Improving the Local Search Ability of Spider Monkey Optimization Algorithm Using Quadratic Approximation for Unconstrained Optimization

Abstract

Spider monkey optimization (SMO) algorithm, which simulates the food searching behavior of a swarm of spider monkeys, is a new addition to the class of swarm intelligent techniques for solving unconstrained optimization problems. The purpose of this article is to study the performance of SMO after incorporating quadratic approximation (QA) operator in it. The proposed version is named as QA-based spider monkey optimization (QASMO). An experimental study has been carried out to check the validity and applicability of QASMO. For validation purpose, the performance of QASMO is tested over a benchmark set of 46 scalable and nonscalable problems, and results are compared with the original SMO algorithm. In order to test the applicability of the proposed algorithm in solving real-life optimization problems, one of the most challenging optimization problems, namely, Lennard-Jones (LJ) problem is considered. LJ clusters containing atoms from three to ten have been taken into consideration, and results are presented. To the best of our knowledge, this is the first attempt to apply SMO and its proposed variant on a real-life problem. The results demonstrate that incorporation of QA in SMO has positive effects on its performance in terms of reliability, efficiency, and accuracy. NOMENCLATUREN swarm size D no. of dimensions U.a; b/ uniformly generated random number between a and b NG number of groups in the current swarm MG maximum number of groups allowed in the swarm Pr perturbation rate GS [k] number of members in the kth group G[k] [0] index of the first member of the kth group in the swarm G[k] [1] index of the last member of the kth group in the swarm SM i position vector of the ith spider monkey in the swarm SM new a trial vector for creating a new position of a spider monkey SM newlocal a trial vector for creating a new position of a spider monkey in local leader phase SM newglobal a trial vector for creating a new position of a spider monkey in global leader phase SM r position vector of randomly selected member of the group SM worstglobal position vector of worst member of the swarm in global leader learning phase SM worstlocal position vector of worst member of a group in local leader learning phase

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“…Quadratic approximation based spider monkey optimization (QASMO) [59] is designed by incorporating quadratic approximation (QA) operator in SMO. The idea of applying QA in SMO is based on various stochastic search techniques such as controlled random search, GA, PSO, DE.…”