An efficient robust hyper-heuristic algorithm to clustering problem

Abstract

Designing and modeling an optimization algorithm with dedicated search is a costly process and it need a deep analysis of problem. In this regard, heuristic and hybrid of heuristic algorithms have been widely used to solve optimization problems because they have been provided efficient way to find an approximate solution but they are limited to use number of different heuristic algorithm and they are so problem-depend. Hyper-heuristic is a set of heuristics, meta- heuristics, and high-level search strategies that work on the heuristic search space instead of solution search space. Hyper-heuristics techniques have been employed to develop approaches that are more general than optimization search methods and traditional techniques. The aim of a hyperheuristic algorithms is to reduce the amount of domain knowledge by using the capabilities of high-level heuristics and the abilities of low-level heuristics simultaneously in the search strategies. In this study, an efficient robust hyperheuristic clustering algorithm is proposed to find the robust and optimum clustering results based on a set of easy-to-implement low-level heuristics. Several data sets are tested to appraise the performance of the suggested approach. Reported results illustrate that the suggested approach can provide acceptable results than the alternative methods.