An improved whale optimization algorithm with armed force program and strategic adjustment

Jiang, Ruiye; Yang, Ming; Wang, Songyan; Chao, Tao

doi:10.1016/j.apm.2020.01.002

Cited by 50 publications

(14 citation statements)

References 34 publications

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“…A comparison of the optimal solution and statistical results are presented in Tables 9 and 10, respectively. It is observed that the optimal solution by TPSA is superior to PSA, CPSO [47], IGMM [48], TEO [49], IGWO [50], SFOA [51], CS-BSA [52], and WAROA [53] and is equivalent to the results of AEO [46]. However, the mean and standard deviation of TPSA are better than those of AEO.…”

Section: Welded-beam Design Problemmentioning

confidence: 90%

“…where .05 ≤ x 1 ≤ 2.00, 0.25 ≤ x 2 ≤ 1.30, and 2.00 ≤ x 3 ≤ 15.00. e population size was set to 50; the maximum number of iterations was 1,000; the step-size scaling 11 and 12, respectively. Tables 11 and 12 show that the optimal solution of TPSA is superior to PSA, HMPA [4], AEO [46], CPSO [47], IGWO [50], SFOA [51], and PO [54] and is equivalent to the results of the TEO [49] and WAROA [53]. However, the worst and average values of TPSA are better than those of WAROA.…”

Section: 3mentioning

confidence: 91%

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Porcellio scaber Algorithm with t-Distributed Elite Mutation for Global Optimization

Zhang

Yang

2022

Scientific Programming

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As the convergence accuracy of the Porcellio scaber algorithm (PSA) is low, this study proposes an improved algorithm based on the t-distribution elite mutation mechanism. First, the improved algorithm applies a t-distribution mutation to each dimension of the optimal solution of each generation. Using the dominant information of the optimal solution and t-distribution characteristics, the result of the mutation is then employed as the updated location of the selected porcellio; thus, the algorithm enhances the ability to jump out of local extreme values and improves the convergence speed. Second, the updated iterative rule of PSA may lead to the loss of information of the elite porcellio in the last generation. To solve this problem, the judgment mechanisms of the current and previous optimal solutions are included in the algorithm process. Finally, dynamic self-adaptive improvement is applied to the weight allocation parameters of PSA. Simulation results on 24 benchmark functions show that the improved algorithm has significant advantages in convergence accuracy, convergence speed, and stability when compared with basic PSA, PSO, GSA, and FPA. This indicates that the improved algorithm has certain advantages in terms of optimization. An optimal solution with good practicability is obtained by solving three practical engineering problems: three-bar truss, welded beam, and tension/compression-spring design.

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Section: Welded-beam Design Problemmentioning

confidence: 90%

Section: 3mentioning

confidence: 91%

Porcellio scaber Algorithm with t-Distributed Elite Mutation for Global Optimization

Zhang

Yang

2022

Scientific Programming

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“…To verify the optimization performance of the PRN-SSA for basic test functions, the basic sparrow search algorithm (SSA) [40], improved sparrow search algorithm (GGSC-SSA) [41], particle swarm optimization algorithm (PSO) [42], grey wolf optimizer algorithm (GWO) [43], whale optimization algorithm (WOA) [44], and harris hawk optimization algorithm (HHO) [45] are compared with PRN-SSA. On the one hand, it aims to judge whether the improvement of this research can exceed the performance of other SSA and its variants, and on the other hand, it can reflect the optimization ability of the improved sparrow algorithm compared with other precision algorithms.…”

Section: Comparison Algorithms and Parameter Settingsmentioning

confidence: 99%

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

et al. 2022

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Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.

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“…A numerical example for minmax regret EC 39,3 (WOA) is a novel meta-heuristic optimization algorithm proposed by Mirjalili and Lewis in 2016, inspired by the unique "bubble-net" hunting strategy of humpback whales (Mirjalili and Lewis, 2016). The WOA is chosen because it has been proven to perform better in terms of convergence speed and accuracy over many other meta-heuristic optimizers such as Particle Swarm Optimization, Ant Colony Optimization, Differential Evolution and Evolution Strategy (Jiang et al, 2020). However, the search process of the WOA has the disadvantage of a low exploitation speed and accuracy.…”

Section: Robust Optimization Of Line Balancing Problemmentioning

confidence: 99%

Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

Jin

2021

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PurposeDue to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability.Design/methodology/approachA generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated.FindingsTheory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy.Originality/valueFor the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional Whale Optimization Algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

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An improved whale optimization algorithm with armed force program and strategic adjustment

Cited by 50 publications

References 34 publications

Porcellio scaber Algorithm with t-Distributed Elite Mutation for Global Optimization

Porcellio scaber Algorithm with t-Distributed Elite Mutation for Global Optimization

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

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