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Improvement and Application of Particle Swarm Optimization Algorithm based on the Parameters and the Strategy of Co-Evolution |
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PP: 1355-1364 |
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Author(s) |
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Haigang Li,
Qian Zhang,
Yong Zhang,
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Abstract |
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| PSO algorithm is an intelligent optimization algorithm based on swarm intelligence. Particle swarm optimization algorithm
is simple, easy to implement, and it has a wide application prospect in scientific research and engineering applications. In real life, most
of the optimization problem is the optimization problem of some nonlinear discrete with the existence of local. PSO algorithm also has
some defects in treating optimization problem. The optimal performance of the PSO algorithm is efficiency; the attribute weights are
optimized, which is the same as to improve the accuracy of case retrieval. The application of case is based reasoning in the optimization
of pressure vessel model design. Through the experiment results, the optimization of the performance of PSO algorithm is better;
the result of prediction is more approximate to the actual value, which can meet the needs of practical applications in engineering.
The evolution strategy algorithm and the control parameters of the algorithm on the algorithm performance are affected. The control
parameter adaptive particle swarm optimizer algorithm and evolution strategy of adaptive scheduling particle swarm algorithm, particle
swarm optimization algorithm form a parameter and the strategy of co evolution, the co-evolution PSO algorithm and DBPSO algorithm
and ASPSO algorithm are compared. The results show that, co-evolution PSO algorithm in the optimization performance improved to
a certain extent than DBPSO algorithm and ASPSO algorithm, which achieved good results. |
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