원문정보
초록
영어
Particle swarm optimization (PSO) is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Particle swarm optimization is an optimization method. It is an optimization algorithm, which is based on swarm intelligence. Optimization problems are widely used in different fields of science and technology. Sometimes such problems can be complex due to its practical nature. Particle swarm optimization (PSO) is a stochastic algorithm used for optimization. It is a very good technique for the optimization problems. But still there is a drawback that it gets stuck in local minima. To improve the performance of PSO, the researchers have proposed some variants of PSO. Some researchers try to improve it by improving the initialization of swarm. Some of them introduced new parameters like constriction coefficient and inertia weight. Some define different methods of the inertia weight to improve performance of PSO and some of them work on the global and local best. This paper transplants some of the parameters used to enhance the performance of Particle Swarm Optimization technique.
목차
1. Introduction
1.1. Optimization
1.2. Particle
1.3. Particle Swarm Optimization
1.4. PSO as an Optimization Technique
1.5. PSO Algorithm
2. Background of Particle Swarm Optimization (PSO)
2.1. PSO as a Member of Swarm Intelligence
2.2. PSO Elements
2.3. Parameter Settings for the PSO Algorithm
2.4. Variants of Particle Swarm Optimization
2.5. Parameters for Improving the Performance of Particle Swarm Optimization
3. The PSO Algorithm
3.1. PSO Pseudo Code
3.2. Flow Chart of the Basic PSO
4. Related Work Done
5. Conclusion
6. Future Scope
References