Journal of Global Optimization
Two improved differential evolution schemes for faster global search
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Minimal representation multisensor fusion using differential evolution
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Differential Evolution (DE) is one of the most simple and efficient Evolutionary Algorithms exist till now for global optimization problems. It has reported exceptionally good results when tested over all the benchmark problems and some of the real world problems, although it suffers from the troubles of slow and premature convergence. Generally the performance of DE is sensitive to the choice of mutation and crossover strategies and their associated control parameters. In this paper we propose a DE called Adaptive Differential Evolution with Directional Information based Search Moves (ADE-DISM) in which we basically have improved the mutation and crossover strategies adopted in 'DE/rand/1/bin'. In ADE-DISM we varied the control parameters F and CR in an adaptive manner and have introduced a new parameter w. We have used some directional information based moves over the population and introduced a Mean_Best_Vector for mutation purpose. However, the proposed scheme is shown to be statistically significantly better than or at least comparable to several existing DE variants when tested over the CEC 2005 benchmark problems for 30 and 50 dimensions of the problems.