Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior

Tsai, Hsing‐Chih; Lin, Yong

doi:10.1016/j.asoc.2011.05.022

Cited by 106 publications

(49 citation statements)

References 14 publications

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“…In this context, feed forward neural networks (Wang et al, 2005), parameter estimation in engineering systems (Li et al, 2004), combinatorial optimization problem (Cai, 2010), global optimization (Yang, 2010), Augmented Lagrangian fish swarm based method for global optimization (Rocha et al, 2011), forecasting stock indices using radial basis function neural networks optimized (Shen et al, 2011), and hybridization of the FSA with the Particle Swarm Algorithm to solve engineering systems (Tsai & Lin, 2011).…”

Section: Non-instinctive Collective Movement Operatormentioning

confidence: 99%

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

Souza¹,

Lobato²,

Gedraite³

2012

Frontiers in Advanced Control Systems

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Section: Non-instinctive Collective Movement Operatormentioning

confidence: 99%

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

Souza¹,

Lobato²,

Gedraite³

2012

Frontiers in Advanced Control Systems

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“…Artificial fish swarm algorithm (AFSA) is an intelligent optimization algorithm simulating fish swarm behavior, such as foraging, swarming, chasing, random, in the search domain for optimization [20][21][22]. In the fish swarm pattern, the method described in Figure 4 is adopted to simulate the visual sensor system of the artificial fish.…”

Section: Artificial Fish Swarm Algorithmmentioning

confidence: 99%

RBF Neural Network Soft-Sensor Model of Electroslag Remelting Process Optimized by Artificial Fish Swarm Optimization Algorithm

Wang

Han

Yang

2014

Advances in Mechanical Engineering

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For predicting the key technology index of electroslag remelting (ESR) process (the melting rate and cone purification coefficient of the consumable electrode), a radial basis function (RBF) neural network soft-sensor model optimized by the artificial fish swarm algorithm (AFSA) is proposed. Based on the technique characteristics of ESR production process, the auxiliary variables of softsensor model are selected. Then the AFSA is adopted to train the RBF neural network prediction model in order to realize the nonlinear mapping between input and output variables. Simulation results show that the model has better generalization and prediction accuracy, which can meet the online soft sensing requirement of ESR process real-time control.

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“…avoiding behavior [26], leaping behavior [30], communication behavior [31], etc. On the other hand, Li pointed out that AFSA could also be moderately simplified according to the property of the problem to be solved in his doctor thesis [29].…”

Section: Brief Review To Afsamentioning

confidence: 99%

A New Artificial Fish Swarm Algorithm for the Multiple Knapsack Problem

Liu

Odaka

Kuroiwa

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYA new artificial fish swarm algorithm (AFSA) for solving the multiple knapsack problem (MKP) is introduced in this paper. In the proposed AFSA, artificial fish (AF) individuals are only allowed to search the region near constraint boundaries of the problem to be solved. For this purpose, several behaviors to be performed by AF individuals, including escaping behavior, randomly moving behavior, preying behavior and following behavior, were specially designed. Exhaustive experiments were implemented in order to investigate the proposed AFSA's performance. The results demonstrated the proposed AFSA has the ability of finding highquality solutions with very fast speed, as compared with some other versions of AFSA based on different constraint-handling methods. This study is also meaningful for solving other constrained problems. key words: artificial fish swarm algorithm, multiple knapsack problem, constraint boundary, search region

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Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior

Cited by 106 publications

References 14 publications

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

RBF Neural Network Soft-Sensor Model of Electroslag Remelting Process Optimized by Artificial Fish Swarm Optimization Algorithm

A New Artificial Fish Swarm Algorithm for the Multiple Knapsack Problem

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