A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems

Quantum-behaved particle swarm optimization (QPSO), a novel variant of PSO inspired by quantum mechanics, is a global convergence guaranteed algorithm, which outperforms the original PSO in search ability and has fewer parameters to control. But as many other PSOs, it is easy to fall into local opti...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences 2014-12, Vol.289, p.162-189
Main Authors: Tang, Deyu, Cai, Yongming, Zhao, Jie, Xue, Yun
Format: Article
Language:English
Subjects:
Algorithms
Continuous non-linear large scale problem
Convergence
Mathematical models
Memetic algorithm
Memory mechanism
Nonlinearity
Optimization
Quantum-behaved particle swarm optimization
Random numbers
Searching
Swarm intelligence
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Quantum-behaved particle swarm optimization (QPSO), a novel variant of PSO inspired by quantum mechanics, is a global convergence guaranteed algorithm, which outperforms the original PSO in search ability and has fewer parameters to control. But as many other PSOs, it is easy to fall into local optimum in solving high-dimensional complex optimization problems. This paper proposes an improved QPSO algorithm for continuous non-linear large scale problems based on memetic algorithm and memory mechanism. The memetic algorithm is used to make all particles (each particle corresponding to a memetic) gain some experience through a local search before being involved in the evolutionary process, and the memory mechanism is used to introduce a so-called ‘bird kingdom’ with memory capacity, both of which can improve the global search ability of the algorithm. Another difference compared to the previous QPSOs is that we let each dimension of a particle update with the same random number, thus increasing the speed of convergence and enhancing the ability of local search. Numerical experiments are conducted to compare the proposed algorithm with different variants of PSO and other swarm intelligence algorithms. The experimental results show the superiority of the proposed approach on benchmark test functions.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2014.08.030