Algorithm Design Issues in Adaptive Differential Evolution: Review and taxonomy

View/ Open
Date
2018-05-09Abstract
The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. A trend that has emerged recently is to make the algorithm parameters automatically adapt to different problems during optimization, thereby liberating the user from the tedious and time-consuming task of manual setting. These
fine-tuning techniques continue to be the object of ongoing research. Differential evolution (DE) is a simple yet powerful population-based metaheuristic. It has demonstrated good convergence, and its principles are easy to understand. DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to
investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the
different variants of adaptive DE and convey them in a new classification style. In other words, this study describes in depth the structural analysis and working principle that underlie the promising and recent work in this field, to analyze their advantages and disadvantages and to gain future insights that can further improve these algorithms. Finally, the interpretation of the literature and the comparative analysis of the results offer several guidelines for designing and implementing adaptive DE algorithms. The proposed design framework provides readers with the main steps required to integrate any proposed meta-algorithm into parameter and/or strategy adaptation schemes.
Description
Citation : Al-Dabbagh, R.D., Neri, F., Idris, N., Baba, M.S. (2018) Algorithm Design Issues in Adaptive Differential Evolution: Review and taxonomy. Swarm and Evolutionary Computation, 43, pp. 284-311
ISSN : 2210-6502
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes
Collections
Related items
Showing items related by title, author, creator and subject.
-
Derivation of data intensive algorithms by formal transformation: the Schorr-Waite graph marking algorithm
Ward, Martin (Article)In this paper we consider a particular class of algorithms which present certain difficulties to formal verification. These are algorithms which use a single data structure for two or more purposes, which combine program ... -
Training neural networks with ant colony optimization algorithms for pattern classification
Mavrovouniotis, Michalis; Yang, Shengxiang (Article)Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on: a) the configuration selected and b) the training process. Once the ... -
A Multi-Objective Optimization Approach Using Genetic Algorithms for Quick Response to Effects of Variability in Flow Manufacturing
This paper exemplifies a framework for development of multi-objective genetic algorithm based job sequencing method by taking account of multiple resource constraints. Along this, Theory of Constraints based Drum-Buffer-Rope ...