Recent Advances in Evolutionary Multi-objective Optimization

1. Front Matter

   Pages i-xii

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2. Multi-objective Optimization: Classical and Evolutionary Approaches

   • Maha Elarbi, Slim Bechikh, Lamjed Ben Said, Rituparna Datta

   Pages 1-30

3. Dynamic Multi-objective Optimization Using Evolutionary Algorithms: A Survey

   • Radhia Azzouz, Slim Bechikh, Lamjed Ben Said

   Pages 31-70

4. Evolutionary Bilevel Optimization: An Introduction and Recent Advances

   • Ankur Sinha, Pekka Malo, Kalyanmoy Deb

   Pages 71-103

5. Many-objective Optimization Using Evolutionary Algorithms: A Survey

   • Slim Bechikh, Maha Elarbi, Lamjed Ben Said

   Pages 105-137

6. On the Emerging Notion of Evolutionary Multitasking: A Computational Analog of Cognitive Multitasking

   • Abhishek Gupta, Bingshui Da, Yuan Yuan, Yew-Soon Ong

   Pages 139-157

7. Practical Applications in Constrained Evolutionary Multi-objective Optimization

   • Arun Kumar Sharma, Rituparna Datta, Maha Elarbi, Bishakh Bhattacharya, Slim Bechikh

   Pages 159-179

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.

• Computational Intelligence
• Multi-Objective Optimization
• Evolutionary Computation
• Decision Making
• Dynamic Optimization
• Constrained Optimization
• Bi-Level
• Programming
• Multi-Tasking
• Applications

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

  Slim Bechikh

• Department of Mechanical Engineering, Institute of Technology, Kalyanpur, Kanpur, India

  Rituparna Datta

• School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

  Abhishek Gupta

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