Search

• The adaptation of parameter and strategy is realized through accompanying evolution. • Accompanying population and generalized opposition-based learning enhance the population diversity. • Radial spatial projection is used for dynamic analysis of evolutionary direction. Differential evolution (DE) is an intelligent optimization algorithm inspired by biological evolution. Setting a mutation strategy and control parameters that meet the optimization requirements are the premise for DE to achieve good performance. This paper proposes a parameter and strategy adaptive differential evolution algorithm based on accompanying evolution (APSDE). Through the accompanying population, in which individuals are composed of suboptimal solutions, the mutation strategy and control parameters are optimized to realize the adaptation of the strategy and parameters of the main population. Population diversity is enhanced in evolution by generating reverse individuals. In addition, radial spatial projection technology is utilized to track the change in evolution direction with optimization. The performance of APSDE is validated under four sets of benchmark problem suites from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC), and compared with state-of-the-art optimization algorithms. The results show that the proposed algorithm has better optimization performance than the competitive algorithms because of its efficient adaptive mechanism and its excellent population diversity. [ABSTRACT FROM AUTHOR]

The paper describes the problem of bias in parameter adaptation in Differential Evolution and other Evolutionary Algorithms. Based on the newly proposed Expected Fitness Improvement metric, the shift towards exploitation is demonstrated. The generalized Lehmer mean and Linear Bias Reduction are for the first time proposed to control the parameter adaptation bias for the fitness improvement based L-SHADE and distance based Db-L-SHADE algorithms. The experiments are performed on the benchmark functions of the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2017 competition on real-parameter optimization. The influence of the modified scaling factor and crossover rate adaptation is evaluated using Friedman ranking procedure and Mann–Whitney statistical test. The usage of Lehmer mean and Linear Bias Reduction is shown to deliver statistically better results for high-dimensional functions and improve the exploration properties of the search algorithm in the long-term perspective. To support the statements made, the search process is additionally analyzed using diversity measures and cluster analysis techniques. [ABSTRACT FROM AUTHOR]