A new ensemble algorithm of differential evolution and backtracking search optimization algorithm with adaptive control parameter for function optimization

Article history: Received July 21 2015 Received in Revised Format August 16 2015 Accepted September 8 2015 Available online September 12 2015 Differential evolution (DE) is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA) is a new evolutionary algorithm (EA) for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy. Also the performance results are compared with state of the art PSO variant. © 2016 Growing Science Ltd. All rights reserved

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