Book Chapter10.1007/978-3-642-23777-5_39
An Improved Differential Evolution Algorithm for Optimization Problems
Libiao Zhang,Xiangli Xu,Chunguang Zhou,Ming Ma,Zhezhou Yu +4 more
- 01 Jan 2011
- pp 233-238
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TL;DR: The mutation of the classical DE is improved and effectively guarantees the convergence of the algorithm and avoids the local minima in this paper.
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Abstract: There are many optimization problems in the intelligent material and adaptive material fields. Differential evolution (DE) is simple and effective and has been successfully applied to solve optimization problems. And it can be applied to intelligent material field. It is easy to understand and realized and has a strong spatial search capability compared to other evolutionary algorithms. In order to avoid the original versions of DE to remain trapped into local minima and accelerate the optimization process, several approaches have been proposed. The mutation of the classical DE is improved in this paper. It effectively guarantees the convergence of the algorithm and avoids the local minima. Testing and comparing results showed the effectiveness of the algorithm.
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Citations
Enhanced Butterfly Optimization Algorithm with a New fuzzy Regulator Strategy and Virtual Butterfly Concept
Ali Mortazavi,Mahsa Moloodpoor +1 more
TL;DR: In this article, a new fuzzy decision-making strategy and a new auxiliary concept called virtual butterfly are introduced to enhance the search capability of the standard butterfly optimization algorithm, which is called Fuzzy Butterfly Optimization Algorithm (FBOA).
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•Posted Content
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA
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35
Differential evolution method integrated with a fuzzy decision-making mechanism and Virtual Mutant agent: Theory and application
Ali Mortazavi,Mahsa Moloodpoor +1 more
TL;DR: The acquired results indicate that the proposed Fuzzy Differential Evolution incorporated Virtual Mutant could improve the search process in the terms of computational cost, stability and accuracy.
19
Adaptive Athlete Training Plan Generation: An intelligent control systems approach.
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Integrated Pairwise Testing based Genetic Algorithm for Test Optimization
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References
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