Adaptation of operators and continuous control parameters in differential evolution for constrained optimization

A large number of differential evolution algorithms has been proposed in recent years, many of which have been used to solve mainly unconstrained problems. However, similar to other evolutionary algorithms, their performances are highly dependent on their search operators and control parameters. Although many investigations have been conducted to ensure appropriate choices of these operators and parameters, the task is recognized as tedious. In this research, a differential evolution algorithm, which includes a new mechanism for automatically selecting the best combinations of parameters (amplification factor, crossover rate, and population size) as well as search operators, is developed. Instead of choosing discrete values for the amplification factor and crossover rate from a given set of values, this study adaptively selects them from some given continuous ranges and, furthermore, proposes a new methodology for handling constraints. The performance of the algorithm is assessed using a well-known set of constrained problems, with the experimental results demonstrating that it is superior to state-of-the-art algorithms.

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