Beetle Swarm Optimization Algorithm:Theory and Application

TL;DR

This paper introduces a novel meta-heuristic algorithm inspired by beetle foraging, demonstrating superior performance over existing algorithms on benchmark functions and engineering design problems.

Contribution

The paper proposes the beetle swarm optimization algorithm, enhancing swarm optimization with beetle foraging principles, and validates its effectiveness through extensive experiments.

Findings

Outperforms particle swarm, genetic, and grasshopper algorithms on benchmark tests.

Achieves competitive results in engineering design problems.

Demonstrates improved convergence and solution quality.

Abstract

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, genetic algorithm (GA) and grasshopper optimization algorithm . Numerical experiments show that the beetle swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblaus optimization problem, are also considered and the proposed beetle swarm optimization algorithm is shown to be competitive in those applications.