Recent Advances in Evolutionary Multi-objective Optimization

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This book covers the most recent advances in the field of evolutionary multiobjectiveoptimization. With the aim of drawing the attention of up-andcomingscientists towards exciting prospects at the forefront of computationalintelligence, the authors have made an effort to ensure that the ideas conveyedherein are accessible to the widest audience. The book begins with a summaryof the basic concepts in multi-objective optimization. This is followed by briefdiscussions on various algorithms that have been proposed over the years forsolving such problems, ranging from classical (mathematical) approaches tosophisticated evolutionary ones that are capable of seamlessly tackling practicalchallenges such as non-convexity, multi-modality, the presence of multipleconstraints, etc. Thereafter, some of the key emerging aspects that are likelyto shape future research directions in the field are presented. These include: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.
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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Multi-Objective Optimization Evolutionary Computation Decision Making Dynamic Optimization Constrained Optimization Bi-Level Programming Multi-Tasking Applications

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