A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm

Sadollah, Ali; Salarieh, Hassan; Yadav, Anupam

doi:10.1016/j.asoc.2018.07.039

Cited by 189 publications

(116 citation statements)

References 27 publications

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“…43,44 In this algorithm, the best solution at each iteration is considered the output data, and the main goal is to decrease the error between this best solution and the other predicted solutions. The ANN structure consists of several artificial neurons, which are connected together to form the NN.…”

Section: Neural Network Optimization Algorithmmentioning

confidence: 99%

“…These input data can be produced from experiments or simulation results. 43,44 In this algorithm, the best solution at each iteration is considered the output data, and the main goal is to decrease the error between this best solution and the other predicted solutions. This target solution is updated with the course of iterations.…”

Section: Neural Network Optimization Algorithmmentioning

confidence: 99%

“…43 It was applied to solve several complex optimization problems, and it is examined by many well-known benchmark functions. It is invented by Ali Sadollah and other researchers in 2018.…”

Section: Introductionmentioning

confidence: 99%

“…It is invented by Ali Sadollah and other researchers in 2018. 43 It was applied to solve several complex optimization problems, and it is examined by many well-known benchmark functions. A comparison of its results with other optimization methods has proven its high efficacy and performance.…”

Section: Introductionmentioning

confidence: 99%

“…A comparison of its results with other optimization methods has proven its high efficacy and performance. 43 It is mainly based on the ANN structure and its coherent concepts to produce new candidate solutions. It has several merits such as its high speed of convergence and its lower number of parameters to be designed.…”

Section: Introductionmentioning

confidence: 99%

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Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

2019

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Summary I/V polarization curves of proton‐exchange membrane fuel cells (PEMFCs) are used to characterize the performance of single cells and stacks. Numerous semi‐empirical models are presented to predict such polarization curves by determining the unknown parameters of mathematical model of the PEMFCs stack. In this paper, a novel optimization approach, namely neural network algorithm (NNA) is applied for an estimation of the unknown PEMFC model parameters. The NNA is employed to minimize adopted objective function, which is formulated as the sum of squared deviations (SSD) between the actual data and estimated voltage points subjects to set of inequality constraints are satisfied. Three commercial types of PEMFCs stack namely Ballard Mark V, BCS‐500 W, and Nedstack PS6 are numerically simulated to show the effectiveness of the proposed NNA‐based tool for parameter identification. The minimum values of SSD are 0.8536 V2 for Ballard Mark V, 0.011698 V2 for BCS‐500 W stack, and 2.14487 V2 for Nedstack PS6, respectively. The obtained results of the NNA are compared with other optimizers recently published in the literature such as flower pollination algorithm, slap swarm optimizer, grey wolf algorithm, grasshopper optimization algorithm, and shark smell algorithm under the same conditions. The comparisons and other performance tests indicate the robustness and the competition of the adopted NNA‐based method for producing accurate I/V polarization curves under different operating scenarios.

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Section: Neural Network Optimization Algorithmmentioning

confidence: 99%

Section: Neural Network Optimization Algorithmmentioning

confidence: 99%

“…43 It was applied to solve several complex optimization problems, and it is examined by many well-known benchmark functions. It is invented by Ali Sadollah and other researchers in 2018.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

2019

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Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

Elsisi

2020

Int J Intell Syst

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The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition‐based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.

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Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

Elsisi

Ebrahim

2021

Int J of Intelligent Sys

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The adjusting of autonomous vehicles (AVs) steering angle represents a challenging issue in the intelligent vehicular applications. Among different control strategies, the model predictive control (MPC) is devoted to providing efficacious steering control for the AVs. However, the formulation of MPC with a few parameters is the main challenge to overcome the complicated computation that happens due to the long prediction and control horizons. Hybridization between discrete‐time Laguerre function (DTLF) and MPC is suggested to formulate the MPC with few parameters. While the hybrid DTLF–MPC requires an adequate tuning technique for their gains to improve the system performance. A new low computational burden design is proposed in this paper to adjust the gains of the hybrid DTLF–MPC based on the social ski driver algorithm (SSDA). This algorithm is developed as a recently artificial intelligence technique. The tuned hybrid DTLF–MPC by SSDA is devoted to control AV's steering angle. Furthermore, the proposed system takes into account the vision dynamics. The system performance based on the tuned DTLF–MPC by SSDA is evaluated with the MPC based on the neural network algorithm for comparison. Different scenarios are carried out to confirm the superiority of the suggested control mechanism to handle small as well as large‐scale variations of road curvatures.

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A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm

Cited by 189 publications

References 27 publications

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

Optimal design of low computational burden model predictive control based on SSDA towards autonomous vehicle under vision dynamics

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