A new hybrid particle swarm and simulated annealing stochastic optimization method

2017 ◽  
Vol 60 ◽  
pp. 634-654 ◽  
Author(s):  
F. Javidrad ◽  
M. Nazari
Keyword(s):  
Simulated Annealing ◽  
Stochastic Optimization ◽  
Particle Swarm ◽  
Optimization Method ◽  
Hybrid Particle

Distribution System Restoration Based on Hybrid Particle Swarm

2013 ◽  
Vol 732-733 ◽  
pp. 662-668
Author(s):  
Li Zhi Tang ◽  
Jun Du ◽  
Kun Peng Xu ◽  
Xue Qing Qi
Keyword(s):  
Stochastic Optimization ◽  
Distribution System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Hybrid Particle ◽  
Matrix Operations ◽  
Global Optimal ◽  
Loop Matrix ◽  
Network Fault

By the heuristic algorithm and particle swarm optimization algorithm combining hybrid particle swarm algorithm proposed combination of heuristic search and stochastic optimization,stochastic optimization process using a spanning tree and the loop matrix operations combined to ensure the system topology constraints to improve the efficiency of solution. The analysis shows that the proposed method calculation speed,easy to converge to the global optimal solution. It can effectively solve the problem of distribution network fault recovery.

Hybrid Particle Swarm Optimization Algorithm Merging Simulated Annealing and Mountain-Climb Searching

2014 ◽  
Vol 651-653 ◽  
pp. 2159-2163
Author(s):  
Jia Xing You ◽  
Ji Li Chen ◽  
Ming Gang Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Local Search ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

To solve the problem of standard particle swarm optimization (PSO) easy turn to premature convergence and poor ability in local search, this paper present a hybrid particle swarm optimization algorithm merging simulated annealing (SA) and mountain-climb. During the running time, the algorithm use the pso to find the global optimal position quickly, take advantage of the Gaussian mutation and mountain-climb strategy to enhance local search ability, and combine with SA to strengthen the population diversity to enable particles to escape from local minima. Test results on several typical test functions show that this new algorithm has a significant improve in searching ability and effectively overcome the premature convergence problem.

A hybrid particle swarm–Nelder–Mead optimization method for crack detection in cantilever beams

2012 ◽  
Vol 12 (8) ◽  
pp. 2217-2226 ◽  
Author(s):  
M.T. Vakil Baghmisheh ◽  
Mansour Peimani ◽  
Morteza Homayoun Sadeghi ◽  
Mir Mohammad Ettefagh ◽  
Aysa Fakheri Tabrizi
Keyword(s):  
Crack Detection ◽  
Particle Swarm ◽  
Optimization Method ◽  
Cantilever Beams ◽  
Hybrid Particle

Multi-Agent Collaborative Research Based on an Optimization Method about Multiple Evaluation Function with Hybrid Particle Swarm with Constraints

2013 ◽  
Vol 380-384 ◽  
pp. 1510-1514
Author(s):  
Zai Jun Wang ◽  
Bao Fu Fang ◽  
Wei Liu
Keyword(s):  
Heterogeneous Agents ◽  
Particle Swarm ◽  
Optimization Method ◽  
Simulation System ◽  
Evaluation Function ◽  
Hybrid Particle ◽  
Evaluation Functions ◽  
Agent Collaboration ◽  
Multi Agent ◽  
Multiple Conditions

This paper presents an optimization method about multiple evaluation function with hybrid particle swarm with constraints on the base of an optimization algorithm of hybrid particle swarm, which is used to solve the problem of multi-agent collaboration in the rescue simulation system. The optimization process uses a variety of evaluation function and also calculates the constraint relationship among the evaluation functions on the particle iterative process in order to obtain multi-objective optimization results that meet multiple conditions. The method is suitable for the collaborative problem among a variety of heterogeneous agents, which presents the collaboration among heterogeneous agents through constraints. The method proves to be effective in the practical application of the rescue simulation system.

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