A Novel Multi-objective Particle Swarm Optimization Algorithm for Flow Shop Scheduling Problems

被引：0

作者：

Wang, Wanliang ^{[1
]}

Chen, Lili ^{[2
]}

Jie, Jing ^{[1
]}

Zhao, Yanwei ^{[3
]}

Zhang, Jing ^{[2
]}

机构：

[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China

[2] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310014, Zhejiang, Peoples R China

[3] Zhejiang Univ Technol, Coll Mech Engn, Hangzhou 310014, Zhejiang, Peoples R China

来源：

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2012年 / 6839卷

基金：

美国国家科学基金会; 中国博士后科学基金;

关键词：

multi-objective optimization; particle swarm optimization; flow shop scheduling; Baldwinian learning mechanism;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a novel hybrid multi-objective particle swarm algorithm Mopsocd_BL is proposed to solve the flow shop scheduling problem with two objectives of minimizing makespan and the total idle time of machines. This algorithm bases on Baldwinian learning mechanism to improve local search ability of particle swarm optimization, and uses the Pareto dominance and crowding distance to update the solutions. Experimental results show that this algorithm can maintain the diversity of solutions and find more uniformly distributed Pareto optimal solutions.

引用

页码：24 / +

页数：2

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