Ensemble Laplacian Biogeography-Based Sine Cosine Algorithm for Structural Engineering Design Optimization Problems

TL;DR

This paper introduces LX-BBSCA, an ensemble metaheuristic combining Laplacian Biogeography-Based Optimization and Sine Cosine Algorithm, to improve structural engineering design optimization by enhancing convergence and avoiding local minima.

Contribution

The paper presents a novel ensemble algorithm that outperforms existing metaheuristics on benchmark functions and real-world structural engineering problems.

Findings

LX-BBSCA outperforms basic BBO, SCA, and LX-BBO algorithms.

It achieves better convergence and solution quality.

Statistical tests confirm the significance of improvements.

Abstract

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian Biogeography-Based Optimization (LX-BBO) and the Sine Cosine Algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA, and LX-BBO but also other state-of-the-art metaheuristic algorithms.