Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications

Chen, Cheng-Hung; Liao, Yen-Yun

doi:10.1016/j.cnsns.2013.08.022

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…A tribal PSO (TPSO) is proposed in [106] where the population is split into several tribes or sub-swarms using a self-clustering algorithm. The process of the TPSO algorithm consists of four major steps: initializing population, using a clustering algorithm to generate tribes, performing the evaluation step where the performance of each particle is evaluated, and finally using the tribe's adaptation method to add and delete particles.…”

Section: ) Multi-swarm Psomentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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Section: ) Multi-swarm Psomentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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“…Thus, bacteria in the chemotaxis process have the opportunity to move to the best location P i l best of own, the best location B j leader of a group, and the best location P g best of a swarm. In addition, in order to improve the searching capability of optimal area, a group concept 20,21 is introduced. Before the chemotaxis operation process is executed, the fitness functions of a swarm are arrayed in ascending order.…”

Section: The Proposed Gsbfomentioning

confidence: 99%

Mobile robot wall-following control using a fuzzy cerebellar model articulation controller with group-based strategy bacterial foraging optimization

Lin

2017

International Journal of Advanced Robotic Systems

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In this study, a fuzzy cerebellar model articulation controller based on group-based strategy bacterial foraging optimization is proposed for mobile robot wall-following control. In fuzzy cerebellar model articulation controller, the inputs are the distance between the sonar and the wall, and the outputs are the angular velocity of two wheels. The proposed groupbased strategy bacterial foraging optimization learning algorithm is used to adjust the parameters of fuzzy cerebellar model articulation controller model. The proposed group-based strategy bacterial foraging optimization has the advantages of global search, evolutionary strategies, and group evolution to speed up the convergent rate. A new fitness function is defined to evaluate the performance of mobile robot wall-following control. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall, (2) ensuring successfully running a cycle, (3) avoiding mobile robot collisions, and (4) mobile robot running at a maximum speed. The experimental results show that the proposed group-based strategy bacterial foraging optimization obtains a better wallfollowing control than other methods in unknown environments.

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“…Compared with other nature-inspired algorithms, PSO is easy to implement, simple in concept, computationally efficient, and can be easily used in combination with other algorithms [ 47 , 59 ]. Some variants of PSO algorithms are, darwinian PSO (DPSO) [ 50 ], fractional order DPSO (FODPSO) [ 12 ], adaptive cooperative PSO (ACPSO) [ 57 ], multi-swarm PSO (MCPSO) [ 36 ], tribal PSO (TPSO) [ 10 ], genetical swarm optimization (GSO) [ 9 ], hybrid differential evolution with PSO (DEPSO) [ 45 ], hybrid PSO with ant colony optimization [ 48 ], binary PSO [ 52 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques

Kanadath¹,

Jothi²,

Urolagin³

2023

SN COMPUT. SCI.

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Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu’s discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.

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Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications

Cited by 9 publications

References 27 publications

Particle Swarm Optimization: A Comprehensive Survey

Particle Swarm Optimization: A Comprehensive Survey

Mobile robot wall-following control using a fuzzy cerebellar model articulation controller with group-based strategy bacterial foraging optimization

Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques

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