Abstract
This paper proposed an improved particle swarm optimization algorithm based on analysis of scientific materials. The core thesis of MPSO (Multiple Particle Swarm Algorithm) is to improve the single population PSO to interactive multi-swarms, which is used to settle the problem of being trapped into local minima during later iterations because it is lack of diversity. The simulation results show that the convergence rate is fast and the search performance is good, and it has achieved very good results.
Highlights
In order to solve actual problems, a number of methods of algorithmic Swarm intelligence have been worked out,2 the most effective of which is Particle Swarm Optimization (PSO)[1]
Being as a skill to solve problems, the main advantage of PSO is its high speed of convergence, which exceeds that of Evolutionary Algorithms (EA) and the rest global optimization algorithms
The PSO algorithm first randomly initialized a group of particles, in each iteration, the particle is updated by following the two extremes: one is the optimal particle itself to find the solution, called individual best; another is the whole population found in the optimal solution, called global extreme value point, and the local PSO algorithm without the only one part of the population, adjacent particles, is the local extreme in the optimal solution
Summary
In order to solve actual problems , a number of methods of algorithmic Swarm intelligence have been worked out, the most effective of which is Particle Swarm Optimization (PSO)[1]. Being as a skill to solve problems, the main advantage of PSO is its high speed of convergence, which exceeds that of Evolutionary Algorithms (EA) and the rest global optimization algorithms. Particle velocity and position by tracking individual extreme and global extreme update, each iteration update once, to find the optimal solution or the maximum number of iterations is reached so far.[4,5]. We would analyzes this potential by evaluation of MPSO on both mathematical benchmark functions. There are five benchmark functions used to simulation results, which are compared to that of other optimization algorithms, the advantaged of the proposed algorithms would be clearly reported in this paper
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