Journal Article10.1016/J.NEUCOM.2020.09.007
Differential evolution algorithm with multi-population cooperation and multi-strategy integration
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TL;DR: The research results reveal that the performance of MPEDE may be improved by adding an information sharing mechanism and modifying the grouping mechanism, and the experimental results show that thePerformance of the MPMSDE algorithm is very competitive in calculation accuracy and convergence speed.
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About: This article is published in Neurocomputing. The article was published on 15 Jan 2021. The article focuses on the topics: Evolutionary algorithm & Differential evolution.
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Citations
A new selection operator for differential evolution algorithm
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Differential evolution with mixed mutation strategy based on deep reinforcement learning
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An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization
Jiao Wang,Yan Li,Gang Yi Hu,Mingshun Yang +3 more
TL;DR: In this paper , an enhanced artificial hummingbird algorithm based on golden sine factor (DGSAHA) is proposed, where chaos mapping is used to generate the initial candidate solution to increase the diversity of the population, which lays the foundation for the global search.
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An elite-guided hierarchical differential evolution algorithm
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TL;DR: An elite-guided hierarchical mutation mechanism is presented, which integrates elite elements into the hierarchical population structure, and the proposed EHDE has excellent optimization performance.
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References
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
SCA: A Sine Cosine Algorithm for solving optimization problems
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Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
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JADE: Adaptive Differential Evolution With Optional External Archive
Jingqiao Zhang,A.C. Sanderson +1 more
TL;DR: Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems.
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Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems
TL;DR: The results show that the algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained.
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