Multi-agent based distributed computing framework for master-slave particle swarms

被引：2
作者：
机构：
[1] College of Computer Science and Technology, Zhejiang University of Technology
[2] Department of Mechanical Engineering, PLA University of Science and Technology
来源：
Zheng, Y.-J. (yujun.zheng@computer.org) | 1600年 / Chinese Academy of Sciences卷 / 23期
关键词：
Agent; Cooperative evolution; Distributed computing; Master-slave model; Particle swarm optimization (PSO);
D O I：
10.3724/SP.J.1001.2012.04305
中图分类号：
学科分类号：
摘要：
To effectively solve large-scale optimization problems, the paper proposes a distributed agent computing framework based on the parallel particle swarm optimization (PSO). The framework uses a master swarm for evolving complete solutions of the problem, and uses a set of slave swarms for evolving sub-solutions of the subproblems concurrently. The master swarm and slave swarms alternatively implement the PSO procedure to improve the problem-solving efficiency. Using the asynchronous team based agent architecture, a master/slave swarm consists of different kinds of agents, which share a population of solutions and cooperate to evolve the population, such as initializing solutions, moving particles, handling constraints, and decomposing/synthesizing sub-solutions. The framework can be used to solve complicated constained and multiobjective optimization problems efficiently. Experimental results demonstrate that this approach has significant performance advantage over two other state-of-the-art algorithms on a typical transportation problem. © 2012 ISCAS.
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页码：3000 / 3008
页数：8
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