Adaptive Guidance-based Differential Evolution with Iterative Feedback Archive Strategy for Multimodal optimization Problems

摘要

Multimodal optimization problems (MMOPs) target to locate multiple global optima simultaneously, which require the algorithms not only can maintain the population diversity to locate the global optima as many as possible, but also can ensure the convergence on each found optimal region to refine the solutions as high accuracy as possible. Aiming to these two goals and to efficiently deal with MMOPs, an adaptive guidance-based differential evolution (AGDE) with archive strategy is proposed in this paper, including three novel components. Firstly, an adaptive mutation strategy (AMS) is introduced, which guides the current individual to move towards the peak that is closest to itself. Secondly, an iterative feedback archive (IFA) strategy is used to store the global optima of the population in every iteration. Thirdly, a Gaussian disturbance-based elite learning (GDEL) strategy is performed on the archive to refine the accuracy of the solutions. The AMS strategy helps to locate more peaks, while the IFA and GDEL strategies help to maintain the found solutions and refine their accuracy. The performance of AGDE is tested on 20 widely used multimodal benchmark functions of CEC’2013. The experimental results of AGDE are competitive with the results obtained by the state-of-the-art multimodal algorithms.