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.

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

Immersive Interactive Virtual Fish Swarm Simulation Based on Infrared Sensors

2020 ◽  
Vol 34 (11) ◽  
pp. 2054027
Author(s):  
Xingquan Cai ◽  
Yakun Ge ◽  
Chen Sun ◽  
Chao Chen ◽  
Honghao Buni
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Particle Swarm ◽  
Optimization Method ◽  
Simulation System ◽  
Movement Behavior ◽  
Infrared Sensors ◽  
3D Interaction ◽  
User Interactions ◽  
Simulation Based

Virtual simulation and 3D interaction have shown great potentials in a variety of domains for our future life. For a virtual fish swarm simulation system, the simulation of cohesion behaviors of fish swarm and the interaction between human and fish swarm are two key components to create immersive interactive experiences. However, it is a huge challenge to create a realistic fish swarm simulation system while providing a natural and comfortable interaction. In this paper, we propose a method for immersive virtual fish swarm simulation based on infrared sensors. Based on dynamic weight constraints, we propose a particle swarm optimization method for fish swarm cohesion simulation, which separates a particle swarm by the state of each particle and dynamically controls the particle swarm, making the movement behavior of virtual fish more realistic. In addition, an interactive fast skinning method is proposed for cartoon fishes, which leverages image segmentation, Optical Character Recognition (OCR) and bone skinning are used to generate cartoon fishes based on user-created colors. With infrared sensors, we propose a method for virtual fish swarm interaction, where the positions of human skeleton are processed by an action analyzer, achieving real-time user interactions with fish swarms. With all the proposed techniques integrated in a system, the experimental results show that our method is feasible and effective.

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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