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Supervised machine learning techniques include classification and regression. In regression, the objective is to map a real-valued output to a set of input features. The main challenge that existing methods for regression encounter is how to maintain an accuracy-simplicity balance. Since Regression Trees (RTs) are simple to interpret, many existing works have focused on proposing RT and Model Tree (MT) induction algorithms. MTs are RTs with a linear function at the leaf nodes rather than a numerical value are able to describe the relationship between the inputs and the output. Traditional RT induction algorithms are based on a top-down strategy which often leads to a local optimal solution. Other global approaches based on Evolutionary Algorithms (EAs) have been proposed to induce RTs but they can require an important calculation time which may affect the convergence of the algorithm to the solution. In this paper, we introduce a novel approach called Bi-level Evolutionary Model Tree Induction algorithm for regression, that we call BEMTI, and which is able to induce an MT in a bi-level design using an EA. The upper-level evolves a set of MTs using genetic operators while the lower-level optimizes the Linear Models (LMs) at the leaf nodes of each MT in order to fairly and precisely compute their fitness and obtain the optimal MT. The experimental study confirms the outperformance of our BEMTI compared to six existing tree induction algorithms on nineteen datasets.

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Hyperheuristics represent a generic method that provides a high level of abstraction, enabling solving several problems in the combinatorial optimization domain while reducing the need for human intervention in parameters tuning. This category consists in managing a set of low-level heuristics and attempting to find the optimal sequence that produces high-quality results. This paper proposes a hyperheuristic that simulates the honey bees mating behavior called “Honey bee Mating Optimization HyperHeuristic”  to solve the Patient Admission Scheduling Problem (PASP). The PASP is an NP-hard problem that represents an important field in the health care discipline. In order to perceive the influence of low-level heuristics on the model’s performance, we implemented two versions of the hyperheuristic that each one works on a different set of low-level heuristics. The results show that one of the versions generates better results than the other, revealing the important role of low-level heuristics’ quality leading to enhancing the hyperheuristic performance.

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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.

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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.

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In multi-label classification, each instance could be assigned multiple labels at the same time. In such a situation, the relationships between labels and the class imbalance are two serious issues that should be addressed. Despite the important number of existing multi-label classification methods, the widespread class imbalance among labels has not been adequately addressed. Two main issues should be solved to come up with an effective classifier for imbalanced multi-label data. On the one hand, the imbalance could occur between labels and/or within a label. The “Between-labels imbalance” occurs where the imbalance is between labels however the “Within-label imbalance” occurs where the imbalance is in the label itself and it could occur across multiple labels. On the other hand, the labels’ processing order heavily influences the quality of a multi-label classifier. To deal with these challenges, we propose in this paper a bi-level evolutionary approach for the optimized induction of multivariate decision trees, where the upper-level role is to design the classifiers while the lower-level approximates the optimal labels’ ordering for each classifier. Our proposed method, named BIMLC-GA (Bi-level Imbalanced Multi-Label Classification Genetic Algorithm), is compared to several state-of-the-art methods across a variety of imbalanced multi-label data sets from several application fields and then applied on the miRNA-related diseases case study. The statistical analysis of the obtained results shows the merits of our proposal.

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This paper presents a new evolutionary method and tool called BMLDS (Bi-level Multi-Label Detection of Smells) that optimizes a population of classifier chains for the multi-label detection of smells. As the chain is sensitive to the labels' (i.e., smell types) order, the chains induction task is framed as a bi-level optimization problem, where the upper-level role is to search for the optimal order of each considered chain while the lower-level one is to generate the chains. This allows taking into consideration the interactions between smells in the multi-label detection process. The statistical analysis of the experimental results reveals the merits of our proposal with respect to several existing works.

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Code smells, also known as anti-patterns, are poor design or implementation choices that hinder program comprehensibility and maintainability. While several code smell detection methods have been proposed, Mantyla et al. identified the uncertainty issue as one of the major individual human factors that may affect developer’s decisions about the smelliness of software classes: they may indeed have different opinions mainly due to their different knowledge and expertise. Unfortunately, almost all the existing approaches assume data perfection and neglect the uncertainty when identifying the labels of the software classes. Ignoring or rejecting any uncertainty form could lead to a considerable loss of information, which could significantly deteriorate the effectiveness of the detection and identification processes. Inspired by our previous works and motivated by the interesting performance of the PDT (Possibilistic Decision Tree) in classifying uncertain data, we propose ADIPE (Anti-pattern Detection and Identification using Possibilistic decision tree Evolution), as a new tool that evolves and optimizes a set of detectors (PDTs) that could effectively deal with software class labels uncertainty using some concepts from the Possibility theory. ADIPE uses a PBE (Possibilistic Base of Examples: a dataset with possibilistic labels) that it is built using a set of opinion-based classifiers (i.e., a set of probabilistic classifiers) with the aim to simulate human developers’ uncertainty. A set of advisors and probabilistic classifiers are employed in order to mimic the subjectivity and the doubtfulness of software engineers. A detailed experimental study is conducted to show the merits and outperformance of ADIPE in dealing with uncertainty in code smells detection and identification with respect to four relevant state-of-the-art methods, including the baseline PDT. The experimental study was performed in uncertain and certain environments based on two suitable metrics: PF-measure_dist (Possibilistic F-measure_Distance) and IAC (Information Affinity Criterion); which corresponds to the F-measure and Accuracy (PCC) for the certain case. The obtained results for the uncertain environment reveal that for the detection process, the PF-measure_dist of ADIPE ranges within [0.9047 and 0.9285], and its IAC lies within [0.9288 and 0.9557]; while for the identification process, the PF-measure_dist of ADIPE is in [0.8545, 0.9228], and its IAC lies within [0.8751, 0.933]. ADIPE is able to find 35% more code smells with uncertain data than the second best algorithm (i.e., BLOP). In addition, ADIPE succeeds to decrease the number of false alarms (i.e., misclassified smelly instances) with a rate equals to 12%. Our proposed approach is also able to identify 43% more smell types than BLOP and decreases the number of false alarms with a rate equals to 32%. Similar results were obtained for the certain environment, which demonstrate the ability of ADIPE to also deal with the certain environment.

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Discretization-based feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), etc. However, these methods share the same shortcoming which consists in encoding the problem solution as a sequence of cut-points. From this cut-points vector, the decision of deleting or selecting any feature is induced. Indeed, the number of generated cut-points varies from one feature to another. Thus, the higher the number of cut-points, the higher the probability of selecting the considered feature; and vice versa. This fact leads to the deletion of possibly important features having a single or a low number of cut-points, such as the infection rate, the glycemia level, and the blood pressure. In order to solve the issue of the dependency relation between the feature selection (or removal) event and the number of its generated potential cut-points, we propose to model the DBFS task as a bilevel optimization problem and then solve it using an improved version of an existing co-evolutionary algorithm, named I-CEMBA. The latter ensures the variation of the number of features during the migration process in order to deal with the multimodality aspect. The resulting algorithm, termed bilevel discretization-based feature selection (Bi-DFS), performs selection at the upper level while discretization is done at the lower level. The experimental results on several high-dimensional datasets show that Bi-DFS outperforms relevant state-of-the-art methods in terms of classification accuracy, generalization ability, and feature selection bias.

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In recent years, Home Health Care (HHC) has gained popularity in different countries around the world (e.g. France, US, Germany, etc.). The HHC consists in providing medical services to patients at home. During the HHC service, caregivers’ planning may be disrupted by some unexpected events (e.g. urgent request, caregiver absence, traffic congestion, etc.), which makes HHC activities infeasible. This paper addresses the daily HHC routing and scheduling problem by considering unpredicted events. To solve this problem, we propose a Case-Based Reasoning (CBR) methodology. Our purpose is to create the HHC case base which contains the knowledge about the perturbation.

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In the last few years, there has been an increasing interest in Random Constraint Satisfaction Problems (CSP) from both experimental and theoretical points of view. To consider a variant instance of the problems, we used a random benchmark. In the present paper, some work has been done to find the shortest Hamiltonian circuit among specified nodes in each superimposed graph (SGs). The Hamiltonian circuit is a circuit that visits each node in the graph exactly once. The Hamiltonian path may be constructed and adjusted according to specific constraints such as time limits. A new constraint satisfaction optimization problem model for the circuit Hamiltonian circuit problem in a superimposed graph has been presented. To solve this issue, we propose amelioration for the genetic algorithm using Dijkstra’s algorithm, where we create the improved genetic algorithm (IGA). To evaluate this approach, we compare the CPU and fitness values of the IGA to the results provided by an adapted genetic algorithm to find the shortest Hamiltonian circuit in a superimposed graph.

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Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issue of class label uncertainty is usually omitted. Indeed, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Thus, this uncertainty should be taken into account and then processed by detection tools. Unfortunately, these latter usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. This practice could considerably degrade the detection/identification process effectiveness. Motivated by this observation and the interesting performance of the Possibilistic K-NN (PK-NN) classifier in dealing with uncertain data, we propose a new possibilistic evolutionary detection method, named ADIPOK (Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs), that is able to deal with label uncertainty using some concepts stemming from the Possibility theory. ADIPOK is validated using a possibilistic base of smell examples that simulates the subjectivity of software engineers’ opinions’ uncertainty. The statistical analysis of the obtained results on a set of comparative experiments with respect to four state-of-the-art methods show the merits of our proposed method.

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Like the case of any software, Web Services (WSs) developers could introduce anti-patterns due to the lack of experience and badly-planned changes. During the last decade, search-based approaches have shown their outperformance over other approaches mainly thanks to their global search ability. Unfortunately, these approaches do not consider the uncertainty of class labels. In fact, two experts could be uncertain about the smelliness of a particular WS interface but also about the smell type. Currently, existing works reject uncertain data that correspond to WSs interfaces with doubtful labels. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose a new evolutionary detection approach, named Web Services Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs (WS-ADIPOK), which can cope with the uncertainty based on the Possibility Theory. The obtained experimental results reveal the merits of our proposal regarding four relevant state-of-the-art approaches.

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Recently, decomposition has gained a wide interest in solving multi-objective optimization problems involving more than three objectives also known as Many-objective Optimization Problems (MaOPs). In the last few years, there have been many proposals to use decomposition to solve unconstrained problems. However, fewer is the amount of works that has been devoted to propose new decomposition-based algorithms to solve constrained many-objective problems. In this paper, we propose the ISC-Pareto dominance (Isolated Solution-based Constrained Pareto dominance) relation that has the ability to: (1) handle constrained many-objective problems characterized by different types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller CV (Constraint Violation) values. Our constraint handling strategy has been integrated into the framework of the Constrained Non-Dominated Sorting Genetic Algorithm-III (C-NSGA-III) to produce a new algorithm called Isolated Solution-based Constrained NSGA-III (ISC-NSGA-III). The empirical results have demonstrated that our constraint handling strategy is able to provide better and competitive results when compared against three recently proposed constrained decomposition-based many-objective evolutionary algorithms in addition to a penalty-based version of NSGA-III on the CDTLZ benchmark problems involving up to fifteen objectives. Moreover, the efficacy of ISC-NSGA-III on a real world water management problem is showcased.

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For the most schedulling problems studied in literature, job processing times are assumed to be known and constant over time. However, this assumption is not appropriate for many realistic situations where the employees and the machines execute the same task in a repetitive manner. They learn how to perform more efficiently. As a result, the processing time of a given job is shorter if it is scheduled later, rather than earlier in the sequence. In this paper, we consider the flexible job-shop problem (FJSP) with two kinds of constraint, namely, the sequence-dependent setup times (SDST) and the learning effects. Makespan is specified as the objective function to be minimised. To solve this problem, an adaptive genetic algorithm (AGA) is proposed. Our algorithm uses an adaptive strategy based on: 1) the current specificity of the search space; 2) the preceding results of already used operators; 3) their associated parameter settings. We adopt this strategy in order to maintain the balance between exploration and exploitation. Experimental studies are presented to assess and validate the benefit of the incorporation of the learning process to the SDST-FJSP over the original problem.

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Code smells, also known as anti-patterns, are indicators of bad design solutions. However, two different experts may have different opinions not only about the smelliness of a particular software class but also about the smell type. This causes an uncertainty problem that should be taken into account. Unfortunately, existing works reject uncertain data that correspond to software classes with doubtful labels. Uncertain data rejection could cause a significant loss of information that could considerably degrade the performance of the detection process. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose in this paper a new evolutionary detection method, named ADIPOK (Anti-pattern Detection and Identification using Possibilistic Optimized K-NN), that is able to cope with the uncertainty factor using the possibility theory. The comparative experimental results reveal the merits of our proposal with respect to four relevant state-of-the-art approaches.

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Decomposition-based evolutionary algorithms using predefined reference points have shown good performance in many-objective optimization. Unfortunately, almost all experimental studies have focused on problems having regular Pareto fronts (PFs). Recently, it has been shown that the performance of such algorithms is deteriorated when facing irregular PFs, such as degenerate, discontinuous, inverted, strongly convex, and/or strongly concave fronts. The main issue is that the predefined reference points may not all intersect with the PF. Therefore, many researchers have proposed to update the reference points with the aim of adapting them to the discovered Pareto shape. Unfortunately, the adaptive update does not really solve the issue for two main reasons. On the one hand, there is a considerable difficulty to set the time and the frequency of updates. On the other hand, it is not easy to define how to update the search directions for an unknown PF shape. This article proposes to approximate irregular PFs using a set of predefined normal-boundary intersection (NBI) directions. The main motivation behind this article is that when using a set of well-distributed NBI directions, all these directions intersect with the PF regardless of its shape, except for the case of discontinuous and/or degenerate fronts. To handle the latter cases, a simple interaction mechanism between the decision maker (DM) and the algorithm is used. In fact, the DM is asked if the number of NBI directions needs to be increased in some stages of the evolutionary process. If so, the resolution of the NBI directions that intersect the PF is increased to properly cover discontinuous and/or degenerate PFs. Our experimental results on benchmark problems with regular and irregular PFs, having up to fifteen objectives, show the merits of our algorithm when compared to eight of the most representative state-of-the-art algorithms.

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This paper presents the home health care routing and scheduling problem as the vehicle routing problem with time windows (VRPTW). we propose a dynamic approach for home care planning to ensure the continuity of care for patients. The proposed approach aims to optimize the care plan route of each caregiver according to their skills, availabilities and preferences. We aim also to minimize the violation of time windows in order to maximize patient and caregiver’s satisfaction. The optimal plan route is generated with a population-based algorithm which is the Artificial Immune Algorithm (AIS). A multi-agent approach is used to ensure communication and coordination between the different actors.

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Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.

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Multi-objective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in solving problems with two or three objectives. However, recent studies show that MOEAs face many difficulties when tackling problems involving a larger number of objectives as their behavior becomes similar to a random walk in the search space since most individuals are nondominated with respect to each other. Motivated by the interesting results of decomposition-based approaches and preference-based ones, we propose in this paper a new decomposition-based dominance relation to deal with many-objective optimization problems and a new diversity factor based on the penalty-based boundary intersection method. Our reference point-based dominance (RP-dominance), has the ability to create a strict partial order on the set of nondominated solutions using a set of well-distributed reference points. The RP-dominance is subsequently used to substitute the Pareto dominance in nondominated sorting genetic algorithm-II (NSGA-II). The augmented MOEA, labeled as RP-dominance-based NSGA-II, has been statistically demonstrated to provide competitive and oftentimes better results when compared against four recently proposed decomposition-based MOEAs on commonly-used benchmark problems involving up to 20 objectives. In addition, the efficacy of the algorithm on a realistic water management problem is showcased.

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During the few past years, decomposition has shown a high performance in solving Multi-objective Optimization Problems (MOPs) involving more than three objectives, called as Many-objective Optimization Problems (MaOPs). The performance of most of the existing decomposition-based algorithms has been assessed on the widely used DTLZ and WFG unconstrained test problems. However, the number of works that have been devoted to tackle the problematic of constrained many-objective optimization is relatively very small when compared to the number of works handling the unconstrained case. Recently there has been some interest to exploit infeasible isolated solutions when solving Constrained MaOPs (CMaOPs). Motivated by this observation, we firstly propose an IS-update procedure (Isolated Solution-based update procedure) that has the ability to: (1) handle CMaOPs characterized by various types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller Constraint Violation (CV) values. The IS-update procedure is subsequently embedded within the Multi-Objective Evolutionary Algorithm-based on Decomposition (MOEA/D). The new obtained algorithm, named ISC-MOEA/D (Isolated Solution-based Constrained MOEA/D), has been shown to provide competitive and better results when compared against three recent works on the CDTLZ benchmark problems.

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During the few past years, decomposition has shown a high performance in solving Multi-objective Optimization Problems (MOPs) involving more than three objectives, called as Many-objective Optimization Problems (MaOPs). The performance of most of the existing decomposition-based algorithms has been assessed on the widely used DTLZ and WFG unconstrained test problems. However, the number of works that have been devoted to tackle the problematic of constrained many-objective optimization is relatively very small when compared to the number of works handling the unconstrained case. Recently there has been some interest to exploit infeasible isolated solutions when solving Constrained MaOPs (CMaOPs). Motivated by this observation, we firstly propose an IS-update procedure (Isolated Solution-based update procedure) that has the ability to: (1) handle CMaOPs characterized by various types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller Constraint Violation (CV) values. The IS-update procedure is subsequently embedded within the Multi-Objective Evolutionary Algorithm-based on Decomposition (MOEA/D). The new obtained algorithm, named ISC-MOEA/D (Isolated Solution-based Constrained MOEA/D), has been shown to provide competitive and better results when compared against three recent works on the CDTLZ benchmark problems.

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Multi-objective evolutionary algorithms are efficient in solving problems with two or three objectives. However, recent studies have shown that they face many difficulties when tackling problems involving a larger number of objectives and their behaviors become similar to a random walk in the search space since most individuals become non-dominated with each others. Motivated by the interesting results of decomposition-based approaches and preference-based ones, we propose in this paper a new decomposition-based dominance relation called TSD-dominance (Targeted Search Directions based dominance) to deal with many-objective optimization problems. Our dominance relation has the ability to create a strict partial order on the set of Pareto-equivalent solutions using a set of well-distributed reference points, thereby producing a finer grained ranking of solutions. The TSD-dominance is subsequently used to substitute the Pareto dominance in NSGA-II. The new obtained MOEA, called TSD-NSGA-II has been statistically demonstrated to provide competitive and better results when compared with three recently proposed decomposition-based algorithms on commonly used benchmark problems involving up to twenty objectives.

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Constrained optimization is applicable to most real world engineering science problems. An efficient constraint handling method must be robust, reliable and computationally efficient. However, the performance of constraint handling mechanism deteriorates with the increase of multi-modality, non-linearity and non-convexity of the constraint functions. Most of the classical mathematics based optimization techniques fails to tackle these issues. Hence, researchers round the globe are putting hard effort to deal with multi-modality, non-linearity and non-convexity, as their presence in the real world problems are unavoidable. Initially, Evolutionary Algorithms (EAs) were developed for unconstrained optimization but engineering problems are always with certain type of constraints. The in-dependability of EAs to the structure of problem has led the researchers to re-think in applying the same to the problems incorporating the constraints. The constraint handling techniques have been successfully used to solve many single objective problems but there has been limited work in applying them to the multi-objective optimization problem. Since for most engineering science problems conflicting multi-objectives have to be satisfied simultaneously, multi-objective constraint handling should be one of the most active research area in engineering optimization. Hence, in this chapter authors have concentrated in explaining the constrained multi-objective optimization problem along with their applications.

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Multi-objective Evolutionary Algorithms (MOEAs) have proven their effectiveness and efficiency in solving complex problems with two or three objectives. However, recent studies have shown that the performance of the classical MOEAs is deteriorated when tackling problems involving a larger number of conflicting objectives. Since most individuals become non-dominated with respect to each others, the MOEAs’ behavior becomes similar to a random walk in the search space. Motivated by the fact that a wide range of real world applications involves the optimization of more than three objectives, several Many-objective Evolutionary Algorithms (MaOEAs) have been proposed in the literature. In this chapter, we highlight in the introduction the difficulties encountered by MOEAs when handling Many-objective Optimization Problems (MaOPs). Moreover, a classification of the most prominent MaOEAs is provided in an attempt to review and describe the evolution of the field. In addition, a summary of the most commonly used test problems, statistical tests, and performance indicators is presented. Finally, we outline some possible future research directions in this research area.

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Problems involving multiple conflicting objectives arise in most real world optimization problems. Evolutionary Algorithms (EAs) have gained a wide interest and success in solving problems of this nature for two main reasons: (1) EAs allow finding several members of the Pareto optimal set in a single run of the algorithm and (2) EAs are less susceptible to the shape of the Pareto front. Thus, Multi-objective EAs (MOEAs) have often been used to solve Multi-objective Problems (MOPs). This chapter aims to summarize the efforts of various researchers algorithmic processes for MOEAs in an attempt to provide a review of the use and the evolution of the field. Hence, some basic concepts and a summary of the main MOEAs are provided. We also propose a classification of the existing MOEAs in order to encourage researchers to continue shaping the field. Furthermore, we suggest a classification of the most popular performance indicators that have been used to evaluate the performance of MOEAs.

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Because of the rapid growth of air traffic, optimizing airport management is becoming necessary in order to improveairport's capacity and better align its resources to the received traffic. In this paper we study the assignment of the arriving aircrafts to the available gates using the fixed daily schedule. We introduce a new approach based on Genetic Algorithms (GA) to solve the gate assignment problem (GAP). The encoding strategy consists in representing the chromosome by a vector of integers. The index of each gene represents the flight number and its value represents the gate to which the flight will be assigned. The method used to generate the initial population is based on three different heuristics and a random sorting of the gates. The selection method is the “In fitness proportionate selection” known as “roulette wheel selection”. In addition to one point and two point Crossover operators, we designed a Greedy procedure Crossover (GPX) operator. The experimentation is based on the use of fictive scenarios generated in accordance with the physical characteristics of the Tunis Carthage Airport and using different flight schedules. The comparison between deterministic approach, simple heuristics and the GA has shown the efficiency of the last approach in terms of solution's quality when we aim at solving the problems of large size. In order to determine the best configuration of the GA, we compared the different crossover operators and we noticed that the use of GPX improves the speed of convergence of the algorithm towards better solutions.

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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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In situations where some sellers have surplus stock while others, belonging to the same firm, are stocked out, it may be desirable to share the unsold units to fulfill more unmet demands and avoid holding costs. Such practice is named Transshipment. It ensures cost reduction and service level improvement. In this paper, we present a multiobjective study of a multi-location transshipment inventory which optimizes three objectives: (1) the aggregate cost, (2) the fill rate, and (3) the shared inventory quantity (SIQ), in the presence of different storage capacity constraints. Simulation is needed to evaluate the expected value of the problem stochastic objective functions. Two reference evolutionary multiobjective algorithms (SPEA2 and NSGA-II) are used to solve instances of the problem. Based on the obtained Pareto fronts, it is shown that both low aggregate cost and high fill rate levels could be ensured, while the shared inventory quantity is considerably increased.