An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount

Wang, Dongxing; Ban, Xiaojuan; Ji, Linhong; Guan, Xinyu; Liu, Kang; Xu, Qian

doi:10.1155/2020/7986982

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…The ASGS-CWOA was proposed in 2018 by Wang et al in [25], and it is an efficient variant of the wolf pack optimization algorithm in the optimization field aimed at solving application problems efficiently. Its key improvements are as follows.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive, Discrete Space Oriented Wolf Pack Optimization Algorithm for a Movable Wireless Sensor Network

Wang

Ban

et al. 2019

Sensors

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Recently, many related algorithms have been proposed to find an efficient wireless sensor network with good sustainability, a stable connection, and a high covering rate. To further improve the coverage rate of movable wireless sensor networks under the condition of guaranteed connectivity, this paper proposes an adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network (DSO-WPOA). Firstly, a strategy of adaptive expansion based on a minimum overlapping full-coverage model is designed to achieve minimum overlap and no-gap coverage for the monitoring area. Moreover, the adaptive shrinking grid search wolf pack optimization algorithm (ASGS-CWOA) is improved to optimize the movable wireless sensor network, which is a discrete space oriented problem. This improvement includes the usage of a target–node probability matrix and the design of an adaptive step size method, both of which work together to enhance the convergence speed and global optimization ability of the algorithm. Theoretical research and experimental results indicate that compared with the coverage algorithm based on particle swarm optimization (PSO-WSN) and classical virtual force algorithm, the newly proposed algorithm possesses the best coverage rate, better stability, acceptable performance in terms of time, advantages in energy savings, and no gaps.

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Section: Related Workmentioning

confidence: 99%

An Adaptive, Discrete Space Oriented Wolf Pack Optimization Algorithm for a Movable Wireless Sensor Network

Wang

Ban

et al. 2019

Sensors

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“…Literature [20] aims to improve the optimization quality, stability and convergence speed of the wolf pack algorithm. All wolves in the pack are allowed to compete as the leading wolf to improve the probability of finding the global optimal solution, thus enhancing the search ability; In order to promote information exchange between artificial wolves, improve wolves' grasp of global information and enhance their exploration ability, reference [21] introduces drift operators and wave operators into wolves' reconnaissance behavior and summation behavior.…”

Section: Introductionmentioning

confidence: 99%

A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm

Zhou

et al. 2022

Applied Sciences

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With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.

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Research on Production Scheduling Optimization of Flexible Job Shop Production with Buffer Capacity Limitation Based on the Improved Gene Expression Programming Algorithm

Chang,

Shi,

Han

et al. 2023

Int. J. Precis. Eng. Manuf.

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An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount

Cited by 6 publications

References 29 publications

An Adaptive, Discrete Space Oriented Wolf Pack Optimization Algorithm for a Movable Wireless Sensor Network

An Adaptive, Discrete Space Oriented Wolf Pack Optimization Algorithm for a Movable Wireless Sensor Network

A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm

Research on Production Scheduling Optimization of Flexible Job Shop Production with Buffer Capacity Limitation Based on the Improved Gene Expression Programming Algorithm

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