scholarly journals Sequence and Direction Planning of Multiobjective Attack in Virtual Navigation Based on Variable Granularity Optimization Method

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chengwei Ruan ◽  
Lei Yu ◽  
Zhongliang Zhou ◽  
Jinfu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Solution Space ◽  
Optimization Method ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Proposed Model ◽  
High Level ◽  
Variable Granularity

In multiobjective air attacking tasks, it is essential to find the optimal combat preplanning for the attacking flight. This paper solves the planning problem by decomposing it into two subproblems: attacking sequence planning and attacking direction planning. According to this decomposition, we propose the VGHPSO (Variable Granularity Hybrid Particle Swarm Optimization) method. VGHPSO employs the Particle Swarm Optimization, a metaheuristic global optimization method, to solve the planning problem in multiple granularities, including optimizing the high-level attacking sequence and optimizing the low-level attacking directions for engagements. Furthermore, VGHPSO utilizes infeasible individuals in the swarm in order to enhance the capability of searching in the boundary of the feasible solution space. Simulation results show that the proposed model is feasible in the combat, and the VGHPSO method is efficient to complete the preplanning process.

A Hybrid Particle Swarm Optimization Method for Traveling Salesman Problem

2017 ◽  
Vol 8 (3) ◽  
pp. 53-65 ◽  
Author(s):  
Yong Wang ◽  
Ning Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Initial Approximation ◽  
Optimization Method ◽  
Traveling Salesman ◽  
Hamiltonian Circuit ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Optimal Paths

Traveling salesman problem (TSP) is one well-known NP-Complete problem. The objective is to search the optimal Hamiltonian circuit (OHC) in a tourist map. The particle swarm optimization (PSO) integrated with the four vertices and three lines inequality is introduced to detect the OHC or approximate OHC. The four vertices and three lines inequality is taken as local heuristics to find the local optimal paths composed of four vertices and three lines. Each of this kind of paths in the OHC or approximate OHC conforms to the inequality. The particle swarm optimization is used to search an initial approximation. The four vertices and three lines inequality is applied to convert all the paths in the approximation into the optimal paths. Then a better approximation is obtained. The method is tested with several Euclidean TSP instances. The results show that the much better approximations are searched with the hybrid PSO. The convergence rate is also faster than the traditional PSO under the same preconditions.

Design on Filter Parameters with Hybrid PSO Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1888-1891
Author(s):  
Yi Zhang ◽  
Ke Wen Xia ◽  
Gen Gu
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Filter Model ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Model Setting

In order to solve the problems in the optimization of filter parameters, such as large amounts of calculation and the complicated mathematical hypotheses, an approach to optimize filter parameters is presented based on the Hybrid Particle swarm optimization (HPSO) algorithm, which includes the establishing of filter model, setting up the fitness-function and optimizing filter parameters by HPSO algorithm. The application example shows that the optimization method improves the design accuracy and saves calculation, and HPSO algorithm is superior to PSO algorithm in optimization of filter parameters.

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