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Recent Advances in Evolutionary Multi-objective Optimization pp 159–179Cite as

Practical Applications in Constrained Evolutionary Multi-objective Optimization

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Part of the book series: Adaptation, Learning, and Optimization ((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.

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Authors and Affiliations

  1. Smart Materials Structure and Systems (SMSS) Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Arun Kumar Sharma & Bishakh Bhattacharya

  2. Graduate School of Knowledge Service Engineering, Department of Industrial and Systems Engineering, Korean Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea

    Rituparna Datta

  3. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Rituparna Datta

  4. SOIE Lab, Computer Science Department, ISG-Tunis, University of Tunis, Bouchoucha City, 2000, Le Bardo, Tunis, Tunisia

    Maha Elarbi & Slim Bechikh

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  1. Arun Kumar Sharma
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  2. Rituparna Datta
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  3. Maha Elarbi
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  5. Slim Bechikh
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Correspondence to Arun Kumar Sharma .

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Editors and Affiliations

  1. SOIE lab, Computer Science Department, University of Tunis, ISG-Tunis , Bouchoucha, Le Bardo, Tunisia

    Slim Bechikh

  2. Department of Mechanical Engineering, Institute of Technology, Kalyanpur, Kanpur, Uttar Pradesh, India

    Rituparna Datta

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Abhishek Gupta

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Sharma, A.K., Datta, R., Elarbi, M., Bhattacharya, B., Bechikh, S. (2017). Practical Applications in Constrained Evolutionary Multi-objective Optimization. In: Bechikh, S., Datta, R., Gupta, A. (eds) Recent Advances in Evolutionary Multi-objective Optimization. Adaptation, Learning, and Optimization, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-42978-6_6

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