Practical Applications in Constrained Evolutionary Multi-objective Optimization

  • Arun Kumar Sharma
  • Rituparna Datta
  • Maha Elarbi
  • Bishakh Bhattacharya
  • Slim Bechikh
Chapter
First Online:
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 20)

Abstract

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.

Keywords

Multi-objective optimization Constraint handling Evolutionary algorithms Practical applications 
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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Arun Kumar Sharma
    • 1
  • Rituparna Datta
    • 2
    • 3
  • Maha Elarbi
    • 4
  • Bishakh Bhattacharya
    • 1
  • Slim Bechikh
    • 4
  1. 1.Smart Materials Structure and Systems (SMSS) Laboratory, Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia
  2. 2.Graduate School of Knowledge Service Engineering, Department of Industrial and Systems EngineeringKorean Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea
  3. 3.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia
  4. 4.SOIE Lab, Computer Science Department, ISG-TunisUniversity of TunisLe Bardo, TunisTunisia

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