No abstract

Abstract-SHADE is an adaptive DE which incorporates success-history based parameter adaptation and one of the state-of-the-art DE algorithms. This paper proposes L-SHADE, which further extends SHADE with Linear Population Size Reduction (LPSR), which continually decreases the population size according to a linear function. We evaluated the performance of L-SHADE on CEC2014 benchmarks and compared its search performance with state-of-the-art DE algorithms, as well as the state-of-the-art restart CMA-ES variants. The experimental results show that L-SHADE is quite competitive with state-ofthe-art evolutionary algorithms.

Abstract-Differential Evolution is a simple, but effective approach for numerical optimization. Since the search efficiency of DE depends significantly on its control parameter settings, there has been much recent work on developing self-adaptive mechanisms for DE. We propose a new, parameter adaptation technique for DE which uses a historical memory of successful control parameter settings to guide the selection of future control parameter values. The proposed method is evaluated by comparison on 28 problems from the CEC2013 benchmark set, as well as CEC2005 benchmarks and the set of 13 classical benchmark problems. The experimental results show that a DE using our success-history based parameter adaptation method is competitive with the state-of-the-art DE algorithms.