Experimental analysis of binary differential evolution in dynamic environments

Abstract

Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems has increased greatly. Differential Evolution (DE) belongs to the group of evolutionary algorithms which operate in continuous search spaces. DE has been successfully applied to many stationary problem domains. Recently there has been some research into applying DE to dynamic optimization problems too. Many real-world problems consist of decision variables which require the optimization algorithm to work with binary parameters. This makes it impossible to apply DE in its basic form. For this purpose, binary differential evolution (BDE) approaches have been introduced. The main focus of this paper is to perform a series of experiments to test the behavior of a simple BDE under different change conditions. A simple bit-matching problem is chosen as the test environment. The results of this preliminary study show that further study is needed to make BDEs suitable to work in dynamic environments.

Classification of Gene Expression Data Using Multiobjective Differential Evolution

“…We replace our binary differential evolution in MOBDE by using the binary DE, BDE and BDEAIS [33,34,39]. That is to say, all of these methods use the same multiobjective framework to conduct a fair comparison.…”

“…The datasets are classified by LIBSVM based on LOOCV. We compared our method with some binary differential evolution algorithms: binary DE [33], binary differential evolution (BDE) [34], binary differential evolution with artificial immune system (BDEAIS) [39], binary particle swarm optimization (BPSO) [40], binary genetic algorithm (BGA) [41] and binary estimation distribution algorithm (BEDA) [42]. In this paper, we replace these methods with our binary method and then compare our method to show the effective of the algorithm.…”

“…For all algorithms, the population size is 50; the maximum number of iterations is 100. For the different version of binary DE algorithms [33,34,39], the value of the F is 0.5, and the value of CR is 0.7. For the genetic algorithm, the cross-over rate is 0.7, and the mutation rate is 0.5.…”

“…Therefore, we can draw the conclusion that our algorithm MOBDE significantly outperforms the other algorithms. [39] 0.02959 BDE [39] 0.0009766 BDEAIS [33] 0.01125 BDEAIS [33] 0.002945 BGA [40] 0.007074 BGA [40] 0.0009766 BPSO [41] 0.01125 BPSO [41] 0.0009766 EDA [42] 0.01802 EDA [42] 0.0009766 Grid search SVM 0.0009766 Grid search SVM 0.0009766 IBPSO [8] 0.01802 IBPSO [8] 0.04199 IBPSO [6] 0.01802 IBPSO [6] 0.0009766 NSGAII 0.007133 NSGAII 0.0009766…”

CellularDE: A Cellular Based Differential Evolution for Dynamic Optimization Problems

“…It has been shown that Differential evolution algorithm (DE) [11] is be a simple and powerful algorithm for continuous function optimization, not even in static [12] but also in dynamic environments [7,[13][14][15]. Moreover, DynDE [7], which to the best of our knowledge is the best-performing differential evolution algorithm for dynamic optimization problems, produces competitive results compared to other dynamic optimization algorithms.…”