Overview
- Editors:
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Slim Bechikh
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SOIE lab, Computer Science Department, University of Tunis, ISG-Tunis , Bouchoucha, Le Bardo, Tunisia
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Rituparna Datta
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Department of Mechanical Engineering, Institute of Technology, Kalyanpur, Kanpur, India
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Abhishek Gupta
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School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
- Provides both methodological treatments and real world insights
- Serves as comprehensive reference for researchers, practitioners, and advanced-level students
- Covers both the theory and practice of using evolutionary algorithms in tackling real world applications involving multiple objectives
- Includes supplementary material: sn.pub/extras
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Table of contents (6 chapters)
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- Maha Elarbi, Slim Bechikh, Lamjed Ben Said, Rituparna Datta
Pages 1-30
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- Radhia Azzouz, Slim Bechikh, Lamjed Ben Said
Pages 31-70
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- Ankur Sinha, Pekka Malo, Kalyanmoy Deb
Pages 71-103
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- Slim Bechikh, Maha Elarbi, Lamjed Ben Said
Pages 105-137
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- Abhishek Gupta, Bingshui Da, Yuan Yuan, Yew-Soon Ong
Pages 139-157
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- Arun Kumar Sharma, Rituparna Datta, Maha Elarbi, Bishakh Bhattacharya, Slim Bechikh
Pages 159-179
About this book
This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
Editors and Affiliations
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SOIE lab, Computer Science Department, University of Tunis, ISG-Tunis , Bouchoucha, Le Bardo, Tunisia
Slim Bechikh
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Department of Mechanical Engineering, Institute of Technology, Kalyanpur, Kanpur, India
Rituparna Datta
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School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
Abhishek Gupta
