A new algorithm for improving deficiencies of past self-organized criticality based extinction algorithms

. In this paper new ideas are presented for resolving the issues of two past Self-Organized Criticality (SOC) Evolutionary Algorithms (EAs). The concept of SOC was first developed for modeling Mass Extinction and implemented by means of Sand Pile model in EAs. These types of EAs are especially employed when the optimization problems are multimodal in which preserving the diversity of solutions is a crucial task. Therefore analyzing the problems of SOC based EAs is worthwhile for making a progress in the field of multimodal optimization. Consequently, after an exact inspection of past research studies, the major shortcomings of previously developed algorithms are addressed which are twofold: firstly, the lack of avalanches in early generations, and secondly, the number of avalanches occurred in a population is out of proportion in terms of population size. In order to resolve these problems, some solutions are proposed in this study. The impact of these modifications are examined and illustrated by means of several benchmark optimization problems extracted from past research studies. Modified algorithm is compared and contrasted against previously developed SOC based algorithms and classical Genetic Algorithm (CGA). Results apparently show the effectiveness of eliminating addressed deficiencies in terms of accuracy and escaping from local optima.