A New Hybrid Particle Swarm Optimization Algorithm for Handling Multiobjective Problem Using Fuzzy Clustering Technique

Benameur, Lamia; Alami, Jamila El; Imrani, A. El

doi:10.1109/cssim.2009.42

Cited by 16 publications

(7 citation statements)

References 17 publications

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“…FC -MOPSO is another research that combined the multiobjective particle swarm (MOPSO) approach with the fuzzy clustering (FC) technique [4] . In FC -MOPSO, the migration concept is used to exchange information between different subswarms and to ensure their diversity.…”

Section: Related Workmentioning

confidence: 99%

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

Farajzadeh

2015

Journal of Intelligent Systems

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AbstractAmong the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time.

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Section: Related Workmentioning

confidence: 99%

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

Farajzadeh

2015

Journal of Intelligent Systems

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“…M a n u s c r i p t 10 In addition to the discussed basic variations in the PSO algorithm, there are many more modified variants and applications of PSO found in literature. Some of them include Orthogonal Learning PSO [31], Gaussian-Distributed PSO [32], Comprehensive Learning PSO [33], Frankenstein's PSO [34], Cooperatively Coevolving PSO [35], Dissipative PSO [36], Distance-based Locally Informed PSO [37], Aging Leader and Challengers PSO [38], Crown-Jewel-Defence Strategy based PSO [39], Immune Cooperative PSO [40], Single solution PSO [41], Niching PSO [42], Chaos-PSO [43], Binary Multi-Objective PSO [44], PSO with Speciation and Adaptation [45], Discrete PSO [46] and Binary PSO [47].…”

Section: Page 8 Of 43mentioning

confidence: 99%

A Swarm Optimization approach for clinical knowledge mining

Christopher¹,

Nehemiah²,

Kannan³

2015

Computer Methods and Programs in Biomedicine

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“…As in single-objective, a popular use for clustering in multi-objective algorithms is to split the population (or swarm) in several subgroups, examples of these approaches can be found in (Pulido and Coello Coello 2004;Zhang and Xue 2007;Benameur et al 2009).…”

Section: Clustering In Multi-objective Optimizationmentioning

confidence: 99%

“…Despite using a clustering quality measure, in this paper the authors are only interested in estimating the diversity of solutions in the population, and not to evaluate the clustering quality itself for other purposes. Benameur et al (2009) used a measure called the normalized partition entropy to compare the quality of di↵erent clustering runs to choose the best of these runs.…”

Section: Clustering Spaces Similarity Measures and Quality Indicatorsmentioning

confidence: 99%

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

et al. 2017

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A variety of general strategies have been applied to enhance the performance of multi-objective optimization algorithms for many-objective optimization problems (those with more than three objectives). One of these strategies is to split the solutions to cover di↵erent regions (clusters) and apply an optimizer to each region with the aim of producing more diverse solutions and achieving a better distributed approximation of the Pareto front. However, the e↵ectiveness of clustering in this context depends on a number of issues, including the characteristics of the objective functions. In this paper we show how the choice of the clustering strategy can greatly influence the behavior an optimizer. We try to find a relation between the characteristics a of multi-objective optimization problem (MOP) and the e ciency of the use of a clustering type in its resolution. Using as a case study, the Iterated Multi-swarm (I-Multi), a recently introduced multi-objective particle swarm optimization (MOPSO) algorithm, we scrutinize the impact that clustering in di↵erent spaces (of variables, objectives, and a combination of both) can have on the approximations of the Pareto front. Furthermore, using two difficult function benchmarks of problems of up to 20 objectives, we evaluate the e↵ect of using di↵erent metrics for determining the similarity between the solutions during the clustering process. Our results confirm the important e↵ect of the clustering strategy on the behavior of multi-objective optimizers. Moreover, we present evidence that some problem characteristics can be used to select the most e↵ective clustering strategy, significantly improving the quality of the Pareto front approximations produced by I-Multi.

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A New Hybrid Particle Swarm Optimization Algorithm for Handling Multiobjective Problem Using Fuzzy Clustering Technique

Cited by 16 publications

References 17 publications

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

A Swarm Optimization approach for clinical knowledge mining

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

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