Based Cost Differential Evolution Algorithm for Multi-objective Optimization Problems
Keywords:Differential Evolution, global numerical optimization, multi-objective optimisation
In this paper, we present A new variant of the Differential Evolution algorithm. Our proposed algorithm called Based cost Differential Evolution (Bc-DE), generated the mutant vector by adding a weighted difference of two cost function multiplied by a vector selected randomly to the best individual vector. The performance of the Bc-DE algorithm is broadly evaluated on several bound-constrained nonlinear and non-differentiable continuous numerical optimization problems and compares with the conventional DE and several others DE variants. It is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods.
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