Success-history based parameter adaptation for Differential Evolution

Abstract

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.

Optimization of modified T‐shape microstrip patch antenna using differential algorithm for X and ku band applications

“…DE inherits some of its features from other EAs. [13][14][15] it differs considerably in some aspects as distance and direction information is used for guiding the search process. It works with the iterative method to optimize the given parameters for improving the candidate result.…”

“…[19], dual-band circularly polarized spidron fractal microstrip patch antenna for X and Ku band applications had been designed with frequency band ranging from 11.44 to 12.48 GHz and 13.47 to 14.39 GHz, the overall size was 50 3 50 3 1.52 mm 3 , this was again considered to be very large and resulted in a complex design. Therefore, visualizing the overall antenna size issues, the main aim of this paper is to design an inset-fed dual-band microstrip antenna using DE with small and compact size which covers the specified frequency range for application in X band (8)(9)(10)(11)(12) and Ku band (12)(13)(14)(15)(16)(17)(18) in Ref. [20].…”

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

“…Control parameters are automatically updated according to previously successful experiences. In success-history based adaptive DE (SHADE) [31], a new parameter adaptation mechanism which is based on the successful searching experience is proposed. Many variants of parameters control such as FiADE, DMPSADE and DESSA are available in the literature [32][33][34].…”

Improved Biogeography-Based Optimization Based on Affinity Propagation

“…For convenient description, these functions [19] are denoted as F1-F15, as shown in Table 1. The SPS-L-SHADE-EIG algorithm combines the adaptive differential evolution [20,21] with linear population size reduction(L-SHADE) [22] with the eigenvector-based (EIG) [23] crossover and successful-parent-selecting (SPS) frameworks [24]. The DEsPA algorithm is a new Differential Evolution algorithm with a success-based parameter adaptation with resizing population space [25].…”