Fuzzy Logic Based Parameter Adaptation of Interior Search Algorithm

Abstract

Interior Search Algorithm (IS A) is a novel algorithm recently developed for solving optimization problems. Though, this metaheuristic has proven to be effective for function optimization on widely used benchmark functions, this algorithm may converge slowly or be trapped in local minima for complex functions. In this paper, a new hybrid approach of IS A is presented. The proposed approach includes a fuzzy logic system for improving convergence of the solutions in IS A by dynamic parameter adaptation. To validate the performance of the proposed algorithm, several comparisons are presented on the basis of benchmark functions. Simulation results demonstrated the efficiency of the proposed fuzzy IS A algorithm.