Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 392, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2022.114616
Keywords
Optimization algorithm; Metaheuristic algorithm; Bio-inspired algorithm; Swarm intelligence algorithm; Quantum computing; Applied mechanics and engineering problems
Ask authors/readers for more resources
This paper presents a novel bio-inspired algorithm called SMO, which mimics the behaviors of starlings during their stunning murmuration, to solve complex engineering optimization problems. The SMO introduces dynamic multi-flock construction and three new search strategies, achieving competitive results in solution quality and convergence rate compared to other state-of-the-art algorithms.
This paper presents a novel bio-inspired algorithm inspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks' quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders. (c) 2022 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available