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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Main Authors:	Ismail, Ibrahim, Zuwairie, Ibrahim, Hamzah, Ahmad, Zulkifli, Md. Yusof
Format:	Conference or Workshop Item
Language:	English
Published:	2015
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/9349/ http://umpir.ump.edu.my/id/eprint/9349/ http://umpir.ump.edu.my/id/eprint/9349/1/An%20Improved%20Multi-State%20Particle%20Swarm%20Optimization%20for%20Discrete%20Optimization%20Problems.pdf

id	ump-9349
recordtype	eprints
spelling	ump-93492018-02-08T00:58:18Z http://umpir.ump.edu.my/id/eprint/9349/ An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems Ismail, Ibrahim Zuwairie, Ibrahim Hamzah, Ahmad Zulkifli, Md. Yusof TK Electrical engineering. Electronics Nuclear engineering 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. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/9349/1/An%20Improved%20Multi-State%20Particle%20Swarm%20Optimization%20for%20Discrete%20Optimization%20Problems.pdf Ismail, Ibrahim and Zuwairie, Ibrahim and Hamzah, Ahmad and Zulkifli, Md. Yusof (2015) An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems. In: 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2015), 3–5 June 2015 , Riga, Latvia. pp. 3-8.. http://dx.doi.org/10.1109/CICSyN.2015.11
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Ismail, Ibrahim Zuwairie, Ibrahim Hamzah, Ahmad Zulkifli, Md. Yusof An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems
description	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.
format	Conference or Workshop Item
author	Ismail, Ibrahim Zuwairie, Ibrahim Hamzah, Ahmad Zulkifli, Md. Yusof
author_facet	Ismail, Ibrahim Zuwairie, Ibrahim Hamzah, Ahmad Zulkifli, Md. Yusof
author_sort	Ismail, Ibrahim
title	An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems
title_short	An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems
title_full	An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems
title_fullStr	An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems
title_full_unstemmed	An Improved Multi-State Particle Swarm Optimization for Discrete Optimization Problems
title_sort	improved multi-state particle swarm optimization for discrete optimization problems
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/9349/ http://umpir.ump.edu.my/id/eprint/9349/ http://umpir.ump.edu.my/id/eprint/9349/1/An%20Improved%20Multi-State%20Particle%20Swarm%20Optimization%20for%20Discrete%20Optimization%20Problems.pdf
first_indexed	2023-09-18T22:07:50Z
last_indexed	2023-09-18T22:07:50Z
_version_	1777414827488051200

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

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