ArticleScientific reports2024
Application of a novel metaheuristic algorithm inspired by connected banking system in truss size and layout optimum design problems and optimization problems.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Multi-Strategy Improved Connected Banking System Optimizer for Numerical Optimization and Real Problems.Biomimetics (Basel, Switzerland) · 2026Article
- An enhanced connected banking system optimizer with multiple strategies for numerical optimization problems.Scientific reports · 2026Article
- An enhanced connected banking system optimizer incorporating triple mechanism for solving global optimization problems.Scientific reports · 2026Article
- DRIME: A Distributed Data-Guided RIME Algorithm for Numerical Optimization Problems.Biomimetics (Basel, Switzerland) · 2025Article
- A Novel Black Widow Optimization Algorithm Based on Lagrange Interpolation Operator for ResNet18.Biomimetics (Basel, Switzerland) · 2025Article
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Abstract
Optimum design of truss structures can be a challenging and difficult field of study specially if an optimum design problem is comprised of continuous and discrete decision variables such as in truss size and layout optimization problems. Usually metaheuristic approaches are selected to solve these kind of problems and there are many different metaheuristic methods that have been used in dealing with truss optimization problems, which provide optimum solutions for those problems. However, among these various proposed metaheuristics, it is very seldom to face and find a method that is based on technologies such as computer networks. This paper aims to utilize a metaheuristic algorithm which is based on some principals of connected banking systems, which is called the Connected Banking System Optimizer(CBSO) algorithm. The performance of the CBSO is tested against three benchmark truss size/layout optimum design problems namely 15, 18 and 25 bar truss structures. Besides the truss size/layout problems, CEC-BC-2017 test functions with ten dimensions and also three other constrained optimum design problems are also investigated to examine the performance of the CBSO in dealing with these standard unconstrained and constrained test functions. The results of the CBSO optimum designs are presented and compared with some of the available studies in the literature. Statistical analyses are done to have a fair evaluation of the CBSO algorithm's efficiency and its ability in dealing with the benchmark truss size/layout optimization problems. The CBSO outperforms its rivals and presents lighter weight truss structures. There is no need for configuration of extra parameters in the CBSO algorithm when solving the examined benchmark truss structures, which makes it a robust parameter-free optimization algorithm.
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