Journal of Global Optimization
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
Mixed mutation strategy embedded differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress 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
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In the present work we propose a modified variant of Differential Evolution (DE) algorithm named MDE. MDE differs from the basic DE in the manner in which the base vector is generated. While in simple/basic DE, base vector is usually randomly selected from the population of individuals, in MDE base vector is generated as convex linear combination (clc) of three randomly selected vectors out of which one is the one having best fitness value. This mutation scheme is used stochastically with mutation scheme in which the base generated using a clc of three randomly generated vectors. MDE is validated on a set of benchmark problems and is compared with basic DE and other DE variants. Numerical and statistical analysis shows the competence of proposed MDE.