Position Deployment Optimization of Maneuvering Conventional Missile Based on Improved Whale Optimization Algorithm

Li, Yaxiong; Zhang, Xinwei; Zhao, Jing; Yang, Xue‐Xia; Xi, Mingyuan

doi:10.1155/2022/4373879

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…It improves the computational efficiency and calculates the overall accessibility compared to traditional station planning methods. Next, we will study the deployment optimization model more deeply from the aspects of cooperation target point measurement accuracy and station transfer accuracy and explore more complex location configuration modes to solve the booth optimization problem [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Yan

Liu

et al. 2023

Entropy

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In the large-scale measurement field, deployment planning usually uses the Monte Carlo method for simulation analysis, which has high algorithm complexity. At the same time, traditional station planning is inefficient and unable to calculate overall accessibility due to the occlusion of tooling. To solve this problem, in this study, we first introduced a Poisson-like randomness strategy and an enhanced randomness strategy to improve the remora optimization algorithm (ROA), i.e., the PROA. Simultaneously, its convergence speed and robustness were verified in different dimensions using the CEC benchmark function. The convergence speed of 67.5–74% of the results is better than the ROA, and the robustness results of 66.67–75% are better than those of the ROA. Second, a deployment model was established for the large-scale measurement field to obtain the maximum visible area of the target to be measured. Finally, the PROA was used as the optimizer to solve optimal deployment planning; the performance of the PROA was verified by simulation analysis. In the case of six stations, the maximum visible area of the PROA reaches 83.02%, which is 18.07% higher than that of the ROA. Compared with the traditional method, this model shortens the deployment time and calculates the overall accessibility, which is of practical significance for improving assembly efficiency in large-size measurement field environments.

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Section: Discussionmentioning

confidence: 99%

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Yan

Liu

et al. 2023

Entropy

View full text Add to dashboard Cite

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“…The experimental results prove that the EWOA-SVM model is superior to other existing methods in terms of fast convergence speed and high prediction accuracy [59]. In order to reasonably allocate mobile conventional missile battle positions, Li et al optimized the original WOA using the convergence factor and the diversity mutation operation, which provides a reference for the deployment mode of mobile missile positions [60].…”

Section: Algorithm Categorymentioning

confidence: 96%

A Nonlinear Adaptive Weight-Based Mutated Whale Optimization Algorithm and Its Application for Solving Engineering Problems

Wang,

Li,

et al. 2024

IEEE Access

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Whale optimization algorithm (WOA) is a swarm-based optimization algorithm with exceptional performance and significant originality. In this study, a novel variant of WOA called nonlinear adaptive weight-based mutated WOA (NAWMWOA) is proposed to overcome the shortcomings of original WOA such as easily falling into local optimum and slow convergence speed. In detail, the proposed NAWMWOA includes three novel strategies as comparing with original WOA. Firstly, a nonlinear convergence factor is embedded into the original WOA to balance exploration and exploitation ability. The second improvement is an adaptive weight strategy, which can enhance the exploratory searching trends and improve the solution accuracy. Moreover, the thirdly proposed hybrid mutation strategy has the function of increasing the accuracy and jumping out of the local optimum. The combination of the three strategies significantly improve convergence efficiency and search accuracy of original WOA. To verify the remarkable performance of the proposed NAWMWOA, a series of illustrious WOA variants and state-of-the-art intelligent algorithms is compared with the NAWMWOA on 37 benchmark functions and three typical engineering problems. The details of experimental and statistics results illustrate that the presented NAWMWOA has higher convergence efficiency and better solution accuracy. As a conclusion, the proposed NAWMWOA is a competitive and outstanding algorithm.

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Position Deployment Optimization of Maneuvering Conventional Missile Based on Improved Whale Optimization Algorithm

Cited by 2 publications

References 14 publications

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

A Nonlinear Adaptive Weight-Based Mutated Whale Optimization Algorithm and Its Application for Solving Engineering Problems

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