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| Abstract |
We propose a triple comparison-based interactive differential evolution (IDE) algorithm and differential evolution (DE) algorithm. The comparison of target vector and trial vector supports a local fit...ness landscape for IDE and DE algorithms to conduct a memetic search. In addition to the target vector and trial vector used in canonical IDE and DE algorithm frameworks, we conduct a memetic search around whichever vector has better fitness. We use a random number from a normal distribution generator or a uniform distribution generator to perturb the vector, thereby generating a third vector. By comparing the target vector, the trial vector, and the third vector, we implement a triple comparison mechanism in IDE and DE algorithms. A Gaussian mixture model is used as a pseudo-IDE user for evaluating the IDE and 25 benchmark functions from the CEC2005 test suite are employed to evaluate the DE. We compare our proposals with canonical IDE and triple comparison-based IDE implemented by opposite-based learning and apply several statistical tests to investigate the significance of our proposed algorithms. We also compare our proposals with several evaluation metrics, such as number of function calls, success rate and acceleration rate. Our proposed triple comparison-based IDE and DE algorithms show significantly better optimization performance arising from the evaluation results. We also investigate potential issues arising from our proposal and discuss some open topics and future opportunities.show more
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