A novel multi-state particle swarm optimization for discrete combinatorial optimization problems

Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term...

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Main Authors: Ibrahim, Ismail, Md. Yusof, Zulkifli, Nawawi, Sophan Wahyudi, Abdul Rahim, Muhammad Arif, Khalil, Kamal, Ahmad, Hamzah, Ibrahim, Zuwairie
Format: Article
Published: 2012
Subjects:
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author Ibrahim, Ismail
Md. Yusof, Zulkifli
Nawawi, Sophan Wahyudi
Abdul Rahim, Muhammad Arif
Khalil, Kamal
Ahmad, Hamzah
Ibrahim, Zuwairie
author_facet Ibrahim, Ismail
Md. Yusof, Zulkifli
Nawawi, Sophan Wahyudi
Abdul Rahim, Muhammad Arif
Khalil, Kamal
Ahmad, Hamzah
Ibrahim, Zuwairie
author_sort Ibrahim, Ismail
collection ePrints
description Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term of convergence speed, stagnation in local optimum, and complexity. In this paper, a novel multi-state particle swarm optimization (MSPSO) is proposed to solve discrete optimization problems. Instead of evolving a high dimensional bit vector as in BinPSO, the proposed MSPSO mechanism evolves states of variables involved. The MSPSO algorithm has been applied to two benchmark instances of traveling salesman problem (TSP). The experimental results show that the the proposed MSPSO algorithm consistently outperforms the BinPSO in solving the discrete combinatorial optimization problem.
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spelling utm.eprints-465082017-09-12T04:50:07Z http://eprints.utm.my/46508/ A novel multi-state particle swarm optimization for discrete combinatorial optimization problems Ibrahim, Ismail Md. Yusof, Zulkifli Nawawi, Sophan Wahyudi Abdul Rahim, Muhammad Arif Khalil, Kamal Ahmad, Hamzah Ibrahim, Zuwairie Q Science Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term of convergence speed, stagnation in local optimum, and complexity. In this paper, a novel multi-state particle swarm optimization (MSPSO) is proposed to solve discrete optimization problems. Instead of evolving a high dimensional bit vector as in BinPSO, the proposed MSPSO mechanism evolves states of variables involved. The MSPSO algorithm has been applied to two benchmark instances of traveling salesman problem (TSP). The experimental results show that the the proposed MSPSO algorithm consistently outperforms the BinPSO in solving the discrete combinatorial optimization problem. 2012 Article PeerReviewed Ibrahim, Ismail and Md. Yusof, Zulkifli and Nawawi, Sophan Wahyudi and Abdul Rahim, Muhammad Arif and Khalil, Kamal and Ahmad, Hamzah and Ibrahim, Zuwairie (2012) A novel multi-state particle swarm optimization for discrete combinatorial optimization problems. Proceedings of International Conference on Computational Intelligence, Modelling and Simulation . pp. 18-23. ISSN 2166-8523 http://dx.doi.org/10.1109/CIMSim.2012.46
spellingShingle Q Science
Ibrahim, Ismail
Md. Yusof, Zulkifli
Nawawi, Sophan Wahyudi
Abdul Rahim, Muhammad Arif
Khalil, Kamal
Ahmad, Hamzah
Ibrahim, Zuwairie
A novel multi-state particle swarm optimization for discrete combinatorial optimization problems
title A novel multi-state particle swarm optimization for discrete combinatorial optimization problems
title_full A novel multi-state particle swarm optimization for discrete combinatorial optimization problems
title_fullStr A novel multi-state particle swarm optimization for discrete combinatorial optimization problems
title_full_unstemmed A novel multi-state particle swarm optimization for discrete combinatorial optimization problems
title_short A novel multi-state particle swarm optimization for discrete combinatorial optimization problems
title_sort novel multi state particle swarm optimization for discrete combinatorial optimization problems
topic Q Science
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