ScholarLens Back to search
2009•Unpublished venueRequires access

Evolutionary Swarm Optimization Algorithm for Numerical Function Optimization

Haiyan Quan, Xinling Shi

Open publisher page 0 citations

Abstract

The paper introduces an evolutionary swarm model (ESM), based on the model, an evolutionary swarm algorithm (ESA) is designed out using five elements. In this work, the performance of ESA is tested with 5 multivariable benchmark functions, and compared with the other optimization algorithms. The simulation results show that the algorithm has an excellent performance in the global optimization, and can be efficiently employed to solve the optimization problem for the multimodal function with high dimensionality.

About this research paper

What this paper is about

The paper introduces an evolutionary swarm model (ESM), based on the model, an evolutionary swarm algorithm (ESA) is designed out using five elements. In this work, the performance of ESA is tested with 5 multivariable benchmark functions, and compared with the other optimization algorithms. The simulation results show that the algorithm has an excellent performance in the global optimization, and can be efficiently employed to solve the optimization problem for the multimodal function with high dimensionality.

Why it matters

A significance statement is not available in the OpenAlex record.

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

The paper introduces an evolutionary swarm model (ESM), based on the model, an evolutionary swarm algorithm (ESA) is designed out using five elements. In this work, the performance of ESA is tested with 5 multivariable benchmark functions, and compared with the other optimization algorithms. The simulation results show that the algorithm has an excellent performance in the global optimization, and can be efficiently employed to solve the optimization problem for the multimodal function with high dimensionality.

Key concepts: Benchmark (surveying), Evolutionary algorithm, Multi-swarm optimization, Swarm behaviour, Computer science, Curse of dimensionality, Mathematical optimization, Meta-optimization

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Evolutionary Swarm Optimization Algorithm for Numerical Function Optimization — Research Paper | ScholarLens