A Modified Differential Evolution Algorithm Applied to Challenging Benchmark Problems of Dynamic Optimization

摘要

Many real-world optimization problems are dynamic in nature. In order to deal with these Dynamic Optimization Problems (DOPs), an optimization algorithm must be able to continuously locate the optima in the constantly changing environment. In this paper, we propose a multi-population based differential evolution (DE) algorithm to address DOPs. This algorithm, denoted by pDEBQ, uses Brownian & adaptive Quantum individuals in addition to DE individuals to increase the diversity & exploration ability. A neighborhood based new mutation strategy is incorporated to control the perturbation & there by to prevent the algorithm from converging too quickly. Furthermore, an exclusion rule is used to spread the subpopulations over a larger portion of the search space as this enhances the optima tracking ability of the algorithm. Performance of pDEBQ algorithm has been evaluated over a suite of benchmarks used in Competition on Evolutionary Computation in Dynamic and Uncertain Environments, CEC'09.