Statistical Firefly Algorithm for Truss Topology Optimization

TL;DR

This paper introduces a statistical firefly algorithm (SFA) that enhances the original firefly algorithm for truss topology optimization by using hypothesis testing to improve efficiency without sacrificing result quality.

Contribution

The paper presents a novel statistical strategy integrated into the firefly algorithm, improving computational efficiency in truss topology optimization.

Findings

SFA reduces firefly evaluations significantly.

SFA maintains high-quality optimization results.

SFA outperforms the original FA in benchmark tests.

Abstract

This study proposes an algorithm titled a statistical firefly algorithm (SFA) for truss topology optimization. In the proposed algorithm, historical results of fireflies' motions are used in hypothesis testing to limit the motions of fireflies that are suggested by current information exchanges between fireflies only to those that are potentially useful. Hypothesis testing is applied to the mechanism of an ordinary firefly algorithm (FA) without changing its structure. As a result, the implementation of the proposed algorithm is simple and straightforward. Limiting the motions of fireflies to those that are potential useful results in reduction of firefly evaluations, and, subsequently, reduction of computational efforts. To test the validity and efficiency of the proposed algorithm, it is used to solve several truss topology optimization problems, including some benchmark problems. It is found that the added statistical strategy in the SFA significantly enhances the performance of the original FA in terms of computational efforts while still maintains the quality of the obtained results.