scholarly journals Research on Timetabling Problems Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 736-741
Author(s):  
Xin Min Ma ◽  
Lin Li Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Educational System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Course Timetabling ◽  
Fitness Value ◽  
Course Timetabling Problem

A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

scholarly journals Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem

2020 ◽  
Vol 3 (1) ◽  
pp. 12-21
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Power Dispatch ◽  
Fitness Value ◽  
Symbiotic Organisms

In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA).  Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value.  Projected HGPSOS algorithm improves the quality of the search.  Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.

scholarly journals An improved dynamic cooperative random drift particle swarm optimization algorithm based on search history decision

2020 ◽  
Vol 14 ◽  
pp. 174830262097353
Author(s):  
Ji Zhao ◽  
Yi Fu ◽  
Juan Mei
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Function Approximation ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Random Drift ◽  
Swarm Optimization ◽  
Search History ◽  
Drift Particle

A novel dynamic cooperative random drift particle swarm optimization algorithm based on entire search history decision (CRDPSO) is reported. At each iteration, the positions and the fitness values of the evaluated solutions in the algorithm are stored by a binary space partitioning tree structure archive, which leads to a fast fitness function approximation. The mutation is adaptive and parameter-less because of the fitness function approximation enhancing the mutation strategy. The dynamic cooperation between the particles by using the context vector increases the population diversity helps to improve the search ability of the swarm and cooperatively searches for the global optimum. The performance of CRDPSO is tested on standard benchmark problems including multimodal and unimodal functions. The empirical results show that CRDPSO outperforms other well-known stochastic optimization methods.

scholarly journals Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 160 ◽  
Author(s):  
Jiawei Jiang ◽  
Yanhong Wu ◽  
Hongyan Wang ◽  
Yakun Lv ◽  
Lei Qiu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Random Parameters ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Fitness Value ◽  
The Relationship ◽  
Jamming Effect

Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.

Target Position Measurement Technology Based on Quantum-Behaved Particle Swarm Optimization

2013 ◽  
Vol 303-306 ◽  
pp. 403-406 ◽  
Author(s):  
Jin Jie Yao ◽  
Jing Yang ◽  
Jian Li ◽  
Li Ming Wang ◽  
Yan Han
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Target Position ◽  
Measurement Technology ◽  
Position Measurement ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Fitness Value

Quantum-behaved particle swarm optimization algorithm (QPSO) was proposed as a kind of swarm intelligence, which outperformed standard particle swarm optimization algorithm (PSO) in search ability. This paper presents an improved QPSO with nonlinear controlled parameter according to the fitness value of the particles. Simultaneously, we apply the improved QPSO to solve the problems of target position measurement. The experimental results show that the improved QPSO has faster convergence speed, higher measurement accuracy, and good localization performance.

An Adaptive Particle Swarm Optimization Algorithm Based on Cloud Model

2010 ◽  
Vol 129-131 ◽  
pp. 612-616
Author(s):  
Jin Rong Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Cloud Model ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Fitness Value ◽  
Model C

In this paper, an adaptive particle swarm optimization algorithm based on cloud model (C-APSO) is proposed. In the suggested method, the velocities of the all particles are adjusted based on the strategy that a particle whose fitness value is nearer to the optimal particle will fly with smaller velocity. Considering the properties of randomness and stable tendency of a normal cloud model, a Y-conditional normal cloud generator is used to gain the inertial factors of the particles. The simulations of function optimization show that the proposed method has advantage of global convergence property and can effectively alleviate the problem of premature convergence.

Study on Dynamic Balance in Main Driving Mechanism of Cold Rolling Mill Based on the Particle Swarm Optimization Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 633-638 ◽  
Author(s):  
Wen Xian Tang ◽  
Jun Jie Sun ◽  
Bin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Dynamic Balance ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Inertia Force ◽  
Driving Mechanism ◽  
Swarm Optimization

A method for comprehensive dynamic balance of mechanism based on the particle swarm optimization is presented. This paper adopted nonlinear multi-objective programming method to carry out a study on three dynamic property indexes including inertia force, reaction of kinematic pair and input torque. Optimum solution for the parameters estimation problem based on the particle swarm optimization algorithm is obtained by constructing a fitness function of the mathematical optimization model, which consists of those property indexes. The simulation results indicate that the proposed method could eliminate the reluctant evaluations and interactions remarkably, thus improves the application's performance.

Application of Synergistic Optimization Method by Maximin Fitness Function Strategy Based Multi-Objective Particle Swarm Optimization Algorithm in Metal Bellows Structural Design

2013 ◽  
Vol 722 ◽  
pp. 550-556
Author(s):  
Xiao Mo Yu ◽  
Ai Ling Qin ◽  
Jia Hai Xue ◽  
Jun Ke Ye ◽  
Wen Jing Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Structure Design ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Metal Bellows ◽  
Function Strategy

In this paper, the forming process is applied to the structure design of the metal bellows for the synergistic optimization.With bellows minimum overall stiffness and minimum weight for the optimization objectives to establish multi-objective optimization design model, using the Maximin fitness function strategy based multi-objective particle swarm optimization algorithm and introduce the multiple subgroup cooperative search strategy by master-slave clustering to get the optimized solution at the same time. The algorithm is applied to the synergistic optimization of the metal bellows structure design. The results show that the convergent speed of the algorithm is fast and can effectively approximate the actual bellows structure design, and provide users with more practical and intuitive effectively design scheme.

A Novel Hybrid Optimization Algorithm Based on Multi-Agent and Particle Swarm

2011 ◽  
Vol 460-461 ◽  
pp. 512-517
Author(s):  
De Jia Shi ◽  
Wei Jin Jiang ◽  
Xiao Ling Ding
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Agent Interactions ◽  
Fitness Value ◽  
Multi Agent

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in MAI.SO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of I.SO. MAPSO realizes the purpose of' minimizing the value of' objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

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