A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning

Authors:
Hui Wang;Zhijian Wu;Shahryar Rahnamayan;Lishan Kang
Affiliations:
-;-;-;-
Venue:
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Year:
2009

Citing 0
Cited 6

Diversity analysis of opposition-based differential evolution: an experimental study

ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Hybrid differential evolution algorithm with chaos and generalized opposition-based learning

ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
An effective memetic differential evolution algorithm based on chaotic local search

Information Sciences: an International Journal
A direct optimization approach to the P300 speller

Proceedings of the 13th annual conference on Genetic and evolutionary computation
Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems

Journal of Parallel and Distributed Computing
Survey A review of opposition-based learning from 2005 to 2012

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a scalability test over eleven scalable benchmark functions, provided by the current workshop (Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems - A Scalability Test), are conducted for accelerated DE using generalized opposition-based learning (GODE). The average error of the best individual in the population has been reported for dimensions 50, 100, 200, and 500 in order to compare with the results of other algorithms which are participating in this workshop. Current work is based on opposition-based differential evolution (ODE) and our previous work, accelerated PSO by generalized OBL.