Improved Differential Evolution for Single Objective Optimization Problems

• Ricardo de A. Araújo UFPE
• Germano C. Vasconcelos UFPE
• Tiago A. E. Ferreira UFPE

Resumo

This work proposes the Improved Differential Evolution (IDE) for single objective optimization problems with continuous variables. The proposed IDE uses improved Differential Evolution (DE) operators (mutation and crossover) in order to explore the state space of possible solutions with greater effectiveness, as well as to accelerate its convergence speed. Furthermore, an experimental analysis with the proposed IDE is presented using six well-known benchmark problems of single objective optimization. The results clearly show that the proposed IDE converges faster, as well as find better solutions than the Standard DE (SDE).

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