Multi-model function optimization by a new hybrid nonlinear simplex search and particle swarm algorithm

N/ACitations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

A new hybrid Particle Swarm Optimization (PSO) algorithm is proposed based on the Nonlinear Simplex Search (NSS) method. At late stage of PSO, when the most promising regions of solutions are fixed, the algorithm isolates particles that are very close to the extrema, and applies the NSS method to them to enhance local exploitation searching. Explicit experimental results on famous benchmark functions indicate that this approach is reliable and efficient, especially on multi-model function optimizations. It yields better solution qualities and success rates compared to other published methods. © Springer-Verlag Berlin Heidelberg 2005.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Wang, F., Qiu, Y., & Feng, N. (2005). Multi-model function optimization by a new hybrid nonlinear simplex search and particle swarm algorithm. In Lecture Notes in Computer Science (Vol. 3612, pp. 562–565). Springer Verlag. https://doi.org/10.1007/11539902_68

Readers' Seniority

Tooltip

Researcher 3

60%

Professor / Associate Prof. 1

20%

PhD / Post grad / Masters / Doc 1

20%

Readers' Discipline

Tooltip

Computer Science 2

33%

Engineering 2

33%

Philosophy 1

17%

Agricultural and Biological Sciences 1

17%

Save time finding and organizing research with Mendeley

Sign up for free