A hybrid method of evolutionary algorithms is introduced in this study. The hybrid method includes two additional operations, acceleration and migrating operations. These two operations are used for the improvement of the convergence speed without decreasing diversity among the individuals. The hybrid method incorporated with multiplier updating is introduced to solve a dynamic optimization pro...

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization. In this paper, we propose a novel hybrid DE variant to accelerate the convergence rate of the classical DE algorithm. The proposed algorithm is hybridized with a convex mutation. The convex mutation is able to utilize the information of the parents, and hence, provides faster convergen...

Based on the principle of maximum entropy method (MEM) for quantitative texture analysis, the differential evolution (DE) algorithm was effectively introduced. Using a DE-optimized algorithm with a faster but more stable convergence rate of iteration, more reliable complete orientation distribution functions (C-ODF) have been obtained for deep-drawn IF steel sheets and the recrystallized alumin...

This paper presents a novel differential evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. ...

This paper proposes method for solving steel alloying problem using evolution algorithms SOMA and differential evolution. Both algorithms belong to the family of the evolution algorithms but the main ideas of these algorithms are different. In differential evolution new offspring is created during the evolution, the individuals are crossed and mutated, while in SOMA the individuals move in the ...

the selection of gene subsets that retain high predictive accuracy for certain cell-type classification, poses a central problem in microarray data analysis. The application and combination of various computational intelligence methods holds a great promise for automated feature selection and classification. In this paper, we present a new approach based on evolutionary algorithms that addresse...

The study and research of evolutionary algorithms (EAs) is getting great attention in recent years. Although EAs have earned extensive acceptance through numerous successful applications in many fields, the problem of finding the best combination of evolutionary parameters especially for population size that need the manual settings by the user is still unresolved. In this paper, our system is ...

Despite the fact that evolutionary algorithms often solve static problems successfully, dynamic optimization problems tend to pose a challenge to evolutionary algorithms [21]. Differential evolution (DE) is one of the evolutionary algorithms that does not scale well to dynamic environments due to lack of diversity [35]. A significant body of work exists on algorithms for optimizing dynamic prob...

Many real-world optimization problems are large-scale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problems’ particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algo...

This paper presents a differential evolution algorithm to solve continuous function optimization problems. The algorithm was tested using 14 newly proposed benchmark instances in Congress on Evolutionary Computation 2005. For these benchmark problems, the problem definition files, codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since these benchmarks are newl...