A novel chaotic differential evolution hybridized with quadratic programming for short-term hydrothermal coordination

Chaudhry, F. A.; Amin, Muhammad; Iqbal, Muhammad Shoaib; Khan, R. D.; Khan, Junaid Ali

doi:10.1007/s00521-017-2940-9

Cited by 20 publications

(6 citation statements)

References 30 publications

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“…Sequential quadratic programming belongs to the class of convex optimization problem-solving technique exploit for both constrained and unconstrained optimization tasks. Few recent submissions of SQP are multiproduct economic production [35], economic load dispatch problems [36], bipedal dynamic walking robot [37], temporary hydrothermal organization [38], analysis of guidewire distortion in the vessels of blood [39], convex quadratic bi-level programming problems [40], Lane--Emden pantograph systems [41], simple LNG process [42], optimal power flow problems [43], and flight recovery for transference aircraft [44].…”

Section: Optimization: Gasqpmentioning

confidence: 99%

Design of Mayer Wavelet Neural Networks for Solving Functional Nonlinear Singular Differential Equation

Sabir

Raja

Guirao

et al. 2022

Mathematical Problems in Engineering

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In the present work, an advance computational intelligence paradigm based on functional Mayer artificial neural network (FM-ANN) is accessible for solving the singular nonlinear functional differential equation (NFDE) numerically. The solution of singular NFDE is performed by using the artificial neural networks (ANNs) optimized with global search genetic algorithm (GA) enhanced by local refinements of sequential quadratic (SQ) programming and the hybrid of GASQ programming. The proposed scheme is applied for solving three types of second-order singular NFDEs. In order to validate the correctness of the designed scheme, the comparison of the proposed and exact solutions has been performed. Moreover, the statistical interpretations are used to prove the worth, convergence, accuracy, stability, and robustness of FM-ANN-GASQP for the solution of singular NFDEs.

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Section: Optimization: Gasqpmentioning

confidence: 99%

Design of Mayer Wavelet Neural Networks for Solving Functional Nonlinear Singular Differential Equation

Sabir

Raja

Guirao

et al. 2022

Mathematical Problems in Engineering

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“…Hence, ASM is suitable to regulate the parameters. ASM has been implemented in many recent submissions, such as trade and industry load dispatch models [49], short-term hydrothermal supervision [50], bipedal walking robot dynamics [51], economic multiproduct manufacture [52], LNG process [53], model of heating in the thermal blow frame cycling [54], aircraft transportation [55], wind turbine support structures [56] and quadratic convex bilevel programming models [57]. In this study, the combination of GAASM is implemented to solve the second kind of LE-NSM, and the optimization process of GAASM is provided in Table 1.…”

Section: Network Optimizationmentioning

confidence: 99%

Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

et al. 2021

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In this work, a new heuristic computing design is presented with an artificial intelligence approach to exploit the models with feed-forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of genetic algorithms (GA) combined with local convergence aptitude of active-set method (ASM), i.e., FF-GNN-GAASM to solve the second kind of Lane–Emden nonlinear singular models (LE-NSM). The proposed method based on the computing intelligent Gudermannian kernel is incorporated with the hidden layer configuration of FF-GNN models of differential operatives of the LE-NSM, which are arbitrarily associated with presenting an error-based objective function that is used to optimize by the hybrid heuristics of GAASM. Three LE-NSM-based examples are numerically solved to authenticate the effectiveness, accurateness, and efficiency of the suggested FF-GNN-GAASM. The reliability of the scheme via statistical valuations is verified in order to authenticate the stability, accuracy, and convergence.

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“…Therefore, SQP is appropriate to adjust the parameters. SQP has many recent applications, like industry and trade load dispatch systems [34], dynamics of bipedal walking robot [35], LNG procedure [36], heating model in the thermal cycling blow frame [37], economic multiproduct production [38], temporary hydrothermal management [39], flight vector recovery of aircraft transportation [40], convex quadratic bi-level programming systems [41] and wind turbine support constructions [42]. In this work, the hybridization of GASQP system is functional to solve the nonlinear singular EF model of the third kind.…”

Section: Network's Optimizationmentioning

confidence: 99%

Evolutionary heuristic with Gudermannian neural networks for the nonlinear singular models of third kind

Sabir

Wahab

2021

Phys. Scr.

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The presented research work articulates a new design of heuristic computing platform with artificial intelligence algorithm by exploitation of modeling with feed-forward Gudermannian neural networks (FFGNN) trained with global search viability of genetic algorithms (GA) hybrid with speedy local convergence ability of sequential quadratic programing (SQP) approach, i.e., FFGNN-GASQP for solving the singular nonlinear third order Emden-Fowler (SNEF) models. The proposed FFGNN-GASQP intelligent computing solver Gudermannian kernel unified in the hidden layer structure of FFGNN systems of differential operators based on the SNEF that are arbitrary connected to represent the error-based merit function. The optimization objective function is performed with hybrid heuristics of GASQP. Three problems of the third order SNEF are used to evaluate the correctness, robustness and effectiveness of the designed FFGNN-GASQP scheme. Statistical assessments of the performance of FFGNN-GASQP are used to validate the consistent accuracy, convergence and stability.

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A novel chaotic differential evolution hybridized with quadratic programming for short-term hydrothermal coordination

Cited by 20 publications

References 30 publications

Design of Mayer Wavelet Neural Networks for Solving Functional Nonlinear Singular Differential Equation

Design of Mayer Wavelet Neural Networks for Solving Functional Nonlinear Singular Differential Equation

Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

Evolutionary heuristic with Gudermannian neural networks for the nonlinear singular models of third kind

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