Enhanced Biogeography-based Optimization: A New Method for Size and Shape Optimization of Truss Structures with Natural Frequency Constraints

Abstract

The current study presents an enhanced biogeography-based optimization (EBBO) algorithm for size and shape optimization of truss structures with natural frequency constraints. The BBO algorithm is one of the recently developed meta-heuristic algorithms inspired by the mathematical models in biogeography science and is based on the migration behavior of species among the habitats in the nature. In this study, the overall performance of the standard BBO algorithm is enhanced by new migration and mutation operators. The efficiency of the proposed algorithm is demonstrated by utilizing four benchmark truss design examples with frequency constraints. Numerical results show that the proposed EBBO algorithm not only significantly improves the performance of the standard BBO algorithm, but also finds competitive results compared with recently developed optimization methods.

An optics inspired optimization method for optimal design of truss structures

“…The parameter ε has a major effect on the algorithm's performance. At initial stages of the optimization process, the value of this parameter should be small enough to explore the whole search space (exploration), whereas whatever the optimization process closes to the final stages, it should be large enough to provide more focus on the feasible solutions (exploitation) . Moreover, it should be noted that the value of this parameter may depend on the type of the problem.…”

“…The results obtained using OIO are presented and compared with those obtained by CSS‐BBBC, DPSO, OMGSA, HALC‐PSO, BBO, EBBO, and LCA‐Tie‐II methods in Table . From the results of comparisons given in Table , it is observed that OIO performs surprisingly well and is able to find the lowest structural weight for this design example.…”

Design of Pin Jointed Structures under Stress and Deflection Constraints Using Hybrid Electromagnetism-like Mechanism and Migration Strategy Algorithm

“…Hence, extensive studies have been carried out to develop different optimization methods, ranging from gradient-based search techniques to derivative-free global optimization algorithms. As an alternative to the classical optimization approaches, meta-heuristic optimization techniques such as harmony search (HS) [7] have been widely utilized and improved to solve structural optimization problems characterized by non-convex, dis-continuous, and non-differentiable [8][9][10][11][12][13][14][15][16].…”

“…Hence, extensive studies have been carried out to develop different optimization methods, ranging from gradient-based search techniques to derivative-free global optimization algorithms. As an alternative to the classical optimization approaches, meta-heuristic optimization techniques such as harmony search (HS) algorithm [1], particle swarm optimization (PSO) [2], big bang-big crunch (BB-BC) [3] algorithm, teachinglearning-based optimization (TLBO) [4], Biogeography-Based Optimization (BBO) [5], League Championship Algorithm (LCA) [6], and Cultural Algorithm (CA) [7] have been widely utilized and improved to solve structural optimization problems characterized by non-convex, dis-continuous, and non-differentiable [8][9][10][11][12][13][14][15][16].…”

“…Hence, extensive studies have been carried out to develop different optimization methods, ranging from gradient-based search techniques to derivative-free global optimization algorithms. As an alternative to the classical optimization approaches, meta-heuristic optimization techniques such as harmony search (HS) [7] have been widely utilized and improved to solve structural optimization problems characterized by non-convex, dis-continuous, and non-differentiable [8][9][10][11][12][13][14][15][16].The meta-heuristic algorithms have some advantages such as a simple framework and ease of implementation. Therefore, these algorithms have been adopted by researchers so far and are well suited to solve various structural optimum design problems including the sizing, layout, and topology optimization problems [17][18][19][20][21].…”