Abstract
This paper presents a differential evolution (DE) algorithm for real-parameter optimization. The algorithm includes the self-adaptive jDE algorithm with one of its strongest extensions, population reduction, combined with multiple mutation strategies using a structured population. The two mutation strategies used are run dependent on the population size, which is reduced with growing function evaluation number. The population is structured with a separate part where only DE/best strategy is executed and then the best vectors are exchanged with the main population part. Algorithm performance assessment results are presented for 10, 30, and 50 dimension settings for all of the 28 problems included in the Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization.
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