Artificial Glowworm Swarm Optimization Algorithm for Solving Multi-Objective Constrained Optimization

Qi Fang Luo; Qiao Qiao Gong; Yong Quan Zhou

doi:10.4028/www.scientific.net/AMM.220-223.2393

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 220-223Artificial Glowworm Swarm Optimization Algorithm...

Artificial Glowworm Swarm Optimization Algorithm for Solving Multi-Objective Constrained Optimization

Abstract:

Focused on the disadvantages of some current constrained optimization algorithm, glowworm swarm multi-objective optimization algorithm ( GSMOA） is proposed in this paper. The main character of this algorithm conforms to feasibility rules and adapts self- adaptive penalty function to search feasible solutions. This algorithm has been tested on 4 standard functions and it shows that the proposed algorithm has more advantage in the convergence rate and the solution precision.

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Periodical:

Applied Mechanics and Materials (Volumes 220-223)

Pages:

2393-2397

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.220-223.2393

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Online since:

November 2012

Authors:

Qi Fang Luo, Qiao Qiao Gong, Yong Quan Zhou

Keywords:

Constrained Optimization, Dynamically Changing Penalty Function, Feasibility Rules, Glowworm Swarm Optimization

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