Empirical study of bound constraint-handling methods in Particle Swarm Optimization for constrained search spaces

In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

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Constraint Handling in Particle Swarm Optimization

International Journal of Swarm Intelligence Research ◽

10.4018/jsir.2010010103 ◽

2010 ◽

Vol 1 (1) ◽

pp. 42-63 ◽

Cited By ~ 3

Author(s):

Wen Fung Leong ◽

Gary G. Yen

Keyword(s):

Particle Swarm Optimization ◽

Pareto Front ◽

Particle Swarm ◽

Constraint Handling ◽

Hidden Information ◽

Swarm Optimization ◽

Nondominated Solutions ◽

Local Searches ◽

Multiobjective Approach ◽

Fine Tune

In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

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Comparison of Various Approaches in Multi-objective Particle Swarm Optimization (MOPSO): Empirical Study

Multi-objective Swarm Intelligence - Studies in Computational Intelligence ◽

10.1007/978-3-662-46309-3_3 ◽

2015 ◽

pp. 75-103

Author(s):

Swagatika Devi ◽

Alok Kumar Jagadev ◽

Sachidananda Dehuri

Keyword(s):

Particle Swarm Optimization ◽

Empirical Study ◽

Particle Swarm ◽

Swarm Optimization ◽

Multi Objective

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A Condensed Hybrid Optimization Algorithm Using Enhanced Continuous Tabu Search and Particle Swarm Optimization

ASME 2009 Dynamic Systems and Control Conference, Volume 1 ◽

10.1115/dscc2009-2526 ◽

2009 ◽

Cited By ~ 3

Author(s):

Cheng-Hung Chen ◽

Marco P. Schoen ◽

Ken W. Bosworth

Keyword(s):

Particle Swarm Optimization ◽

Tabu Search ◽

Particle Swarm ◽

Search Space ◽

Hybrid Optimization ◽

Local Optimum ◽

Swarm Optimization ◽

Search Spaces ◽

Hybrid Optimization Algorithm ◽

Promising Area

A novel Condensed Hybrid Optimization (CHO) algorithm using Enhanced Continuous Tabu Search (ECTS) and Particle Swarm Optimization (PSO) is proposed. The proposed CHO algorithm combines the respective strengths of ECTS and PSO. The ECTS is a modified Tabu Search (TS), which has good search capabilities on large search spaces. In this study, ECTS is utilized to define smaller search spaces, which are used in a second stage by the basic PSO to find the respective local optimum. The ECTS covers the global search space by using a TS concept called diversification and then selects the most promising areas in the search space. Once the promising regions in the search space are defined, the proposed CHO algorithm employs another TS concept called intensification in order to search the promising area thoroughly. The proposed CHO algorithm is tested with the multi-dimensional Hyperbolic and Rosenbrock problems. Compared to other four algorithms, the simulations results indicate that the accuracy and effectiveness of the proposed CHO algorithm.

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