A qualitative systematic review of metaheuristics applied to tension/compression spring design problem: Current situation, recommendations, and research direction

Tzanetos, Alexandros; Blondin, Maude Josée

doi:10.1016/j.engappai.2022.105521

Cited by 33 publications

(12 citation statements)

References 94 publications

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“…The first engineering optimization problem we chose was: Tension/compression spring design problem (TCSD) 65 , TCSD is a continuously constrained problem such that the volume V of the coil spring is minimized under constant tension/compression load. The second engineering optimization problem we selected is Constrained truss optimization problem 66 .…”

Section: Methodsmentioning

confidence: 99%

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Meng,

Cong,

et al. 2024

Sci Rep

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With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

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Section: Methodsmentioning

confidence: 99%

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Meng,

Cong,

et al. 2024

Sci Rep

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“…The Tension/Compression Spring Design Problem is a pivotal optimization challenge in mechanical engineering, aiming to minimize the spring's weight while adhering to various physical and geometric constraints. [97] This entails optimizing key design variables such as wire diameter, mean coil diameter, and the number of active coils, which inŕuence the spring's mechanical properties. The choice of material also plays a critical role in determining the spring's strength and resilience, with manufacturing considerations impacting the feasibility and cost-effectiveness of the design.…”

Section: Tension/compression Spring Design Problemmentioning

confidence: 99%

Hybrid Four Vector Intelligent Metaheuristic andDE for Solving Complex and Engineering DesignOptimization Problems

fakhouri,

Ishtaiwi,

Makhadmeh

et al. 2024

Preprint

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Metaheuristic algorithms play a pivotal role in addressing complex and nonlinear optimization challenges. However, traditional optimizers often struggle to locate the global optimum in intricate problem spaces, necessitating the development of hybrid methodologies. This paper introduces FVIMDE, a cutting-edge hybrid optimization algorithm that amalgamates the innovative Four Vector Intelligent Metaheuristic (FVIM) with the proven robustness of Differential Evolution (DE). Designed to adeptly maneuver through the complex terrains of various optimization and engineering design problems, FVIMDE is tested and evaluated over three well-known benchmark suites—CEC2017, CEC2022, and a specially set of 50 benchmark functions. statistacel tests has been calculated including mean, standard deviation and the wilcoxon sum rank test. Further FVIMDE has been compared with state-of-art optimizers. Subsequent applications of FVIMDE to five intricate structural engineering challenges further validate its effectiveness. Comparative analyses against several leading optimization algorithms highlight the superior adaptability and robustness of FVIMDE, showcasing its exceptional performance and significant improvements over traditional methods in diverse optimization environments.

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“…Designing coil springs with the ideal tension and compression is a classic engineering optimization challenge [81]. Fig.…”

Section: Tension/compression Spring Designmentioning

confidence: 99%

E3H2O-LE-TDR Algorithm for Optimizing Solar PV Cell Models and Solving Real-World Engineering Problems

Akl,

Saafan,

Haikal

et al. 2023

Preprint

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Engineering and science have increasingly used metaheuristic algorithms to solve actual optimization problems. One of the challenging problems is the proper selection of the parameters of photovoltaic cells since these cells are a great source of clean energy. For such difficult situations, the Harris Hawks Optimization method can be a useful tool. However, HHO is susceptible to the local minimum. This study suggests a novel optimizer called Enhanced Exploration and Exploitation Harris Hawks Optimization using Logarithms, Exponentials, and Travelled Distance Rate (E3H2O-LE-TDR) algorithm, which is a modified version of HHO. The algorithm proposed in this study emphasizes the utilization of random location-based habitats during the exploration phase and the implementation of strategies 1, 3, and 4 during the exploitation phase. In the proposed algorithm, Harris hawks in the wild will change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. Therefore, the cons of the original HHO have been solved. Furthermore, E3H2O-LE-TDR was also tested across multiple benchmarks to prove its credibility and efficacy. The approach was tested on the CEC2017, CEC2019, CEC2020, and 27 other benchmark functions with different modalities. The suggested approach is also evaluated on six traditional real-world engineering situations. E3H2O-LE-TDR is compared to state-of-the-art algorithms, as well as other modifications of HHO. The numerical results show that the proposed algorithm outperforms all its competitors, which is visually proven using different convergence curves. Furthermore, the results of the mean Friedman rank statistical test proved the superiority of the proposed algorithm. The results for the single and double diode pv cell model, E3H2O-LE-TDR presented the best performance as indicated by the absolute error in both the current and power values for different operating conditions.

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A qualitative systematic review of metaheuristics applied to tension/compression spring design problem: Current situation, recommendations, and research direction

Cited by 33 publications

References 94 publications

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Hybrid Four Vector Intelligent Metaheuristic andDE for Solving Complex and Engineering DesignOptimization Problems

E3H2O-LE-TDR Algorithm for Optimizing Solar PV Cell Models and Solving Real-World Engineering Problems

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