Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization

Nobile, Marco S.; Cazzaniga, Paolo; Besozzi, Daniela; Colombo, Riccardo; Mauri, Giancarlo; Pasi, Gabriella

doi:10.1016/j.swevo.2017.09.001

Cited by 185 publications

(111 citation statements)

References 34 publications

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“…In our future work, on the one hand, the proposed algorithm's structure will be investigated on several different kinds of algorithms [26,[35][36][37] to improve their performances in addressing large-scale optimization problems. On the other hand, we will apply the proposed algorithm to real applications, such as optimization problems in an industrial network, traffic network, location problems and so forth [38,39].…”

Section: Discussionmentioning

confidence: 99%

“…Improper parameters or laws will negatively affect an algorithm's performance. To provide an easy tool for readers in the design of PSO, Spolaor [25] and Nobile [26] proposed reboot strategies based PSO and fuzzy self-tuning PSO, respectively, to provide a simple way to tube PSO parameters. However, for large-scale optimization, it is also a challenge for PSO implementation.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

2019

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As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

2019

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“…In the past two decades, a number of variants to the original have been introduced attempting to further improve its performance. Typical variants can be summarized in the following four types: (1) neighborhood topology [29], [30]; (2) parameter control [31], [32]; (3) hybrid methods [33], [34] and (4) novel learning schemes [35], [36]. Since genetic algorithms (GAs) have good exploration ability, genetic learning PSO (GLPSO) has been proposed [37] to strength the performance of PSO by generating high-quality exemplars to guide the evolution of the particles [38].…”

Section: B Psomentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this paper introduces a particle swarmbased approach for EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without a full retraining. Experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.

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“…Oliveira et al [13] presented a homogeneous cluster ensemble based on particle swarm clustering algorithm. Initially, many base partitions are taken from the data and they are given as input to the consensus function and genetic selection operators are used to decide the final partition.…”

Section: Literature Surveymentioning

confidence: 99%

An Application of Pso-Based Intuitionistic Fuzzy Clustering to Medical Datasets

Parvathavarthini¹,

Visalakshi²,

Shanthi³

et al. 2017

IJSC

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Clustering is the process of splitting data into several groups based on the characteristics of data. Fuzzy clustering assigns a data object to various clusters based on different membership values. In medical field, the diagnosis of the disease has to be done without faults and in an earlier time without any delay. So, there is a need to represent imprecise nature of the data. To represent vague data in a clear manner, Intuitionistic fuzzy set introduces a parameter called hesitancy degree. In case of Intuitionistic fuzzy clustering, this indicates that the user is not aware whether the object belongs to or not belongs to a cluster. In such a case, hesitancy can very well represent the inherent noise in the data or the ignorance of the user that is given by the state 'may be'. All clustering algorithms choose the initial seed in a random fashion. But, this creates a serious impact on the convergence of the algorithm and the clustering algorithms tend to fall into local minima. This work utilizes Intuitionistic fuzzy Particle Swarm Optimization to initialize the centroids for the Intuitionistic fuzzy clustering algorithm. The algorithm is executed over medical datasets from UCI repository and the results indicate that optimal clusters are achieved. The proposed method performs well when compared with IFCM and FCM-PSO.

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Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization

Cited by 185 publications

References 34 publications

An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

Particle Swarm Optimized Autonomous Learning Fuzzy System

An Application of Pso-Based Intuitionistic Fuzzy Clustering to Medical Datasets

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