Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Distributed differential evolution with explorative---exploitative population families
Genetic Programming and Evolvable Machines
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Scale factor inheritance mechanism in distributed differential evolution
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Differential Evolution is a competitive optimizer, with a simplified framework, for numerical optimization problems. Many research works have been done to enhance the performance of Differential Evolution by developing the evolutionary operators. One of the major challenges in DE is performing intelligent search based on population topology. To maintain the diversity in the population as well as to improve the convergence rate, we have introduced a mutation strategy based on relative mapping of the members in population topology. Also Gamma and Cauchy distribution have been adapted in the control parameter framework to include randomness and thorough search. The proposed DE framework is referred to as the Relational Neighbourhood Differential Evolution (ReNbd-DE) and its performance is reported on the set of CEC2005 benchmark functions.