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2002•Control theory & applicationsRequires access

A New Genetic Chaos Optimization Combination Method

Yadong Li

Open publisher page 12 citations

Abstract

Based on the analysis of the properties of genetic algorithm and chaos optimization algorithm, a new genetic chaos optimization combination algorithm is presented. The method not only solves the invalidation problem of chaos optimization algorithm in a large scale, but also increases the ability of local search and search precision. Then, the convergence of the algorithm is proved. In the end, this algorithm is applied to six test functions' optimization problem and the simulation shows that the algorithm is effective.

About this research paper

What this paper is about

Based on the analysis of the properties of genetic algorithm and chaos optimization algorithm, a new genetic chaos optimization combination algorithm is presented. The method not only solves the invalidation problem of chaos optimization algorithm in a large scale, but also increases the ability of local search and search precision. Then, the convergence of the algorithm is proved. In the end, this algorithm is applied to six test functions' optimization problem and the simulation shows that the algorithm is effective.

Why it matters

OpenAlex reports 12 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

Based on the analysis of the properties of genetic algorithm and chaos optimization algorithm, a new genetic chaos optimization combination algorithm is presented. The method not only solves the invalidation problem of chaos optimization algorithm in a large scale, but also increases the ability of local search and search precision. Then, the convergence of the algorithm is proved. In the end, this algorithm is applied to six test functions' optimization problem and the simulation shows that the algorithm is effective.

Key concepts: CHAOS (operating system), Meta-optimization, Mathematical optimization, Convergence (economics), Optimization problem, Genetic algorithm, Test functions for optimization, Derivative-free optimization

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