Intelligent particle swarm optimization in multi-objective problems

Shinn Jang Ho*, Wen Yuan Ku, Jun Wun Jou, Ming Hao Hung, Shinn-Ying Ho

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


In this paper, we proposes a novel intelligent multi-objective particle swarm optimization (IMOPSO) to solve multi-objective optimization problems. High performance of IMOPSO mainly arises from two parts: one is using generalized Pareto-based scale-independent fitness function (GPSISF) can efficiently given all candidate solutions a score, and then decided candidate solutions level. The other one is replacing the conventional particle move process of PSO with an intelligent move mechanism (IMM) based on orthogonal experimental design to enhance the search ability. IMM can evenly sample and analyze from the best experience of an individual particle and group particles by using a systematic reasoning method, and then efficiently generate a good candidate solution for the next move of the particle. Some benchmark functions are used to evaluate the performance of IMOPSO, and compared with some existing multi-objective evolution algorithms. According to experimental results and analysis, they show that IMOPSO performs well.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Number of pages11
StatePublished - 14 Jul 2006
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: 9 Apr 200612 Apr 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006

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