Chaos particle swarm optimization and T–S fuzzy modeling approaches to constrained predictive control

Jiang, Huimin; Kwong, C. K.; Chen, Zengqiang; Ysim, Y. C.

doi:10.1016/j.eswa.2011.07.007

Cited by 58 publications

(26 citation statements)

References 14 publications

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“…In PSO, there are n particles with the capability of moving around a supposed D-dimensional search space [69,70]. Every effective factor influencing urban growth can be considered as a dimension of the search space.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…Then, the fitness value is calculated by a fitness function which determines the optimum position of each particle. [34,70,71]. The fitness function, for example, urban growth modeling, can be built by the accumulative difference between the simulated outcomes produced based on the traditional LR model and corresponding real values.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Naghibi

Delavar

2016

IJGI

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This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error) of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was confirmed.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Naghibi

Delavar

2016

IJGI

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“…The optimization problem given by (15) is a constrained nonlinear and nonconvex optimization problem, the solution of which is difficult and generally expensive in computing time. Different approaches were investigated to solve this problem, such as the numerical optimization techniques [14,15], the metaheuristic based optimization algorithms [16][17][18], the linearization of the process fuzzy model [19], and the use of particular model structures to obtain a convex form for the cost function [20].…”

Section: Design Of the Linear Control Lawmentioning

confidence: 99%

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

2017

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This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.

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“…Yang et al [21] put forward an accelerated chaotic particle swarm optimization for data clustering by randomly generating initial particles and substituting the random parameters r 1 and r 2 of PSO with the sequences generated by the logistic map. More recently, a chaotic particle swarm optimization, which combines chaotic optimization algorithm with PSO and T-S fuzzy modeling, is proposed by Jiang et al [22] to perform constrained predictive control. In [23], Liu et al employ chaotic opposition-based population initialization instead of a pure random initialization as well as a stochastic search technique to improve the performance of PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, chaos has become much popularized in various hybrid soft computing due to its robustness and specific performance [17][18][19][20][21][22][23]. As the representative work of hybrid PSO and chaos, Liu et al [17] propose a chaotic particle swarm optimization with adaptive inertia weight factor and chaos to form a chaotic PSO algorithm, which can reasonably combine the population-based evolutionary searching ability of PSO and chaotic searching behavior.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization with Chaotic Maps and Gaussian Mutation for Function Optimization

Tian¹

2015

IJGDC

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Particle swarm optimization (PSO) is a population-based stochastic optimization that has been widely applied to a variety of problems. However, it is easily trapped into the local optima and appears premature convergence during the search process. To address these problems, we propose a new particle swarm optimization by introducing chaotic maps (tent map and logistic map) and Gaussian mutation into the PSO algorithm. On the one hand, the chaotic map is employed to initialize uniform distributed particles so as to improve the quality of the initial population, which is a simple yet very efficient method to improve the quality of initial population. On the other hand, the Gaussian mutation mechanism based on the maximal focus distance is adopted to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space until the global optimal or the closer-to-optimal solutions can be found. Experimental results on two benchmark functions demonstrate the effectiveness and efficiency of the PSO algorithm proposed in this paper.

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Chaos particle swarm optimization and T–S fuzzy modeling approaches to constrained predictive control

Cited by 58 publications

References 14 publications

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Particle Swarm Optimization with Chaotic Maps and Gaussian Mutation for Function Optimization

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