Design of Temperature Controllor in Diamond Synthetic Chamber Based on Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 496-500 ◽  
pp. 1448-1451
Author(s):  
Hong Yan Hua ◽  
Cheng Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Programming ◽  
Strong Convergence ◽  
Control Problem ◽  
Temperature Control ◽  
Particle Swarm ◽  
Nonlinear Programming Problem ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

Temperature control problem is a typical multi-constraint/multi-objective nonlinear programming problem in diamond synthetic chamber and it cannot be solved by conventional methods. This paper proposed a constraint multi-objective particle swarm optimization (CMOPSO) algorithm to solve the problem. Simulation results show that the CMOPSO algorithm can be used effectively in diamond temperature control and it has strong convergence optimization abilities for all different parameters and constraint conditions.

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

2014 ◽  
Vol 971-973 ◽  
pp. 1242-1246
Author(s):  
Tie Jun Chen ◽  
Yan Ling Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

scholarly journals A MOPSO-Based Optimal Demand Response Management System for the Integration of Wind-PV-FC-Battery Smart Grid

2019 ◽  
Vol 8 (4) ◽  
pp. 4402-4410
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Smart Grid ◽  
Demand Response ◽  
Particle Swarm ◽  
Response Strategy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results ◽  
The Impact

This paper proposes the Multi-Objective Particle Swarm Optimization to optimize the performance of hybrid WindPV-FC-Battery smart grid to minimize operating costs and emissions. The demand response strategy based on the real-time pricing program with the participation of all kinds of consumers such as residential, commercial and industrial consumers is utilized in order to resolve the power generation uncertainty of renewable energy sources. The multi-objective particle swarm optimization based energy management programming model will be leveraged to reduce the operation costs, emission of pollutants, increase the micro grid operator’s demand response benefits and at the same time satisfying the load demand constraints amongst the others. For the purpose of validating the proposed model, the simulation results are considered for different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The simulation results precisely concluded the impact created by the demand side management in reducing the effects of uncertainty that prevails in forecasted power generation through solar cells and wind turbines.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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