Tournament selection mechanism based random vector selection in differential evolution algorithm

Abstract

Differential Evolution (DE) is a simple, powerful and easy to use global optimization algorithm. Trial vector generation mechanism influences the performance of DE algorithm significantly. This research work explores that whether random vector selection in trial vector generation have any role in improving the performance of DE algorithm. A novel tournament selection framework in DE algorithm is proposed to enhance its convergence speed. The novel TSRVDE framework employs tournament selection criteria focuses on the selection of random vector in DE trial vector. We can get rid of worst performing individual selection by TSRVDE that will be helpful to enhance the searching capability of DE algorithm. TSRVDE advancement is applied on the set of frequently used DE variants. To evaluate the performance of TSRVDE a test suit of comprehensive set of well-known multidimensional global optimizations problems is used. The acceleration of TSRVDE can be observed in the experimental results.

ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design

“…In a work by Nish [27], a review of these different methods was presented. In the algorithm developed in the present study, the tournament method developed by Abbas [28] is implemented. This method consists of making a random selection of n particles from the population.…”