A hybridization of growth optimizer and improved arithmetic optimization algorithm and its application to discrete structural optimization

[Display omitted] •An improved hybrid growth optimizer (IHGO) algorithm is proposed.•IHGO combines the growth optimizer (GO) and an improved metaheuristic called IAOA.•IHGO is proposed for structural optimization with discrete design variables.•This is the first time to apply growth optimizer (GO) f...

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Bibliographic Details
Published in:	Computers & structures Vol. 303; p. 107496
Main Authors:	Kaveh, Ali, Biabani Hamedani, Kiarash
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.10.2024
Subjects:	Discrete structural optimization Growth optimizer Improved arithmetic optimization algorithm Improved growth optimizer Metaheuristic algorithms Skeletal structures Improved growth optimizer Skeletal structures Discrete structural optimization Improved arithmetic optimization algorithm Metaheuristic algorithms Growth optimizer
ISSN:	0045-7949
Online Access:	Get full text
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Summary:	[Display omitted] •An improved hybrid growth optimizer (IHGO) algorithm is proposed.•IHGO combines the growth optimizer (GO) and an improved metaheuristic called IAOA.•IHGO is proposed for structural optimization with discrete design variables.•This is the first time to apply growth optimizer (GO) for structural optimization.•IHGO offers results comparable or superior to many state-of-the-art metaheuristics. This paper proposes an improved hybrid growth optimizer (IHGO) to solve discrete structural optimization problems. The growth optimizer (GO) is a recent metaheuristic that has been successfully used to solve numerical and real-world optimization problems. However, it has been found that GO faces challenges with parameter tuning and operator refinement. We also noticed that the formulation of GO has some drawbacks, which may cause degradation in optimization performance. Compared to the original GO, four improvements are introduced in IHGO. First, the learning phase of GO is improved to avoid useless search and reinforce exploration. To do this, the exploration phase of an improved metaheuristic called IAOA is incorporated into the learning phase of GO. Second, the replacement strategy of GO is modified to prevent the loss of the best-so-far solution. Third, the hierarchical structure of GO is modified. Fourth, some adjustments are made to the reflection phase of GO to promote the exploitation of promising regions. To demonstrate the performance of the proposed IHGO, four discrete optimization problems of skeletal structures are provided. The results are compared with those of the original GO and some other metaheuristics in the literature. The source codes of IHGO are available at https://github.com/K-BiabaniHamedani/Improved-Hybrid-Growth-Optimizer.
ISSN:	0045-7949
DOI:	10.1016/j.compstruc.2024.107496