Journal of Global Optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Fuzzy keyword search over encrypted data in cloud computing
INFOCOM'10 Proceedings of the 29th conference on Information communications
A clustering-based differential evolution for global optimization
Applied Soft Computing
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
Information Sciences: an International Journal
Learning-enhanced differential evolution for numerical optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Computation on General Purpose Graphics Processing Units
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In the field of evolutionary algorithm EA, differential evolution DE is successfully used in various scientific and engineering fields due to its strong global optimisation capability and simple implementation. However, in most of DE, the search is guided by the random or local optimal vectors. That is, DE does not effectively use the good information of population to guide the search. Therefore, to alleviate this drawback and enhance the search ability of DE, a competent leaders guiding strategy cLGS is proposed in this paper. The proposed cLGS is inspired by the natural phenomenon that good species usually contain useful information to guide the search of population. With the competent leaders, the good information of population can be utilised effectively during the evolutionary process. By incorporating cLGS into JADE which is a very competitive DE variant, the resulting algorithm, named JADE-cLGS, is proposed. In order to test the effectiveness of JADE-cLGS, a suite of benchmark functions is used. Experimental results demonstrate the high performance of JADE-cLGS by comparing with several DE variants.