A differential memetic algorithm

Authors:
M. T. Vakil-Baghmisheh;Morteza Alinia Ahandani
Affiliations:
Faculty of Electrical & Computer Engineering, ICT Research Center, University of Tabriz, Tabriz, Iran;Department of Electrical Engineering, Langaroud Branch, Islamic Azad University, Langaroud, Iran
Venue:
Artificial Intelligence Review
Year:
2014

Citing 11
Cited 0

Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions

SIAM Journal on Optimization
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Practical Genetic Algorithms with CD-ROM

Practical Genetic Algorithms with CD-ROM
A hybrid genetic algorithm and particle swarm optimization for multimodal functions

Applied Soft Computing
A comparison of memetic algorithms for the spread spectrum radar polyphase codes design problem

Engineering Applications of Artificial Intelligence
An effective parameter extraction method based on memetic differential evolution algorithm

Microelectronics Journal
The impact of parametrization in memetic evolutionary algorithms

Theoretical Computer Science
Comparison among five evolutionary-based optimization algorithms

Advanced Engineering Informatics
Three modified versions of differential evolution algorithm for continuous optimization

Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Memetic algorithms have been devised to rectify the absence of a local search mechanism in evolutionary algorithms. This paper proposes a differential memetic algorithm (DMA). To this end, first we propose a differential bidirectional random search as a local search algorithm. Then, a randomized blending crossover (RBleX) is proposed which aimed to scatter the new born offspring more diversely in the whole search space. We devise our proposed DMA, by using the RBleX crossover in the GA, and including the DBRS local search algorithm. A comparison of the performance of the DMA and those of seven other evolutionary/memetic or hybrid algorithms reported in two different papers on numerous bechmark functions demonstrates better performance of proposed DMA algorithm in most of the cases.