Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Panagant, Natee; Kumar, Sumit; Tejani, Ghanshyam G.; Pholdee, Nantiwat; Bureerat, Sujin

doi:10.1016/j.mex.2023.102181

Cited by 20 publications

(7 citation statements)

References 49 publications

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“…The results of the algorithm iterations are shown in Figure 3. Based on the computational results obtained from Figure 3, the proposed SHAMODE-IWOA algorithm is compared with other optimization algorithms, including SHAMODE-WO [25][29], Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) [7], Whale Optimization Algorithm (WOA) [34][35], African Vulture Optimization Algorithm (AVOA) [36], Gorilla Troop Optimization Algorithm (GTO) [37], Beetle Optimization Algorithm (DBO) [38], and Snake Optimization Algorithm (SO) [39]. The test results of the algorithms are presented in Table 2, where the first column represents the average values and the second column represents the standard deviations.…”

Section: A Test Results Of Shamode-iwoa Algorithmmentioning

confidence: 99%

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A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

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In the process of daily product development and design, optimizing the shape and external dimensions of the vehicle chassis truss structure while considering both weight and reliability indicators often involves interdisciplinary collaboration and inefficient communication, leading to repetitive mechanical labor and low efficiency. This is widely regarded as a difficult and challenging task. In order to improve the design quality of the chassis truss structure and enhance work efficiency, this paper proposes a novel lightweight multi-objective optimization design system for vehicle chassis trusses based on the ANFIS-SHAMODE-IWOA model. Firstly, an adaptive spiral search strategy is introduced based on the multi-objective hybrid metaheuristic algorithm (SHAMODE) which is based on successful history. Then, a new SHAMODE-IWOA algorithm is proposed. In order to estimate the reliability level of the chassis truss structure under different design parameter combinations, a new ANFIS-SHAMODE-IWOA model is constructed by using the proposed SHAMODE-IWOA algorithm to learn the ANFIS model. Finally, in order to obtain the optimal design parameter combination, multi-objective optimization based on minimum design quality and optimal reliability measurement functions is studied using the SHAMODE-IWOA algorithm. Experimental results show that SHAMODE-IWOA has leading global optimization capability on CEC2017 test set benchmark functions. Compared with other intelligent models, the ANFIS-SHAMODE-IWOA model has better performance in reliability coefficient estimation. At the same time, the SHAMODE-IWOA algorithm obtains a more optimal design parameter combination for the chassis truss, with improvements of 9.5%, 12.5%, and 15% in Mass, Bending, and Torsion indicators, respectively. Finally, the proposed ANFIS-SHAMODE-IWOA multi-objective optimization design system, as a novel intelligent model, can be used to evaluate the reliability of chassis truss structures, improve development and design efficiency, and obtain the best design parameter combination, which is beneficial to improving the level of green intelligent manufacturing design.

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Section: A Test Results Of Shamode-iwoa Algorithmmentioning

confidence: 99%

“…Firstly, in response to the inadequate performance of the original algorithm, we have introduced an adaptive mechanism based on the SHAMODE-WO algorithm [25], and proposed the SHAMODE-IWOA algorithm. The improvement and effectiveness of this algorithm have been verified through benchmark function experiments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

Minqing,

Hongxi,

Weihuan

et al. 2024

IEEE Access

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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 . Three-bar truss is a common structural form in engineering, which is widely used in bridges, buildings, mechanical equipment and other fields.…”

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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“…The details of the problem are presented in Appendix A.5. The objectives of the RWMOP5 problem are to reduce truss mass, minimize compliance, optimize the first natural frequency, and decrease the maximum buckling factor [39]. The details of the problem are presented in Appendix A.5.…”

Section: Water Resources Management (Rwmop2)mentioning

confidence: 99%

“…The PF results of the proposed MaOSCA optimizer and the NSGA-III, MOEADDE, MaOPSO, and MaOJAYA algorithms can be seen in this figure, with the MaOSCA obtaining better convergence and spread than the NSGA-III, MOEADDE, MaOPSO, and MaOJAYA algorithms. The objectives of the RWMOP5 problem are to reduce truss mass, minimize compliance, optimize the first natural frequency, and decrease the maximum buckling factor [39]. The details of the problem are presented in Appendix A.5.…”

Section: Water Resources Management (Rwmop2)mentioning

confidence: 99%

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

Narayanan¹,

Ganesh

Čep

et al. 2023

Mathematics

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In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.

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Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Cited by 20 publications

References 49 publications

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

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

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

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