Hybrid Multiobjective Evolutionary Algorithms for Unsupervised QPSO, BBPSO and Fuzzy clustering

Lai, Daphne Teck Ching; Sato, Yuji

doi:10.1109/cec45853.2021.9504968

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Since it is known that none of the clustering evaluation criteria can work well for all types of datasets, optimizing only one objective function cannot capture various characteristics of datasets. Therefore, single-objective optimization algorithms cannot be effective in clustering highdimensional data or data with complex structure [15]. This work proposes an automatic Multi-Objective Particle swarm optimization (PSO) clustering with Gaussian mutation and Game Theory (MOPGMGT) to discover the human activities fully unsupervised.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Discovery With Automatic Multi-Objective Particle Swarm Optimization Clustering With Gaussian Mutation and Game Theory

Hadikhani

Lai

Ong

2024

IEEE Trans. Multimedia

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Section: Introductionmentioning

confidence: 99%

Human Activity Discovery With Automatic Multi-Objective Particle Swarm Optimization Clustering With Gaussian Mutation and Game Theory

Hadikhani

Lai

Ong

2024

IEEE Trans. Multimedia

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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Lai

Sato

2021

Algorithms

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Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

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Human Activity Discovery with Automatic Multi-objective Particle Swarm Optimization Clustering with Gaussian Mutation and Game Theory

Hadikhani¹,

Lai²,

Ong³

2022

Preprint

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Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutation and game theory (MOPGMGT) to tackle the problem of human activity discovery fully unsupervised and map the multi-objective clustering problem to game theory to get the best optimal solution. The proposed algorithm does not require any prior knowledge of the number of activities to be discovered and can find the optimal number. Multi-objective optimization problems typically cannot have a single optimal solution. To solve this issue, Nash Equilibrium (NE) is applied to the pareto front to choose the best solution. NE does not just look for the best solution, but tries to optimize the final solution by considering the effect of choosing each of the solutions as the best solution on the other solutions and one with the best impact is chosen. Moreover, a Gaussian mutation is applied on the pareto front to avoid premature convergence. Experiments on five challenging datasets demonstrate that the proposed approach is the most efficient to achieve better accuracy in human activity discovery and also can determine the optimal number of clusters.

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Hybrid Multiobjective Evolutionary Algorithms for Unsupervised QPSO, BBPSO and Fuzzy clustering

Cited by 3 publications

References 14 publications

Human Activity Discovery With Automatic Multi-Objective Particle Swarm Optimization Clustering With Gaussian Mutation and Game Theory

Human Activity Discovery With Automatic Multi-Objective Particle Swarm Optimization Clustering With Gaussian Mutation and Game Theory

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Human Activity Discovery with Automatic Multi-objective Particle Swarm Optimization Clustering with Gaussian Mutation and Game Theory

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