TY - GEN
T1 - A new algorithm based on PSO for Multi-Objective Optimization
AU - Leung, Man Fai
AU - Ng, Sin Chun
AU - Cheung, Chi Chung
AU - Lui, Andrew K.
PY - 2015/5/25
Y1 - 2015/5/25
N2 - This paper presents a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm that has two new components: leader selection and crossover. The new leader selection algorithm, called Space Expanding Strategy (SES), guides particles moving to the boundaries of the objective space in each generation so that the objective space can be expanded rapidly. Besides, crossover is adopted instead of mutation to enhance the convergence and maintain the stability of the generated solutions (exploitation). The performance of the proposed MOPSO algorithm was compared with three popular multi-objective algorithms in solving fifteen standard test functions. Their performance measures were hypervolume, spread and inverse generational distance. The performance investigation found that the performance of the proposed algorithm was generally better than the other three, and the performance of the proposed crossover was generally better than three popular mutation operators.
AB - This paper presents a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm that has two new components: leader selection and crossover. The new leader selection algorithm, called Space Expanding Strategy (SES), guides particles moving to the boundaries of the objective space in each generation so that the objective space can be expanded rapidly. Besides, crossover is adopted instead of mutation to enhance the convergence and maintain the stability of the generated solutions (exploitation). The performance of the proposed MOPSO algorithm was compared with three popular multi-objective algorithms in solving fifteen standard test functions. Their performance measures were hypervolume, spread and inverse generational distance. The performance investigation found that the performance of the proposed algorithm was generally better than the other three, and the performance of the proposed crossover was generally better than three popular mutation operators.
UR - http://www.scopus.com/inward/record.url?scp=84963575897&partnerID=8YFLogxK
U2 - 10.1109/CEC.2015.7257283
DO - 10.1109/CEC.2015.7257283
M3 - Conference article published in proceeding or book
AN - SCOPUS:84963575897
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 3156
EP - 3162
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
Y2 - 25 May 2015 through 28 May 2015
ER -