Quantum-behaved Particle Swarm Optimization with Nelder-Mead Simplex Search Method

DOI

10.2991/jrarc.2015.5.1.4How to use a DOI?

Keywords

Swarm optimization,Nelder -Mead simplex method,hybrid algorithm, continuous optimization

Abstract

This paper proposes a novel hybrid algorithm based on quantum-behaved particle swarm optimization (QPSO) algorithm and Nelder-Mead (NM) simplex search method for continuous optimization problems, abbreviated as QPSO-NM. This hybrid algorithm is very easy to be implemented since it does not require continuity and differentiability of objective functions, and it also combines powerful global search ability of QPSO with precise local search of NM simplex method. In a suite of the first 10 test functions taken from CEC2005, QPSO-NM algorithm is compared with other four popular competitors and six special algorithms that are dedicated to solve CEC2005 test function suite. It is showed by the computational results that QPSO-NM outperforms other algorithms in terms of both convergence rate and solution accuracy. The proposed algorithm is extremely effective and efficient at locating optimal solutions for continues optimization.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Weiquan Yao
PY  - 2015
DA  - 2015/04/01
TI  - Quantum-behaved Particle Swarm Optimization with Nelder-Mead Simplex Search Method
JO  - Journal of Risk Analysis and Crisis Response
SP  - 47
EP  - 53
VL  - 5
IS  - 1
SN  - 2210-8505
UR  - https://doi.org/10.2991/jrarc.2015.5.1.4
DO  - 10.2991/jrarc.2015.5.1.4
ID  - Yao2015
ER  -