Optimal load distribution in power plants based on dynamic inertia weight particle swarm algorithm

2016 ◽  
Author(s):  
Daogang Peng ◽  
Hanmei Zhao ◽  
Li Huang ◽  
Liqun Gu
Keyword(s):  
Load Distribution ◽  
Power Plants ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Inertia Weight ◽  
Optimal Load ◽  
Swarm Algorithm ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia ◽  
Optimal Load Distribution

The Improvement of Hybrid Particle Swarm Algorithm and its Application

2011 ◽  
Vol 268-270 ◽  
pp. 798-802 ◽  
Author(s):  
Shu Rong Zou ◽  
Peng Xin Ding ◽  
Hong Wei Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Routing Problem ◽  
Multi Objective ◽  
Inertia Weight ◽  
Swarm Algorithm ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia

Hybrid multi-objective particle swarm algorithm is applied to vehicle routing problem and achieved good results, this paper based on the previous work, dynamic inertia weight is added to the particle swarm algorithm with intelligence factors, it improved the global search ability and the capacity of local convergence of the particle swarm algorithm; and the idea of immunity is introduced in the algorithm ,which makes the hybrid multi-objective particle swarm algorithm can effectively discard the repeated solutions in solving vehicle routing problems, this operation can improve the efficiency of the algorithm, and obtain better results under the same conditions.

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Power Plants ◽  
Optimization Problem ◽  
Thermal Power ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Load ◽  
Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1049-1052
Author(s):  
Bao Ru Han ◽  
Jing Bing Li
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Algorithm ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Bp Algorithm ◽  
Particle Swarm Algorithm ◽  
Inertia Weight ◽  
High Convergence Rate ◽  
Swarm Algorithm ◽  
Circuit Fault Diagnosis

Base on improved particle swarm algorithm, this paper proposes a linear decreasing inertia weight particle swarm algorithm and error back propagation algorithm based on hybrid algorithm combining. The linear decreasing inertia weight particle swarm algorithm and momentum-adaptive learning rate BP algorithm interchangeably adjust the network weights, so that the two algorithms are complementary. It gives full play to the PSO's global optimization ability and the BP algorithm local search advantage, to overcome the slow convergence speed and easily falling into local weight problems. Simulation results show that this diagnostic method can be used for tolerance analog circuit fault diagnosis, with a high convergence rate and diagnostic accuracy.

Alignment of Integrated Photonic Devices Based on Improved Particle Swarm Algorithm

2013 ◽  
Vol 328 ◽  
pp. 817-822
Author(s):  
Meng Peng Zhu ◽  
Yu Zheng ◽  
Ji An Duan
Keyword(s):  
Coupling Efficiency ◽  
Particle Swarm ◽  
Photonic Devices ◽  
Particle Swarm Algorithm ◽  
Search Process ◽  
Alignment Algorithm ◽  
Selection Mechanism ◽  
Inertia Weight ◽  
Automatic Alignment ◽  
Swarm Algorithm

A novel automatic alignment algorithm of integrated photonic devices based on improved particle swarm algorithm is proposed. The combination of dynamically changing inertia weight and selection mechanism is used in the standard particle swarm algorithm to overcome the premature phenomenon in alignment search process, which improves the global search ability and convergence speed compared to particle swarm algorithm. The method used in engineering can significantly improve the alignment and coupling efficiency of photonic devices.

The Application of Quantum-Behaved Particle Swarm Algorithm in Intelligent Test Paper Generation

2013 ◽  
Vol 694-697 ◽  
pp. 2378-2382 ◽  
Author(s):  
Xin Ran Li
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Aggregation ◽  
Particle Swarm Algorithm ◽  
Test Paper ◽  
Inertia Weight ◽  
Local Optimal Solution ◽  
Swarm Algorithm ◽  
Low Efficiency

Aiming at solving the low efficiency and low quality of the existing test paper generation algorithm, this paper proposes an improved particle swarm algorithm, a new algorithm for intelligent test paper generation. Firstly, the paper conducts mathematically modeling based on item response theory. Secondly, in the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. Finally, simulation results show that compared with the quantum-behaved particle swarm algorithm, the proposed algorithm has better performance in success rate and composing efficiency.

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