2016

Chapitre de livre

In Recent advances in evolutionary multi-objective optimization (pp. 159-179). Cham: Springer International Publishing

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.

@incollection{sharma2016practical,
  title={Practical applications in constrained evolutionary multi-objective optimization},
  author={Sharma, Arun Kumar and Datta, Rituparna and Elarbi, Maha and Bhattacharya, Bishakh and Bechikh, Slim},
  booktitle={Recent advances in evolutionary multi-objective optimization},
  pages={159--179},
  year={2016},
  publisher={Springer}
}