Improved PSO based clustering fusion algorithm for multimedia image data projection

Pan, Feng; Chen, Deqiang; Lu, Lu

doi:10.1007/s11042-019-08015-z

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…The unique information exchange and learning methods of PSO makes the movement of the whole population in the same direction, which endows the PSO with strong search performance and high adaptability to optimization problems. Meanwhile, the PSO algorithm has a strong exploitation but its exploration is very poor [41][42][43][44][45]. Exploitation and exploration are local optimization capability and global optimization capability of algorithms respectively.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Gao

Wang

et al. 2020

Mathematics

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Job shop scheduling problem (JSSP) has high theoretical and practical significance in academia and manufacturing respectively. Therefore, scholars in many different fields have been attracted to study this problem, and many meta-heuristic algorithms have been proposed to solve this problem. As a meta-heuristic algorithm, particle swarm optimization (PSO) has been used to optimize many practical problems in industrial manufacturing. This paper proposes a hybrid PSO enhanced with nonlinear inertia weight and and Gaussian mutation (NGPSO) to solve JSSP. Nonlinear inertia weight improves local search capabilities of PSO, while Gaussian mutation strategy improves the global search ability of NGPSO, which is beneficial to the population to maintain diversity and reduce probability of the algorithm falling into the local optimal solution. The proposed NGPSO algorithm is implemented to solve 62 benchmark instances of JSSP, and the experimental results are compared with other algorithms. The results obtained by analyzing the experimental data show that the algorithm is better than other comparison algorithms in solving JSSP.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Gao

Wang

et al. 2020

Mathematics

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Particle swarm optimization performance improvement using deep learning techniques

Pawan

Prakash

Chowdhury³

et al. 2022

Multimed Tools Appl

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Modified bee colony optimization with opposition learning algorithm on use of medical data clustering

Sahoo¹,

Pattanaik²,

Mohanty³

2023

IDT

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Clustering has gained popularity in the data mining field as one of the primary approaches for obtaining data distribution and data analysis. The medical data analysis for different diseases is a great challenge in current research. The benefits of opposition based learning such as faster convergence rate and better approximate result in finding global optimum can be helpful in this area. To achieve faster convergence and better clustering results for medical data, in this work, the authors have proposed an approach utilising modified bee colony optimization with opposition based learning and k-medoids technique. The initial centroid plays an important role in the bee colony optimization based clustering. The proposed approach uses k-medoids algorithm for this task. In order to facilitate faster convergence, it adds the opposite bees which are located at exactly the opposite location of the initial bees. The exploration task is performed by both of these kinds of bees to find potential solutions. This increases the algorithm’s capacity for exploration and, consequently, the rate of convergence. Five distinct medical datasets collected from the UCI library are investigated to demonstrate the algorithm’s efficacy. The implementation results demonstrate that the algorithm gives better convergence rate and clustering quality compared to some the existing algorithms.

show abstract

Improved PSO based clustering fusion algorithm for multimedia image data projection

Cited by 3 publications

References 25 publications

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Particle swarm optimization performance improvement using deep learning techniques

Modified bee colony optimization with opposition learning algorithm on use of medical data clustering

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