Chaotic Evolution Algorithm with Elite Strategy in Single-objective and Multi-objective Optimization

We propose a chaotic evolution algorithm with elite strategy. The conventional chaotic evolution algorithm uses each individual to search in its local area. The proposed algorithm searches the parameter space always around the elite individual from the last generation. We evaluate the proposed algor...

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Published in:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 579 - 584
Main Author: Pei, Yan
Format: Conference Proceeding
Language:English
Published: IEEE 11.10.2020
Subjects:
Benchmark testing
chaotic evolution
Conferences
Cybernetics
elite strategy
multi-objective optimization
Optimization
single-objective optimization
ISSN:2577-1655
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Abstract We propose a chaotic evolution algorithm with elite strategy. The conventional chaotic evolution algorithm uses each individual to search in its local area. The proposed algorithm searches the parameter space always around the elite individual from the last generation. We evaluate the proposed algorithm in both single-objective and multi-objective optimization problems. In the single objective optimization problem, the elite is the individual has the best fitness value, and in the multi-objective optimization problem, the elites are the individuals in the first Pareto front. We design and evaluate these two algorithms with elite strategy using single- and multi-objective benchmark functions. We design a jump strategy to avoid searching within a local optima areas by applying elite strategy several generations one time. The numerical evaluation results demonstrate the proposed algorithm has strong local exploitation capability in the early generations. The optimization performance of chaotic evolution algorithm has a potential possibility to apply in high dimensional and more complex optimization problems.
AbstractList We propose a chaotic evolution algorithm with elite strategy. The conventional chaotic evolution algorithm uses each individual to search in its local area. The proposed algorithm searches the parameter space always around the elite individual from the last generation. We evaluate the proposed algorithm in both single-objective and multi-objective optimization problems. In the single objective optimization problem, the elite is the individual has the best fitness value, and in the multi-objective optimization problem, the elites are the individuals in the first Pareto front. We design and evaluate these two algorithms with elite strategy using single- and multi-objective benchmark functions. We design a jump strategy to avoid searching within a local optima areas by applying elite strategy several generations one time. The numerical evaluation results demonstrate the proposed algorithm has strong local exploitation capability in the early generations. The optimization performance of chaotic evolution algorithm has a potential possibility to apply in high dimensional and more complex optimization problems.
Author Pei, Yan
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  givenname: Yan
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  organization: University of Aizu,Computer Science Division,Aizu-Wakamatsu,Fukushima,Japan,965-8580
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Snippet We propose a chaotic evolution algorithm with elite strategy. The conventional chaotic evolution algorithm uses each individual to search in its local area....
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StartPage 579
SubjectTerms Benchmark testing
chaotic evolution
Conferences
Cybernetics
elite strategy
multi-objective optimization
Optimization
single-objective optimization
Title Chaotic Evolution Algorithm with Elite Strategy in Single-objective and Multi-objective Optimization
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