Gravitational Search Algorithm with Nearest-Better Neighborhood for Multimodal Optimization Problems

Document Type : Persian Original Article

Authors

1 Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khoramabad, Iran.

2 Yazd University

3 Department of Electrical Engineering, Shahid Bahonar university of Kerman, Kerman, Iran

Abstract

Gravitational Search Algorithm (GSA) is a simple and efficient optimization method recently proposed for solving single-objective optimization problems. In this paper, for the first time, the nearest-better neighborhoods are defined in swarm intelligence algorithms and then used in the GSA to solve multi-modal optimization problems. For this purpose, two neighborhoods are defined, called Topological Nearest-Better (TNB) and Distance-based Nearest-Better (DNB), and then these two structures are used separately in the GSA and two different versions of the GSA for multi-modal optimization problems are provided. To investigate the efficiency of the proposed algorithms, an empirical assessment has been performed on several standard multi-modal benchmark functions. The results of these experiments show that the proposed algorithms can achieve good results compared to other multi-modal optimizer algorithms.

Keywords

Journal of Soft Computing and Information Technology
Volume 8, Issue 3 - Serial Number 25
Autumn
September 2019
Pages 10-19

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