Volume 11, Issue 1 (1-2021)
IJOCE 2021, 11(1): 55-73 |
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Sangtarash B H, Ghasemi M R, Ghohani Arab H, Sohrabi M R. HYBRID ARTIFICIAL PHYSICS OPTIMIZATION AND BIG BANG-BIG CRUNCH ALGORITHM (HPBA) FOR SIZE OPTIMIZATION OF TRUSS STRUCTURES. IJOCE 2021; 11 (1) :55-73
URL: http://ijoce.iust.ac.ir/article-1-465-en.html
URL: http://ijoce.iust.ac.ir/article-1-465-en.html
Abstract: (8918 Views)
Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired by the theory of the universe evolution and Artificial Physics Optimization (APO) which is a physical base optimization method. Finally, the performance of the proposed optimization method is compared with the originated methods. Moreover, the performance of the proposed algorithm is evaluated for truss optimization as an applied constrained optimization problem.
Keywords: big bang-big crunch (BB-BC), artificial physics optimization (APO), optimization, metaheuristic methods
Type of Study: Research |
Subject:
Optimal design
Received: 2021/01/31 | Accepted: 2021/01/1 | Published: 2021/01/1
Received: 2021/01/31 | Accepted: 2021/01/1 | Published: 2021/01/1
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