An Improved Multi-state Particle Swarm Optimization for Discrete Optimization Problems

Particle swarm optimization (PSO) has been successfully applied to solve various optimization problems. Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a...

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Bibliographic Details
Published in:	2015 7th International Conference on Computational Intelligence, Communication Systems and Networks pp. 3 - 8
Main Authors:	Ibrahim, Ismail, Ibrahim, Zuwairie, Ahmad, Hamzah, Md Yusof, Zulkifli
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2015
Subjects:	Benchmark testing Birds Cities and towns Convergence discrete combinatorial optimization problem multi-state Optimization Particle swarm optimization
Online Access:	Get full text
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Summary:	Particle swarm optimization (PSO) has been successfully applied to solve various optimization problems. Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a simplified mechanism of transition between two states. However, the MSPSO algorithm has to deal with the production of infeasible solutions and hence, additional step to convert the infeasible solution to feasible solution is required. In this paper, the MSPSO is improved by introducing a strategy that directly produces feasible solutions. The performance of the improved multi-state particle swarm optimization (IMSPSO) is empirically evaluated based on a set of travelling salesman problems (TSPs). The experimental results showed the newly introduced approach is promising and consistently outperformed the binary PSO algorithm.
DOI:	10.1109/CICSyN.2015.11