ScholarLens Back to search
2017•AIP conference proceedingsRequires access

Research of improved particle swarm optimization algorithm

Zhiping Ding

Open publisher page 13 citations

Abstract

Because of the standard particle swarm optimization algorithm (Particle Swarm Optimization PSO) has slow convergence speed and easy to fall into the local minimum problem, in order to solve these problems, this paper proposes a multi-objective particle swarm optimization method based on improved culture. The simulation results show that the proposed algorithm is better in convergence speed and local minimum value, which shows that the improved particle swarm optimization algorithm has a good reference value.

About this research paper

What this paper is about

Because of the standard particle swarm optimization algorithm (Particle Swarm Optimization PSO) has slow convergence speed and easy to fall into the local minimum problem, in order to solve these problems, this paper proposes a multi-objective particle swarm optimization method based on improved culture. The simulation results show that the proposed algorithm is better in convergence speed and local minimum value, which shows that the improved particle swarm optimization algorithm has a good reference value.

Why it matters

OpenAlex reports 13 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Because of the standard particle swarm optimization algorithm (Particle Swarm Optimization PSO) has slow convergence speed and easy to fall into the local minimum problem, in order to solve these problems, this paper proposes a multi-objective particle swarm optimization method based on improved culture. The simulation results show that the proposed algorithm is better in convergence speed and local minimum value, which shows that the improved particle swarm optimization algorithm has a good reference value.

Key concepts: Particle swarm optimization, Multi-swarm optimization, Convergence (economics), Mathematical optimization, Metaheuristic, Meta-optimization, Swarm behaviour, Algorithm

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Research of improved particle swarm optimization algorithm — Research Paper | ScholarLens