An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

Abstract EN

Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size(NP), appropriately is one of the major problems of Differential Evolution (DE) literature.

Well-designedadaptive or self-adaptive parameter control method can highly improve the performance of DE.

Althoughthere are many suggestions for adapting the control parameters, it is still a challenging task to properly adaptthe control parameters for problem.

In this paper, we present an adaptive parameter control DE algorithm.

In the proposed algorithm, each individual has its own control parameters.

The control parameters of eachindividual are adapted based on the average parameter value of successfully evolved individuals’ parametervalues by using the Cauchy distribution.

Through this, the control parameters of each individual are assignedeither near the average parameter value or far from that of the average parameter value which might bebetter parameter value for next generation.

The experimental results show that the proposed algorithmis more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solvingvarious unimodal and multimodal problems.