Knowledge-Base Constrained Optimization Evolutionary Algorithm and its Applications

Wen Long

doi:10.4028/www.scientific.net/AMM.536-537.476

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 536-537Knowledge-Base Constrained Optimization...

Knowledge-Base Constrained Optimization Evolutionary Algorithm and its Applications

Abstract:

The most existing constrained optimization evolutionary algorithms (COEAs) for solving constrained optimization problems (COPs) only focus on combining a single EA with a single constraint-handling technique (CHT). As a result, the search ability of these algorithms could be limited. Motivated by these observations, we propose an ensemble method which combines different style of EA and CHT from the EA knowledge-base and the CHT knowledge-base, respectively. The proposed method uses two EAs and two CHTs. It randomly combines them to generate novel offspring individuals during each generation. Simulations and comparisons based on four benchmark COPs and engineering optimization problem demonstrate the effectiveness of the proposed approach.

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Periodical:

Applied Mechanics and Materials (Volumes 536-537)

Pages:

476-480

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.536-537.476

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Online since:

April 2014

Authors:

Wen Long*

Keywords:

Constrained Optimization Problem, Constraint-Handling Technique, Engineering Application, Evolutionary Algorithm (EA), Knowledge-Base

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* - Corresponding Author

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