A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm

An implicit reserve constraint unit commitment (IRCUC) model is presented in this paper. Different from the traditional unit commitment (UC) model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical method is applied to evaluate the reliability of UC solutions and to estimate the outage loss. The stochastic failures of generating units and uncertainties of load demands are considered while assessing the reliability. The artificial fish swarm algorithm (AFSA) is employed to solve this proposed model. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated from 10 to 100 units system, and the testing results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) in terms of total production cost and computational time. The simulation results show that the proposed method is capable of obtaining higher quality solutions.

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Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm

The Scientific World JOURNAL ◽

10.1155/2014/859239 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 29

Author(s):

Wei Han ◽

Hong-Hua Wang ◽

Ling Chen

Keyword(s):

Power Systems ◽

Collective Behavior ◽

Parameters Identification ◽

Computational Time ◽

Photovoltaic Module ◽

Artificial Fish Swarm Algorithm ◽

Pv Module ◽

Simulation Evaluation ◽

Artificial Fish Swarm ◽

Swarm Algorithm

A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision.

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Gray-Scale Image Matching Technology Based on Artificial Fish Swarm Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1295 ◽

2013 ◽

Vol 411-414 ◽

pp. 1295-1298 ◽

Cited By ~ 1

Author(s):

Jun Lin Zhu ◽

Zu Lin Wang ◽

Hui Liu

Keyword(s):

Particle Swarm Optimization ◽

Image Matching ◽

Particle Swarm ◽

Pso Algorithm ◽

Matching Method ◽

Test Environment ◽

Swarm Optimization ◽

Artificial Fish Swarm Algorithm ◽

Artificial Fish Swarm ◽

Swarm Algorithm

Aiming at the problem of slow speed in image matching,anti-interference difference and relatively poor ability to resist deformation,proposed a fast image matching method based on artificial fish swarm algorithm (AFSA). In the same test environment,Compared with the image matching method based on particle swarm optimization (PSO) algorithm and found,the method is superior to image matching method based on particle swarm optimization,in matching speed and noise resistance ability and deformation resistance ability has a marked improvement.

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

International Journal of Distributed Sensor Networks ◽

10.1177/1550147721992298 ◽

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.

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