scholarly journals Development of an Artificial Fish Swarm Algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background

2020 ◽  
Vol 16 (5) ◽  
pp. 935-946
Author(s):  
Sheng Bai ◽  
Feng Bao ◽  
Fengzhi Zhao ◽  
Miaomiao Liu
Keyword(s):  
Sensor Networks ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

scholarly journals A novel topology optimization of coverage-oriented strategy for wireless sensor networks

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772199229
Author(s):  
Shuxin Wang ◽  
Hairong You ◽  
Yinggao Yue ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Topology Optimization ◽  
Sensor Networks ◽  
Sensor Node ◽  
Particle Swarm Optimization Algorithm ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

scholarly journals A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Zhou ◽  
Guohong Qi ◽  
Changzheng Liu
Keyword(s):  
Water Quality ◽  
Sensor Networks ◽  
Water Quality Monitoring ◽  
Coverage Rate ◽  
Quality Monitoring ◽  
Target Coverage ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
3D Coverage

In recent years, the increasingly severe water pollution problem encouraged researchers to optimize water quality monitoring sensor networks (WQMSNs) by creating new underwater sensor coverage algorithms. Since the sensor is limited by the monitoring range and the number of targets, optimizing the 3D target coverage of heterogeneous multisensors is essential to maximize the 3D target coverage rate of the monitored waters. To enhance the target coverage rate, the target allocation needs to be searched in all possible combinations. To optimize the 3D coverage of underwater targets, this research proposes a chaotic parallel artificial fish swarm algorithm (CPAFSA). CPAFSA uses chaotic selection to initialize parameters and integrates the global search capabilities of parallel operators. It also applies the elite selection which effectively avoiding local optimization and solving the problem of 3D target coverage. Ultimately, CPAFSA is compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results of the simulation experiment demonstrated the excellent performance of CPAFSA in achieving underwater 3D target coverage.

PERFORMANCE EVALUATION OF CULTURAL ARTIFICIAL BEE COLONY AND CULTURAL ARTIFICIAL FISH SWARM ALGORITHM

2019 ◽  
Vol 6 (4) ◽  
pp. 43
Author(s):  
HADIR ADEBIYI BUSAYO ◽  
TIJANI SALAWUDEEN AHMED ◽  
FOLASHADE O. ADEBIYI RISIKAT ◽  
◽  
◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Artificial Bee Colony ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm
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