Abstract
The performance of a hybrid CODEQ method, called HCODEQ method, proposed in this work is compared with that of the CODEQ and differential evolution (DE) methods. CODEQ method is different from the original differential evolution, the concepts of chaotic search, opposition-based learning, and quantum mechanics are used in the CODEQ method to overcome the drawback of selection of the crossover factor and scaling factor used in the original DE method. And, the convergence of the CODEQ outperformed than the DE method. However, a larger population size must be used in the CODEQ method. That is a drawback for all evolutionary algorithms (EAs). To overcome this drawback, two operations, acceleration operation and migrating operation, are embedded into the CODEQ method. The use of these two operations can improve the convergence speed without decreasing the diversity among individuals. One benchmark function is used to compare the performance of the HCODEQ method with the CODEQ and DE methods. Numerical results show that the performance of the HCODEQ method is better than the other methods.
Published Version
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