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A Particle Swarm Optimization with Feasibility-Based Rules for Mixed-Variable Optimization Problems

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3 Author(s)
Chao-Li Sun ; Complex Syst. & Comput. Intell. Lab., Taiyuan Univ. of Sci. & Technol., Taiyuan, China ; Jian-chao Zeng ; Jeng-Shyang Pan

A Particle Swarm Optimization algorithm with feasibility-based rules (FRPSO) is proposed in this paper to solve mixed-variable optimization problems. An approach to handle various kinds of variables is discussed. Constraint handling is based on simple feasibility-based rules, not needing addinional penalty parameters and not guaranteeing to be in the feasible region at all times. Two real-world mixed-varible optimization benchmark problems are presented to evaluate the performance of the FRPSO algorithm, and it is found to be highly competitive compared to other existing stochastic algorithms.

Published in:

Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on  (Volume:1 )

Date of Conference:

12-14 Aug. 2009

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