Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

Abstract

Quantum-behaved particle swarm optimization (QPSO) algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL) is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

“…Comparison results showed that DNSPSO obtained a promising performance on the majority of the test problems. Fu et al [92] proposed a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies were utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning was employed for population initialization.…”

Convergence properties of quantum evolutionary algorithms on high dimension problems

“…Firstly, a set of traditional, basic functions, was taken from the first 13 functions presented in [8]. Additionally, a non-transformed basic version of Schwefel 7 [25] was used when comparing to data published for three recent QIEA [37,42,43], and a basic two dimensional problem from [44], when comparing another QIEA. A second set of more complicated functions was added from the first 20 functions defined in the CEC-2013 specification [9].…”

“…A comparison of SRQEA with five different QIEAs is given in Table 8: a hybrid quantum PSO algorithm HRCQEA [37], a region based QIEA RQEA [42], a hybrid quantum PSO with neighbourhood search NQPSO [43], and two hybrid quantum GAs QGAXM [54] and CQGA [44]. The five fitness functions used in [37] where available in [42] and [43], so were chosen for comparison. When comparing to QGAXM and CQGA, the evaluated fitness functions were matched in their entirety, including a two-dimensional…”

Integration of Entropy Maximization and Quantum Behaved Particle Swarm Algorithm for Unsupervised Change Detection of MR Skull Bone Lesions

“…In Quantum based Particle Swarm Optimization Algorithm the state of the particles are determined by the quantum model. The parameters w, c 1 , c 2 and the velocity term in the Particle Swarm Optimization algorithm are eliminated in QPSO [27].…”