Hyper rectangle search based particle swarm algorithm for dynamic constrained multi-objective optimization problems

2012 ◽  
Author(s):  
Jingxuan Wei ◽  
Yuping Wang
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Swarm Algorithm

Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization

2010 ◽  
Vol 18 (1) ◽  
pp. 127-156 ◽  
Author(s):  
Ahmed Elhossini ◽  
Shawki Areibi ◽  
Robert Dony
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Hybrid Forms

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

Multi-Objective Optimal Sizing Hybrid Power System in a Solar Electric Vehicle Using Particle Swarm Optimization Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2699-2703 ◽  
Author(s):  
Shi Qiong Zhou ◽  
Gui Fang Guo ◽  
Yong Yang Xiang
Keyword(s):  
Power System ◽  
Electric Vehicle ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective Optimization ◽  
Hybrid Power System ◽  
Optimal Sizing ◽  
Multi Objective ◽  
Hybrid Power ◽  
Swarm Algorithm

In a solar electric vehicle, the optimal sizing of hybrid power system can be considered as a multi-objective optimization problem. The two conflicting goals are to maximize the Loss of Peak Power Probability (LPPP) and minimize the system cost. And the former is related to the reliability of the system while the latter relates to whether production prototype so the two optimization objectives are important. An improved particle swarm algorithm was presented to optimal size the hybrid power system. Here the mutation operator of genetic algorithm was introduced and the acceleration factor could change with time. The optimization results show that: the improved particle swarm algorithm can well solve the hybrid power system for multi-objective optimization problems.

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