Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering

Li, Lingling; Jiao, Licheng; Zhao, Jiaqi; Shang, Ronghua; Gong, Maoguo

doi:10.1016/j.patcog.2016.09.013

Cited by 89 publications

(40 citation statements)

References 37 publications

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“…The authors noted that the experiments provided high quality solutions for time-cost optimization of large size projects within seconds and enabled optimal planning of real life-size projects. Li et al [199] modeled complex network clustering as a multi-objective optimization problem and applied a quantum inspired discrete particle swarm optimization algorithm with nondominated sorting for individual replacement to solve it. Experimental results illustrated its competitiveness against some state-of-the-art approaches on the extensions of Girvan and Newman benchmarks [200] as well as many real-world networks.…”

Section: Some Application Instancesmentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

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371

184

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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Section: Some Application Instancesmentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

371

184

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“…From (10), it can be deduced that the sum of r i (t) is 1 and r i (t) is between 0 and 1 at iteration t. When the sum of the objective function value corresponding to all particles is 0, the coefficient r i (t) is 1/S. Otherwise, the smaller f i obj (t) is, the larger r i (t) is.…”

Section: Weighted Mean Personal Best Positionmentioning

confidence: 99%

“…Unlike PSO, QPSO needs no velocity vectors for particles, and also has fewer parameters to adjust, making it easier to implement. Since QPSO was proposed, it has attracted much attention and different variants of QPSO have been proposed to enhance the performance from different aspects and successfully applied to solve a wide range of continuous optimization problems [9][10][11][12][13][14]. In general, most current QPSO variants can be classified into three categories [15]: the improvement based on operators from other evolutionary algorithms, hybrid search methods, and cooperative methods.…”

Section: Introductionmentioning

confidence: 99%

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Chen¹

2019

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Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate.

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“…L. Li, Jiao, Zhao, Shang, and Gong () proposed a quantum‐behaved (Wright & Jordanov, ), discrete multi‐objective particle swarm optimization for complex network clustering with the ability to determine automatically the number of clusters. The two objective functions used were the kernel k means, which is the sum density of links of intraclusters, and the ratio cut, which is defined as the sum densities of links of interclusters.…”

Section: Meta‐heuristic Multi‐ and Many‐objective Algorithms Applied mentioning

confidence: 99%

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

2017

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Recently, multi‐ and many‐objective meta‐heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper‐parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas.

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Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering

Cited by 89 publications

References 37 publications

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

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