Multi-objective optimization in computational intelligence theory and practice
Title:
Multi-objective optimization in computational intelligence theory and practice
Author:
Alam, Sameer.
ISBN:
9781599045009
Publication Information:
Hershey, Pa. : IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA), c2008.
Physical Description:
electronic texts (xix, 475 p. : ill.) : digital files.
Contents:
An Introduction to Multi-Objective Optimization / Lam Bui, Sameer Alam -- Multi-Objective Particles Swarm Optimization Approaches / Konstantinos Parsopoulos, Michael Vrahatis -- Generalized Differential Evolution for Constrained Multi-Objective Optimization / Saku Kukkonen, Jouni Lampinen -- Towards a More Efficient Multi-Objective Particle Swarm Optimizer / Luis Santana-Quintero, Noel Ramírez-Santiago, Carlos Coello Coello -- Multi-Objective Optimization Using Artificial Immune Systems / Licheng Jiao ... [et al.] -- Lexicographic Goal Programming and Assessment Tools for a Combinatorial Production Problem / Seamus McGovern, Surendra Gupta -- Evolutionary Population Dynamics and Multi-Objective Optimisation Problems / Andrew Lewis, Sanaz Mostaghim, Marcus Randall -- Multi-Objective Evolutionary Algorithms for Sensor Network Design / Ramesh Rajagopalan ... [et al.] -- Evolutionary Multi-Objective Optimization for DNA Sequence Design / Soo-Yong Shin, In-Hee Lee, Byoung-Tak Zhang -- Computational Intelligence to Speed-Up Multi-Objective Design Space Exploration of Embedded Systems / Giuseppe Ascia ... [et al.] -- Walking with EMO: Multi-Objective Robotics for Evolving Two, Four, and Six-Legged Locomotion / Jason Teo ... [et al.] -- Evolutionary Multi-Objective Optimization in Energy Conversion Systems / Andrea Toffolo -- Evolutionary Multi-Objective Optimization for Assignment Problems / Mark Kleeman, Gary Lamont -- Evolutionary Multi-Objective Optimization in Military Applications / Mark Kleeman, Gary Lamont.
Abstract:
Multi-objective optimization (MO) is a fast-developing field in computational intelligence research. Giving decision makers more options to choose from using some post-analysis preference information, there are a number of competitive MO techniques with an increasingly large number of MO real-world applications. This book explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.
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Electronic Access:
Chapter PDFs via platform: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-59904-498-9