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

Ismail, Ibrahim and Zulkifli, Md. Yusof and Sophan Wahyudi, Nawawi and Muhammad Arif, Abdul Rahim and Kamal, Khalil and Hamzah, Ahmad and Zuwairie, Ibrahim (2012) A novel multi-state particle swarm optimization for discrete combinatorial optimization problems. In: IEEE 4th International Conference on Computational Intelligence, Modelling and Simulation (CIMSim 2012), 25-27 September 2012 , Kuantan, Pahang Darul Makmur. pp. 18-23.. ISBN 978-1-4673-3113-5

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Abstract

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.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: particle swarm optimization; binary particle swarm optimization; state; decision conflict
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 20 Mar 2020 02:27
Last Modified: 20 Mar 2020 02:27
URI: http://umpir.ump.edu.my/id/eprint/26946
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