Open Access
Differrential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces
R. Storn
- Vol. 11
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The article was published on 1995-01-01 and is currently open access. It has received 2821 citations till now. The article focuses on the topics: Global optimization & Simple (abstract algebra).read more
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Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
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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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A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga,Bahriye Akay +1 more
TL;DR: Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.
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Opposition-Based Differential Evolution
TL;DR: This paper presents a novel algorithm to accelerate the differential evolution (DE), which employs opposition-based learning (OBL) for population initialization and also for generation jumping and results confirm that the ODE outperforms the original DE and FADE in terms of convergence speed and solution accuracy.
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Recent advances in differential evolution – An updated survey
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.