Fuzzy genetic sharing for dynamic optimization

Recently, genetic algorithms (GAs) have been applied to multi-modal dynamic optimization (MDO). In this kind of optimization, an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum. Our fuzzy genetic sharing (FGS) approach is based on a novel genetic algorithm with dynamic niche sharing (GADNS). FGS finds the optimal solutions, while maintaining the diversity of the population. For this, FGS uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS. Second, a modified tournament selection is used to control selection pressure. Third, a novel mutation with an adaptive mutation rate is used to locate unexplored search areas. The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator (GDBG).