Towards a More Efficient Multi-Objective Particle Swarm Optimizer

Towards a More Efficient Multi-Objective Particle Swarm Optimizer

Luis V. Santana-Quintero (CINVESTAV-IPN, Evolutionary Computation Group (EVOCINV), Mexico), Noel Ramírez-Santiago (CINVESTAV-IPN, Evolutionary Computation Group (EVOCINV), Mexico) and Carlos A. Coello Coello (CINVESTAV-IPN, Evolutionary Computation Group (EVOCINV), Mexico)
DOI: 10.4018/978-1-59904-498-9.ch004
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This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this approach is to combine the high convergence rate of the PSO algorithm with a local search approach based on scatter search, in order to have the main advantages of these two types of techniques. We propose a new leader selection scheme for PSO, which aims to accelerate convergence by increasing the selection pressure. However, this higher selection pressure reduces diversity. To alleviate that, scatter search is adopted after applying PSO, in order to spread the solutions previously obtained, so that a better distribution along the Pareto front is achieved. The proposed approach can produce reasonably good approximations of multi-objective problems of high dimensionality, performing only 4,000 fitness function evaluations. Test problems taken from the specialized literature are adopted to validate the proposed hybrid approach. Results are compared with respect to the NSGA-II, which is an approach representative of the state-of-the-art in the area.

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Table of Contents
Zbigniew Michalewicz
Lam Thu Bui, Sameer Alam
Chapter 1
Lam Thu Bui, Sameer Alam
This chapter is devoted to summarize common concepts related to multi-objective optimization (MO). An overview of “traditional” as well as CI-based... Sample PDF
An Introduction to Multi-Objective Optimization
Chapter 2
Konstantinos E. Parsopoulos, Michael N. Vrahatis
The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems... Sample PDF
Multi-Objective Particles Swarm Optimization Approaches
Chapter 3
Saku Kukkonen, Lampinen Jouni
Multi-objective optimization with Evolutionary Algorithms has been gaining popularity recently because its applicability in practical problems. Many... Sample PDF
Generalized Differential Evolution for Constrained Multi-Objective Optimization
Chapter 4
Luis V. Santana-Quintero, Noel Ramírez-Santiago, Carlos A. Coello Coello
This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this... Sample PDF
Towards a More Efficient Multi-Objective Particle Swarm Optimizer
Chapter 5
Licheng Jiao, Maoguo Gong, Wenping Ma, Ronghua Shang
The human immune system (HIS) is a highly evolved, parallel and distributed adaptive system. The information processing abilities of HIS provide... Sample PDF
Multi-Objective Optimization Using Artificial Immune Systems
Chapter 6
Seamus M. McGovern, Surendra M. Gupta
NP-complete combinatorial problems often necessitate the use of near-optimal solution techniques including heuristics and metaheuristics. The... Sample PDF
Lexicographic Goal Programming and Assessment Tools for a Combinatorial Production Problem
Chapter 7
Andrew Lewis, Sanaz Mostaghim, Marcus Randall
Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective... Sample PDF
Evolutionary Population Dynamics and Multi-Objective Optimisation Problems
Chapter 8
Ramesh Rajagopalan, Chilukuri K. Mohan, Kishan G. Mehrotra, Pramod K. Varshney
Many sensor network design problems are characterized by the need to optimize multiple conflicting objectives. However, existing approaches... Sample PDF
Multi-Objective Evolutionary Algorithms for Sensor Network Design
Chapter 9
Soo-Yong Shin, In-Hee Lee, Byoung-Tak Zhang
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Evolutionary Multi-Objective Optimization for DNA Sequence Design
Chapter 10
Giuseppe Ascia, Vincenzo Catania, Alessandro G. Di Nuovo, Maurizio Palesi, Davide Patti
Multi-Objective Evolutionary Algorithms (MOEAs) have received increasing interest in industry, because they have proved to be powerful optimizers.... Sample PDF
Computational Intelligence to Speed-Up Multi-Objective Design Space Exploration of Embedded Systems
Chapter 11
Jason Teo, Lynnie D. Neri, Minh H. Nguyen, Hussein A. Abbass
This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO)... Sample PDF
Walking with EMO: Multi-Objective Robotics for Evolving Two, Four, and Six-Legged Locomotion
Chapter 12
Andrea Toffolo
The research field on energy conversion systems presents a large variety of multi-objective optimization problems that can be solved taking full... Sample PDF
Evolutionary Multi-Objective Optimization in Energy Conversion Systems: From Component Detail to System Configuration
Chapter 13
Mark P. Kleeman, Gary B. Lamont
Assignment problems are used throughout many research disciplines. Most assignment problems in the literature have focused on solving a single... Sample PDF
Evolutionary Multi-Objective Optimization for Assignment Problems
Chapter 14
Mark P. Kleeman, Gary B. Lamont
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Evolutionary Multi-Objective Optimization in Military Applications
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