A gravitational search algorithm with hierarchy and distributed framework

Gravitational search algorithm is an effective population-based algorithm. It simulates the law of gravity to implement the interaction among particles. Although it can effectively optimize many problems, it generally suffers from premature convergence and low search capability. To address these limitations, a gravitational search algorithm with hierarchy and distributed framework is proposed. A distributed framework randomly groups several subpopulations and a three-layered hierarchy manages them. Communication among subpopulations finally enhances the search performance. Experiments discuss parameters and strategies of the proposed algorithm. Comparison between it and sixteen stateof-the-art algorithms demonstrates its superior performance. It also shows the practicality for two real-world optimization problems. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The gravitational search algorithm simulates the law of gravity to achieve interaction among particles, but it suffers from premature convergence and low search capability. To address these limitations, a gravitational search algorithm with hierarchy and distributed framework is proposed, enhancing search performance through communication among subpopulations.