Adaptive Differential Evolution with Difference Mean Based Perturbation for Practical Engineering Optimization Problems

摘要

Differential Evolution(DE) is one of the most versatile evolutionary techniques that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Recent developments on DE includes self adaptation of its parameters (F=step size and CR=cross-over probability) making it a parameter free optimizer. A new self adaptive DE(jDE) proposed by Janez Brest, is a robust improvement of DE, where the self adaptive parameters undergo similar operations of genetic operators. This paper aims at introducing a unique mutation strategy by modifying the existing "DE/rand/1/bin" strategy of jDE with Difference Mean Based Perturbation (DMP) technique. The algorithm addressed as ADE-DMP is basically a variant of jDE, but the modified mutation scheme ensues within the algorithm effective search of area near the current best that effectively proves it to be a better and fast optimizer in complex real world problems of diverse domains.