advances in Computational Logistics and Supply Chain Analytics. Unsupervised and Semi-Supervised Learning. Springer, Cham,129--147.., 2024

    Résumé

    Bi-level optimization problems (BLOPs) is a class of challenging problems with two levels of optimization tasks. The particular structure of the bi-level optimization model facilitates the formulation of several practical situations that involve hierarchical decision-making process where lower-level decisions depend on upper-level actions. In this context, a hierarchical production–distribution (PD) planning problem in supply management is addressed. These two entities (production and distribution) are naturally related; however, in most practical situations, each decision entity concentrates on optimizing its process one at a time, independently on other related decisions. In this chapter, we considered a new formulation of the PD system using the bi-level framework under the constraints of shared depots resources in the distribution phase. To this end, a mixed integer bi-level formulation is proposed to model the problem, and a cooperative decomposition-based algorithm is developed to solve the bi-level model. Statistical experimental results show that our proposed algorithm gives competitive and better results with respect to the competitor algorithm. Indeed, allowing flexible choice of the stop depot reveals the outperformance of our proposal in reducing total traveling cost of generated solution compared to the baseline problem.

    Lilia Rejeb, Abir Chaabani, Hajer Safi, Lamjed Ben Said

    Multimodal freight transport optimization based on economic and ecological constraint

    . In: Alharbi, I., Ben Ncir, CE., Alyoubi, B., Ben-Romdhane, H. (eds) Advances in Computational Logistics and Supply Chain Analytics. Unsupervised and Semi-Supervised Learning. Springer, Cha, 2024

    Résumé

    The increasing demand for efficient global supply chain management and faster product delivery has led to a rise in the use of multimodal transportation systems (MFT). One of the key challenges in multimodal transportation is selecting the appropriate freight mode. This decision depends on several factors such as cost, transit time, reliability, mode availability, service frequency, and cargo characteristics. However, existing research often focuses on only two modes, namely trucks and trains, which fails to capture the complexities of real-world freight transportation decisions. Moreover, while reducing travel time and cost are primary objectives for service providers and researchers, other important considerations such as environmental impact are often overlooked. To this end, in this work, the researchers take into account four major modes of transportation (Air, Road, Rail, and Sea/Water) in a multimodal freight context aiming to optimize three distinct objectives: overall transportation cost, transportation time, and CO2 emissions. To solve this problem, the researchers adopt two the well-known metaheuristic algorithms: Tabu Search and the Genetic Algorithm through an experimental study demonstrating the efficacy of these evolutionary solution methods in tackling such challenging optimization problems.

    Abir Chaabani, Sarra Jeddi, Lamjed Ben Said

    A New Bi-level Modeling for the Home Health Care Problem Considering Patients Preferences

    International Conference on Control Decision and Information Technology Codit’10, Vallette, Malta, 2721-2726, 2024

    Résumé

    Home Health Care (HHC) aims to provide medical care and support services directly to patients in their own homes. The demand for HHC services is steadily increasing due to demographic trends, with a growing preference for receiving care in the home. This trend pushes organizations providing home health care services, to optimize their activities in order to meet this increasing demand efficiently. For this purpose, we propose in this work a new bi-level modeling of the problem, that we termed Bi-level Home Health Care Problem Considering Patients Preferences (Bi-HHCPP) aiming to find an efficient solution corresponding to this design. Existing research studies have focused on optimizing the problem considering only one decision-maker that optimizes both routing and scheduling entities imposed by the problem. This paper is the first to shed light on a new bi-level modeling of the problem involving two hierarchical decision entities: (1) a scheduling entity, and (2) a routing one. The proposed model primarily accounts for nurse qualification, travel costs, and patient preferences on visited nurses. Besides, the proposed mathematical formulation of the problem is tested using the CBC (Coin-or Branch and Cut) optimization solver.

    Laibidi Hamida, Abir Chaabani, Nadia Ben Azzouna, Hassine Khaled

    Hybrid genetic algorithm for solving an online vehicle routing problem with time windows and heterogeneous fleet

    23rd International Conference on Hybrid Intelligent Systems (HIS'23), 437-446, Springer Nature Switzerland, 2024

    Résumé

    The Vehicle Routing Problem (VRP) is a well-known optimization problem in which we aim traditionally to minimize transportation costs while satisfying customer demands. In fact, most logistics companies use a heterogeneous fleet with varying capacities and costs, presenting a more complex variant known as Rich VRP (RVRP). In this paper, we present a mathematical formulation of the RVRP, considering both hard time windows and dynamically changing requests to be as close as possible to real-life logistics scenarios. To solve this challenging problem, we propose a Hybrid Genetic Algorithm (HGA). The experimental study highlights the out-performance of our proposal when evaluated alongside other algorithms on the same benchmark problems. Additionally, we conduct a sensitivity analysis to illustrate how resilient the algorithm is when problem parameters are altered.

  • Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

    Journal of Supercomputing,1-38,, 2023

    Résumé

    Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.

    Abir Chaabani, Mouna Karaja, Lamjed Ben Said

    An Efficient Non-Dominated Sorting Genetic Algorithm for Multi-objective Optimization

    International Conference on Control Decision and Information Technology Codit’9, Rome, 1565-1570, 2023

    Résumé

    Multi-Objective Evolutionary Algorithms (MOEAs) is actually one of the most attractive and active research field in computer science. Significant research has been conducted in handling complex multi-objective optimization problems within this research area. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) has garnered significant attention in various domains, emphasizing its specific popularity. However, the complexity of this algorithm is found to be O(MN2) with M objectives and N solutions, which is considered computationally demanding. In this paper, we are proposing a new variant of NSGA-II termed (Efficient-NSGA-II) based on our recently proposed quick non-dominated sorting algorithm with quasi-linear average time complexity; thereby making the NSGA-II algorithm efficient from a computational cost viewpoint. Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. Moreover, comparisons results against other multi-objective algorithms on a variety of benchmark problems show the effectiveness and the efficiency of this multi-objective version

    Wiem Ben Ghozzi, Abir Chaabani, Zahra Kodia, Lamjed Ben Said

    DeepCNN-DTI: A Deep Learning Model for Detecting Drug-Target Interactions

    International Conference on Control Decision and Information Technology Codit’9, Rome, 2023

    Résumé

    Drug target interaction is an important area of drug discovery, development, and repositioning. Knowing that in vitro experiments are time-consuming and computationally expensive, the development of an efficient predictive model is a promising challenge for Drug-Target Interactions (DTIs) prediction. Motivated by this problem, we propose in this paper a new prediction model called DeepCNN-DTI to efficiently solve such complex real-world activities. The main motivation behind this work is to explore the advantages of a deep learning strategy with feature extraction techniques, resulting in an advanced model that effectively captures the complex relationships between drug molecules and target proteins for accurate DTIs prediction. Experimental results generated based on a set of data in terms of accuracy, precision, sensitivity, specificity, and F1-score demonstrate the superiority of the model compared to other competing learning strategies.

    Abir Chaabani, Mouna Karaja, Lamjed Ben Said

    An Efficient Non-dominated Sorting Genetic Algorithm For Multi-objective Optimization.

    9th International Conference on Control, Decision and Information Technologies, CoDIT 2023, Rome, Italy., 2023

    Résumé

    Multi-Objective Evolutionary Algorithms (MOEAs) is actually one of the most attractive and active research field in computer science. Significant research has been conducted in handling complex multi-objective optimization problems within this research area. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) has garnered significant attention in various domains, emphasizing its specific popularity. However, the complexity of this algorithm is found to be O(MN2) with M objectives and N solutions, which is considered computationally demanding. In this paper, we are proposing a new variant of NSGA-II termed (Efficient-NSGA-II) based on our recently proposed quick non-dominated sorting algorithm with quasi-linear average time complexity; thereby making the NSGA-II algorithm efficient from a computational cost viewpoint. Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. Moreover, comparisons results against other multi-objective algorithms on a variety of benchmark problems show the effectiveness and the efficiency of this multi-objective version.

    Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

    J Supercomput 79, 17716–17753 (2023), 2023

    Résumé

    Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.

  • Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Efficient bilevel multi-objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

    Applied Intelligence, 1-29, 2022

    Résumé

    Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.

    Malek Abbassi, Abir Chaabani, Lamjed Ben Said

    An efficient chemical reaction algorithm for multi-objective combinatorial bi-level optimization

    Engineering Optimization, 54(4), 665-686, 2022

    Résumé

    The Bi-Level Optimization Problem (BLOP) is defined as a mathematical program with two nested optimization tasks. Although many applications fit the bi-level framework, however, existing resolution methods were most proposed to solve single-objective bi-level problems. Regarding Multi-objective BLOPs (MBLOPs), there do not exist too many previous studies because of the difficulties associated with solving these complex problems. Additionally, a recently proposed metaheuristic, called Non-dominated sorting Chemical Reaction Optimization (NCRO), has been successfully applied to solve single-level Multi-Objective Problems (MOPs). NCRO applies a quick-non-dominated sorting technique that makes it one of the most powerful search algorithms in solving MOPs. Based on these observations, a new Bi-level Multi-objective CRO method, called BMCRO, is proposed in this article for solving MBLOPs. The main idea behind BMCRO is to come up with good solutions in an acceptable execution time within the bi-level framework. Experimental results on well-established benchmarks reveal the outperformance of the proposed algorithm against a bi-level variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) which is developed for this purpose.

    Malek Abbassi, Abir Chaabani, Lamjed Ben Said

    An elitist cooperative evolutionary bi-level multi-objective decomposition-based algorithm for sustainable supply chain

    International Journal of Production Research, 60(23), 7013-7032, 2022

    Résumé

    Many real-life applications are modelled using hierarchical decision-making in which: an upper-level optimisation task is constrained by a lower-level one. Such class of optimisation problems is referred in the literature as Bi-Level Optimisation Problems (BLOPs). Most of the proposed methods tackled the single-objective continuous case adhering to some regularity assumptions. This is at odds with real-world problems which involve mainly discrete variables and expensive objective function evaluations. Besides, the optimisation process becomes exorbitantly time-consuming, especially when optimising several objectives at each level. For this reason, the Multi-objective variant (MBLOP) remains relatively less explored and the number of methods tackling the combinatorial case is much reduced. Motivated by these observations, we propose in this work an elitist decomposition-based evolutionary algorithm to solve MBLOPs, called ECODBEMA. The basic idea of our proposal is to handle, decomposition, elitism and multithreading mechanisms to cope with the MBLOP's high complexity. ECODBEMA is applied to the production–distribution problem and to a sustainable end-of-life products disassembly case-study based on real-data of Aix-en-Provence French city. We compared the optimal solutions of an exact method using CPLEX solver with near-optimal solutions obtained by ECODBEMA. The statistical results show the significant outperformance of ECODBEMA against other multi-objective bi-level optimisation algorithms.

    Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment.

    Appl Intell 53, 9009–9037 (2023), 2022

    Résumé

    Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.

  • Malek Abbassi, Abir Chaabani, Lamjed Ben Said, Nabil Absi

    An Approximation-based Chemical Reaction Algorithm for Combinatorial Multi-Objective Bi-level Optimization Problems

    IEEE Congress on Evolutionary Computation, 1627-1634, 2021

    Résumé

    Multi-objective Bi-Level Optimization Problem (MBLOP) is defined as a mathematical program where one multi-objective optimization task is constrained with another one. In this way, the evaluation of a single upper level solution necessitates the evaluation of the whole lower level problem. This fact brings new complexities to the bi-level framework, added to the conflicting objectives and their evaluation which need a large number of Function Evaluations (FEs). Despite the number of works dedicated to solve bi-level optimization problems, the number of methods applied to the multi-objective combinatorial case is much reduced. Motivated by these observations, we propose in this paper an approximation-based version of our recently proposed Bi-level Multi-objective Chemical Reaction Optimization (BMCRO), which we called BMCROII. The approximation technique is adopted here as a surrogate to the lower level leading then to generate efficiently the lower level optimality. Our choice is justified by two main arguments. First, BMCRO applies a Quick Non-Dominated Sorting Algorithm (Q-NDSA) with quasi-linear computational time complexity. Second, the number of FEs savings gained by the approximation technique can hugely improve the whole efficiency of the method. The proposed algorithm is applied to a new multi-objective formulation of the well-known Bi-level Multi Depot Vehicle Routing Problem (BMDVRP). The statistical analysis demonstrates the outperformance of our algorithm compared to prominent baseline algorithms available in literature. Indeed, a large number of savings are detected which confirms the merits of our proposal for solving such type of NP-hard problems.

  • Ameni Azzouz, Abir Chaabani, Meriem Ennigrou, Lamjed Ben Said

    Handling Sequence-dependent Setup Time Flexible Job Shop Problem with Learning and Deterioration Considerations using Evolutionary Bi-level Optimization

    Applied Artificial Intelligence, 34(6), 433-455, 2020

    Résumé

    Bi-level optimization is a challenging research area that has received significant attention from researchers to model enormous NP-hard optimization problems and real-life applications. In this paper, we propose a new evolutionary bi-level algorithm for Flexible Job Shop Problem with Sequence-Dependent Setup Time (SDST-FJSP) and learning/deterioration effects. There are two main motivations behind this work. On the one hand, learning and deterioration effects might occur simultaneously in real-life production systems. However, there are still ill posed in the scheduling area. On the other hand, bi-level optimization was presented as an interesting resolution scheme easily applied to more complex problems without additional modifications. Motivated by these issues, we attempt in this work to solve the FJSP variant using the bi-level programming framework. We suggest firstly a new bi-level mathematical formulation for the considered FJSP; then we propose a bi-level evolutionary algorithm to solve the problem. The experimental study on well-established benchmarks assesses and validates the advantage of using a bi-level scheme over the compared approaches in this research area to solve such NP-hard problem.

    Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    A co-evolutionary hybrid decomposition-based algorithm for bi-level combinatorial optimization problems.

    Soft Computing, 24(10), 7211-7229, 2020

    Résumé

    Bi-level programming problems are a special class of optimization problems with two levels of optimization tasks. These problems have been widely studied in the literature and often appear in many practical problem solving tasks. Although many applications fit the bi-level framework, however, real-life implementations are scarce, due mainly to the lack of efficient algorithms able to handle effectively this NP-hard problem. Several solution approaches have been proposed to solve these problems; however, most of them are restricted to the continuous case. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-based Algorithm (CODBA) to solve bi-level combinatorial problems. CODBA scheme has been able to bring down the computational expense significantly as compared to other competitive approaches within this research area. In this paper, we further improve CODBA approach by incorporating a local search procedure to make the search process more efficient. The proposed extension called CODBA-LS includes a variable neighborhood search to the lower-level task to help in faster convergence of the algorithm. Further experimental tests based on the bi-level production–distribution problems in supply chain management model on a set of artificial and real-life data turned out to be effective on both computation time and solution quality.

    Abir Chaabani, Lamjed Ben Said

    A co-evolutionary decomposition-based algorithm for the bi-level knapsack optimization problem

    International Journal of Computational Intelligence Studies, 2020

    Résumé

    Bi-level optimisation problems (BOPs) are a class of challenging problems with two levels of optimisation tasks. These problems allow to model a large number of real-life situations in which a first decision maker, hereafter the leader, optimises his objective by taking the follower's response to his decisions explicitly into account. In this context, a new proposed algorithm called CODBA-II was suggested to solve combinatorial BOPs. The latter was able to improve the quality of generated bi-level solutions regarding to recently proposed methods. In fact, a wide range of applications fit the bi-level programming framework and real-life implementations still scarce. For this reason, we propose in this paper a co-evolutionary decomposition-based bi-level algorithm for the bi-level knapsack optimisation problem. The computational algorithm turned out to be quite efficient on both computation time and solution quality regarding to other competitive EAs.

    Malek Abbassi, Abir Chaabani, Lamjed Ben Said, Nabil Absi

    Bi-level multi-objective combinatorial optimization using reference approximation of the lower-level reaction.

    International conference on Knowledge Based and Intelligent information and Engineering Systems (On Line), 2098-2107, 2020

    Résumé

    Bi-level optimization has gained a lot of interest during the last decade. This framework is suitable to model several real-life situations. Bi-level optimization problems refer to two related optimization tasks, each one is assigned to a decision level (i.e., upper and lower levels). In this way, the evaluation of an upper level solution requires the evaluation of the lower level. This hierarchical decision making necessitates the execution of a significant number of Function Evaluations (FEs). When dealing with a multi-objective optimization context, new complexities are added and imposed by the conflicting objectives and their evaluation techniques. In this paper, we aim to reduce the induced complexity using approximation techniques in order to obtain the lower level optimality. To this end, ideas from multi-objective optimization have been extracted, improved, and hybridized with evolutionary methods to build an efficient approach for Multi-objective Bi-Level Optimization Problems (MBLOPs). In this work, three techniques are suggested: (1) a complete lower level approximation Pareto front procedure, (2) a reference-based approximation selection procedure, and (3) a sub-set reference-based approximation selection one. The proposed variants are applied to a new multi-objective formulation of a well-known combinatorial problem integrating two systems in the supply chain management, namely, the Bi-level Multi Depot Vehicle Routing Problem (Bi-MDVRP). The statistical analysis demonstrates the efficiency of each algorithm according to a set of metrics. Indeed, a large number of savings are detected which confirms the efficiency of our proposals for solving combinatorial optimization problems.

    Malek Abbassi, Abir Chaabani, Lamjed Ben Said, Nabil Absi

    An improved bi-level multi-objective evolutionary algorithm for the production distribution planning system

    In International Conference on Modeling Decisions for Artificial Intelligence, MDAI’20,, 2020

    Résumé

    Bi-level Optimization Problem (BOP) presents a special class of challenging problems that contains two optimization tasks. This nested structure has been adopted extensively during recent years to solve many real-world applications. Besides, a number of solution methodologies are proposed in the literature to handle both single and multi-objective BOPs. Among the well-cited algorithms solving the multi-objective case, we find the Bi-Level Evolutionary Multi-objective Optimization algorithm (BLEMO). This method uses the elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) with the bi-level framework to solve Multi-objective Bi-level Optimization Problems (MBOPs). BLEMO has proved its efficiency and effectiveness in solving such kind of NP-hard problem over the last decade. To this end, we aim in this paper to investigate the performance of this method on a new proposed multi-objective variant of the Bi-level Multi Depot Vehicle Routing Problem (Bi-MDVRP) which is a well-known problem in combinatorial optimization. The proposed BLEMO adaptation is further improved combining jointly three techniques in order to accelerate the convergence rate of the whole algorithm. Experimental results on well-established benchmarks reveal a good performance of the proposed algorithm against the baseline version.

  • Abir Chaabani, Lamjed Ben Said

    Transfer of learning with the coevolutionary decomposition-based algorithm-II: a realization on the bi-level production-distribution planning system.

    Applied Intelligence, 49(3), 963- 982, 2019

    Résumé

    Bi-Level Optimization Problem (BLOP) is a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem, which has another optimization problem as a constraint. In this way, the evaluation of each upper level solution requires finding an optimal solution to the corresponding lower level problem, which is computationally so expensive. For this reason, most proposed bi-level resolution methods have been restricted to solve the simplest case (linear continuous BLOPs). This fact has attracted the evolutionary computation community to solve such complex problems. Besides, to enhance the search performance of Evolutionary Algorithms (EAs), reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and was demonstrated much promise. Motivated by this observation, we propose in this paper, a memetic version of our proposed Co-evolutionary Decomposition-based Algorithm-II (CODBA-II), that we named M-CODBA-II, to solve combinatorial BLOPs. The main motivation of this paper is to incorporate transfer learning within our recently proposed CODBA-II scheme to make the search process more effective and more efficient. Our proposed hybrid algorithm is investigated on two bi-level production-distribution problems in supply chain management formulated to: (1) Bi-CVRP and (2) Bi-MDVRP. The experimental results reveal a potential advantage of memes incorporation in CODBA-II. Most notably, the results emphasize that transfer learning allows not only accelerating the convergence but also finding better solutions.

    Malek Abbassi, Abir Chaabani, Lamjed Ben Said

    An investigation of a bi-level non-dominated sorting algorithm for production-distribution planning system

    In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA AIE’19, china, 819- 826, 2019

    Résumé

    Bi-Level Optimization Problems (BLOPs) belong to a class of challenging problems where one optimization problem acts as a constraint to another optimization level. These problems commonly appear in many real-life applications including: transportation, game-playing, chemical engineering, etc. Indeed, multi-objective BLOP is a natural extension of the single objective BLOP that bring more computational challenges related to the multi-objective hierarchical decision making. In this context, a well-known algorithm called NSGA-II was presented in the literature among the most cited Multi-Objective Evolutionary Algorithm (MOEA) in this research area. The most prominent features of NSGA-II are its simplicity, elitist approach and a non-parametric method for diversity. For this reason, in this work, we propose a bi-level version of NSGA-II, called Bi-NSGA-II, in an attempt to exploit NSGA-II features in tackling problems involving bi-level multiple conflicting criteria. The main motivation of this paper is to investigate the performance of the proposed variant on a bi-level production distribution problem in supply chain management formulated as a Multi-objective Bi-level MDVRP (M-Bi-MDVRP). The paper reveals three Bi-NSGA-II variants for solving the M-Bi-MDVRP basing on different variation operators (M-VMX, VMX, SBX and RBX). The experimental results showed the remarkable ability of our adopted algorithm for solving such NP-hard problem.

  • Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    A new co-evolutionary decomposition-based algorithm for bi-level combinatorial optimization

    Applied Intelligence, 48(9), 2847-2872, 2018

    Résumé

    Bi-Level Optimization Problems (BLOPs) are a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem which has another optimization problem as a constraint. The latter is called the lower level problem. In this way, the evaluation of each upper level solution requires finding an (near) optimal solution to the corresponding lower level problem, which is computationally very expensive. Many real world applications are bi-level by nature, ranging from logistics to software engineering. Further, proposed bi-level approaches have been restricted to solve linear BLOPs. This fact has attracted the evolutionary computation community to tackle such complex problems and many interesting works have recently been proposed. Unfortunately, most of these works are restricted to the continuous case. Motivated by this observation, we propose in this paper a new Co-evolutionary Decomposition Algorithm inspired from Chemical Reaction Optimization algorithm, called E-CODBA (Energy-based CODBA), to solve combinatorial bi-level problems. Our algorithm is based on our previous works within this research area. The main idea behind E-CODBA is to exploit co-evolution, decomposition, and energy laws to come up with good solution(s) within an acceptable execution time. The statistical analysis of the experimental results on the Bi-level Multi-Depot Vehicle Routing Problem (Bi-MDVRP) show the out-performance of our E-CODBA against four recently proposed works in terms of effectiveness and efficiency.

    Abir Chaabani, Lamjed Ben Said

    Hybrid CODBA-II Algorithm Coupling a Co-Evolutionary Decomposition-Based Algorithm with Local Search Method to Solve Bi-Level Combinatorial Optimization

    International Conference on Tools with Artificial Intelligence ICTAI’18, Volos, 2018

    Résumé

    Bi-level optimization problems (BLOPs) are a class of challenging problems with two levels of optimization tasks. The usefulness of bi-level optimization in designing hierarchical decision processes prompted several researchers, in particular the evolutionary computation community, to pay more attention to such kind of problems. Several solution approaches have been proposed to solve these problems; however, most of them are restricted to the continuous case. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-based Algorithm (CODBA-II) to solve combinatorial bi-level problems. CODBA-II scheme has been able to improve the bi-level performance and to bring down the computational expense significantly as compared to other competitive approaches within this research area. In this paper, we present an extension of the recently proposed CODBA-II algorithm. The improved version, called CODBA-IILS, further improves the algorithm by incorporating a local search process to both upper and lower levels in order to help in faster convergence of the algorithm. The improved results have been demonstrated on two different sets of test problems based on the bi-level production-distribution problems in supply chain management, and comparison results against the contemporary approaches are also provided.

  • Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    A co-evolutionary decomposition-based chemical reaction algorithm for bi-level combinatorial optimization problems.

    International conference on Knowledge Based and Intelligent information and Engineering Systems KES’17, France, 112, 780-789, 2017

    Résumé

    Bi-level optimization problems (BOPs) are a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem which has another optimization problem as a constraint. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level one. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Recently, a new research field, called EBO (Evolutionary Bi-Level Optimization) has appeared thanks to the promising results obtained by the use of EAs (Evolutionary Algorithms) to solve such kind of problems. However, most of these promising results are restricted to the continuous case. The number of existing EBO works for the discrete (combinatorial case) bi-level problems is relatively small when compared to the field of evolutionary continuous BOP. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-Based Algorithm (CODBA) to solve combinatorial bi-level problems. The recently proposed approach applies a Genetic Algorithm to handle BOPs. Besides, a new recently proposed meta-heuristic called CRO has been successfully applied to several practical NP-hard problems. To this end, we propose in this work a CODBA-CRO (CODBA with Chemical Reaction Optimization) to solve BOP. The experimental comparisons against other works within this research area on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of our proposal.

  • Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    A memetic evolutionary algorithm for bi-level combinatorial optimization: a realization between Bi-MDVRP and Bi-CVRP

    IEEE Congress on Evolutionary Computation CEC’16, Canada, 1666-1673, 2016

    Résumé

    Bi-level optimization problems are a class of challenging optimization problems, that contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. In recent decades, it is observed that many efficient optimizations using modern advanced EAs have been achieved via the incorporation of domain specific knowledge. In such a way, the embedment of domain knowledge about an underlying problem into the search algorithms can enhance properly the evolutionary search performance. Motivated by this issue, we present in this paper a Memetic Evolutionary Algorithm for Bi-level Combinatorial Optimization (M-CODBA) based on a new recently proposed CODBA algorithm with transfer learning to enhance future bi-level evolutionary search. A realization of the proposed scheme is investigated on the Bi-CVRP and Bi-MDVRP problems. The experimental studies on well established benchmarks are presented to assess and validate the benefits of incorporating knowledge memes on bi-level evolutionary search. Most notably, the results emphasize the advantage of our proposal over the original scheme and demonstrate its capability to accelerate the convergence of the algorithm.

  • Slim Bechikh, Abir Chaabani, Lamjed Ben Said

    An efficient chemical reaction optimization algorithm for multi-objective optimization

    IEEE transactions on cybernetics, 45(10), 2051-2064, 2015

    Résumé

    Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front.

    Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    A Co-Evolutionary Decomposition-based Algorithm for Bi-Level combinatorial Optimization

    IEEE Congress on Evolutionary Computation CEC’15, Japan, 1659-1666, 2015

    Résumé

    Several optimization problems encountered in practice have two levels of optimization instead of a single one. These BLOPs (Bi-Level Optimization Problems) are very computationally expensive to solve since the evaluation of each upper level solution requires finding an optimal solution for the lower level. Recently, a new research field, called EBO (Evolutionary Bi-Level Optimization) has appeared thanks to the promising results obtained by the use of EAs (Evolutionary Algorithms) to solve such kind of problems. Most of these promising results are restricted to the continuous case. Motivated by this observation, we propose a new bi-level algorithm, called CODBA (CO-Evolutionary Decomposition based Bi-level Algorithm), to tackle combinatorial BLOPs. The basic idea of our CODBA is to exploit decomposition, parallelism, and co-evolution within the lower level in order to cope with the high computational cost. CODBA is assessed on a set of instances of the bi-level MDVRP (MultiDepot Vehicle Routing Problem) and is confronted to two recently proposed bi-level algorithms. The statistical analysis of the obtained results shows the merits of CODBA from effectiveness and efficiency viewpoints.

    Abir Chaabani, Slim Bechikh, Lamjed Ben Said, Radhia Azzouz

    An improved co-evolutionary decomposition-based algorithm for bi-level combinatorial optimization

    Conference on Genetic and Evolutionary Computation GECCO’15, Spain, 1363-1364,, 2015

    Résumé

    Several real world problems have two levels of optimization instead of a single one. These problems are said to be bi-level and are so computationally expensive to solve since the evaluation of each upper level solution requires finding an optimal solution at the lower level. Most existing works in this direction have focused on continuous problems. Motivated by this observation, we propose in this paper an improved version of our recently proposed algorithm CODBA (CO-evolutionary Decomposition-Based Algorithm), called CODBA-II, to tackle bi-level combinatorial problems. Differently to CODBA, CODBA-II incorporates decomposition, parallelism, and co-evolution within both levels: (1) the upper level and (2) the lower one, with the aim to further cope with the high computational cost of the over-all bi-level search process. The performance of CODBA-II is assessed on a set of instances of the MDVRP (Multi-Depot Vehicle Routing Problem) and is compared against three recently proposed bi-level algorithms. The statistical analysis of the obtained results shows the merits of CODBA-II from effectiveness viewpoint.

  • Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    An indicator based chemical reaction optimization algorithm for multi-objective search.

    Genetic and Evolutionary Computation Conference, (GECCO’14), Canada, 85-86, 2014

    Résumé

    In this paper, we propose an Indicator-based Chemical Reaction Optimization (ICRO) algorithm for multiobjective optimization. There are two main motivations behind this work. On the one hand, CRO is a new recently proposed metaheuristic which demonstrated very good performance in solving several mono-objective problems. On the other hand, the idea of performing selection in Multi-Objective Evolutionary Algorithms (MOEAs) based on the optimization of a quality metric has shown a big promise in tackling Multi-Objective Problems (MOPs). The statistical analysis of the obtained results shows that ICRO provides competitive and better results than several other MOEAs.

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