Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

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...

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Vydáno v:Computer methods in applied mechanics and engineering Ročník 392; s. 114616
Hlavní autoři: Zamani, Hoda, Nadimi-Shahraki, Mohammad H., Gandomi, Amir H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 15.03.2022
Elsevier BV
Témata:
Algorithms
Applied mechanics and engineering problems
Bio-inspired algorithm
Diving
Harmonic oscillators
Heuristic methods
Mechanical engineering
Metaheuristic algorithm
Optimization
Optimization algorithm
Quantum computing
Search methods
Statistical analysis
Swarm intelligence algorithm
Swarm intelligence algorithm
Applied mechanics and engineering problems
Optimization algorithm
Quantum computing
Metaheuristic algorithm
Bio-inspired algorithm
ISSN:0045-7825
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Shrnutí: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. •Proposing a novel, bio-inspired algorithm named starling murmuration optimizer (SMO).•Introducing a diving strategy and a quantum random dives operator for exploration.•Introducing a whirling strategy and a cohesion operator for exploitation.•Introducing a strategy using quantum harmonic oscillator to enhance diversity.•SMO is superior to other tested algorithms on benchmarks and engineering problems.
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ISSN:0045-7825
DOI:10.1016/j.cma.2022.114616