The Experts below are selected from a list of 6624 Experts worldwide ranked by ideXlab platform
Muhammad Asif Zahoor Raja - One of the best experts on this subject based on the ideXlab platform.
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Design of Backtracking Search heuristics for parameter estimation of power signals
Neural Computing and Applications, 2020Co-Authors: Ammara Mehmood, Muhammad Asif Zahoor Raja, Aneela Zameer, Naveed Ishtiaq ChaudharyAbstract:This study presents a novel implementation of evolutionary heuristics through Backtracking Search optimization algorithm (BSA) for accurate, efficient and robust parameter estimation of power signal models. The mathematical formulation of fitness function is accomplished by exploiting the approximation theory in mean squared errors between actual and estimated responses, as well as, true and approximated decision variables. Variants of BSA-based meta-heuristics are applied for parameter estimation problem of power signals for identification of amplitude, frequency and phase parameters for different scenarios of noise variation. Analysis of performance evaluation for BSAs is conducted through exhaustive statistical observations in terms of mean weight deviation, root mean square error and Thiel inequality coefficient-based assessment metrics, as well as, ANOVA tests for statistical significance.
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Backtracking Search heuristics for identification of electrical muscle stimulation models using hammerstein structure
Applied Soft Computing, 2019Co-Authors: Ammara Mehmood, Aneela Zameer, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor RajaAbstract:Abstract The electrical muscle stimulation models (EMSMs) are effectively described through Hammerstein structure and are used to restore the functionality of paralyzed muscles after spinal cord injury (SCI). In the present study, global Search efficacy of evolutionary computing paradigm through Backtracking Search algorithm (BSA) is exploited for parameter estimation of EMSMs. The approximation theory in mean squared error sense is used for the construction of a merit function for EMSMs based on deviation between optimal and approximated parameters. Variants of BSA are designed based on memory size and population dynamics for the minimization problem of EMSMs having cubic spline as well as sigmoidal nonlinearities. Comparative studies by means of rigorous statistics establish the worth of scheme for effective, accurate, reliable, robust and stable identification of EMSMs in rehabilitation scenarios of SCI.
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Backtracking Search optimization heuristics for nonlinear hammerstein controlled auto regressive auto regressive systems
Isa Transactions, 2019Co-Authors: Ammara Mehmood, Aneela Zameer, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor RajaAbstract:Abstract In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global Search competency of Backtracking Search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel’s inequality coefficient as well as complexity measures.
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Backtracking Search integrated with sequential quadratic programming for nonlinear active noise control systems
Applied Soft Computing, 2018Co-Authors: Wasim Ullah Khan, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor RajaAbstract:Abstract In the present work, integrated strength of Backtracking Search algorithm (BSA) and sequential quadratic programming (SQP) is exploited for nonlinear active noise control (ANC) systems. Legacy of approximation theory in mean squared sense is utilized to construct a cost function for ANC system based on finite impulse response (FIR) and Volterra filtering procedures. Global Search efficacy of BSA aided with rapid local refinements with SQP is practiced for effective optimization of fitness function for ANC systems having sinusoidal, random and complex random signals under several variants based on linear/nonlinear and primary/secondary paths. Statistical observations demonstrated the worth of stochastic solvers BSA and BSA-SQP by means of accuracy, convergence and complexity indices.
Zhongda Tian - One of the best experts on this subject based on the ideXlab platform.
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Backtracking Search optimization algorithm-based least square support vector machine and its applications
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Zhongda TianAbstract:Abstract Based on statistical learning theory, least square support vector machine can effectively solve the learning problem of small samples. However, the parameters of the least square support vector machine model have a great influence on its performance. At the same time, there is no clear theoretical basis for how to choose these parameters. In order to cope with the parameters optimization of the least square support vector machine, a Backtracking Search optimization algorithm-based least square support vector machine model is proposed. In this model, Backtracking Search optimization algorithm is introduced to optimize the parameters of the least square support vector machine. Meanwhile, the least square support vector machine model is updated by the prediction error combined with the sliding window strategy to solve the problem of mis-match between the prediction model and the actual sample data in the time-varying system. The performance of the proposed model is verified by classification and regression problems. The classification performance of the model is verified by five Benchmark datasets, and the regression prediction performance is verified by the dynamic liquid level of the oil production process. Compared with genetic algorithm, particle swarm optimization algorithm, and improved free Search algorithm optimized least square support vector machine, the simulation results show that the proposed model has higher classification accuracy with less computation time, and higher prediction accuracy and reliability for the dynamic liquid level. The proposed model is effective.
Jian Lin - One of the best experts on this subject based on the ideXlab platform.
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Backtracking Search based hyper heuristic for the flexible job shop scheduling problem with fuzzy processing time
Engineering Applications of Artificial Intelligence, 2019Co-Authors: Jian LinAbstract:Abstract Flexible job-shop scheduling problem (FJSP) is among the most investigated scheduling problems over the past decades. The uncertainty of the processing time is an important practical characteristic in manufacturing. By considering the processing time to be fuzzy variable, the FJSP with fuzzy processing time (FJSPF) is more close to the reality. This paper proposes an effective Backtracking Search based hyper-heuristic (BS-HH) approach to address the FJSPF. Firstly, six simple and efficient heuristics are incorporated into the BS-HH to construct a set of low-level heuristics. Secondly, a Backtracking Search algorithm is introduced as the high-level strategy to manage the low-level heuristics to operate on the solution domain. Additionally, a novel hybrid solution decoding scheme is proposed to find an optimal solution more efficiently. Finally, the performance of the BS-HH is evaluated on two typical benchmark sets. The results show that the proposed hyper-heuristic outperforms the state-of-the-art algorithms in solving the FJSPF.
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A Backtracking Search hyper-heuristic for the distributed assembly flow-shop scheduling problem
Swarm and Evolutionary Computation, 2017Co-Authors: Jian Lin, Zhou-jing WangAbstract:Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is recognized as an important class of problems in modern supply chains and manufacturing systems. In this paper, a Backtracking Search hyper-heuristic (BS-HH) algorithm is proposed to solve the DAPFSP. In the BS-HH scheme, ten simple and effective heuristic rules are designed to construct a set of low-level heuristics (LLHs), and the Backtracking Search algorithm is employed as the high-level strategy to manipulate the LLHs to operate on the solution space. Additionally, an efficient solution encoding and decoding scheme is proposed to generate a feasible schedule. The effectiveness of the BS-HH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed BS-HH scheme over the state-of-the-art algorithms.
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Oppositional Backtracking Search optimization algorithm for parameter identification of hyperchaotic systems
Nonlinear Dynamics, 2014Co-Authors: Jian LinAbstract:Parameter identification is an important issue in nonlinear science and has received increasing interest in the recent years. In this paper, an oppositional Backtracking Search optimization algorithm is proposed to solve the parameter identification of hyperchaotic system. The Backtracking Search optimization algorithm provides a new alternative for population-based heuristic Search. To increase the diversity of initial population and to accelerate the convergence speed, the opposition-based learning method is employed in the Backtracking Search optimization algorithm for population initialization as well as for generation jumping. Numerical simulations on several typical hyperchaotic systems are conducted to demonstrate the effectiveness and robustness of the proposed scheme.
Naveed Ishtiaq Chaudhary - One of the best experts on this subject based on the ideXlab platform.
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Design of Backtracking Search heuristics for parameter estimation of power signals
Neural Computing and Applications, 2020Co-Authors: Ammara Mehmood, Muhammad Asif Zahoor Raja, Aneela Zameer, Naveed Ishtiaq ChaudharyAbstract:This study presents a novel implementation of evolutionary heuristics through Backtracking Search optimization algorithm (BSA) for accurate, efficient and robust parameter estimation of power signal models. The mathematical formulation of fitness function is accomplished by exploiting the approximation theory in mean squared errors between actual and estimated responses, as well as, true and approximated decision variables. Variants of BSA-based meta-heuristics are applied for parameter estimation problem of power signals for identification of amplitude, frequency and phase parameters for different scenarios of noise variation. Analysis of performance evaluation for BSAs is conducted through exhaustive statistical observations in terms of mean weight deviation, root mean square error and Thiel inequality coefficient-based assessment metrics, as well as, ANOVA tests for statistical significance.
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Backtracking Search heuristics for identification of electrical muscle stimulation models using hammerstein structure
Applied Soft Computing, 2019Co-Authors: Ammara Mehmood, Aneela Zameer, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor RajaAbstract:Abstract The electrical muscle stimulation models (EMSMs) are effectively described through Hammerstein structure and are used to restore the functionality of paralyzed muscles after spinal cord injury (SCI). In the present study, global Search efficacy of evolutionary computing paradigm through Backtracking Search algorithm (BSA) is exploited for parameter estimation of EMSMs. The approximation theory in mean squared error sense is used for the construction of a merit function for EMSMs based on deviation between optimal and approximated parameters. Variants of BSA are designed based on memory size and population dynamics for the minimization problem of EMSMs having cubic spline as well as sigmoidal nonlinearities. Comparative studies by means of rigorous statistics establish the worth of scheme for effective, accurate, reliable, robust and stable identification of EMSMs in rehabilitation scenarios of SCI.
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Backtracking Search optimization heuristics for nonlinear hammerstein controlled auto regressive auto regressive systems
Isa Transactions, 2019Co-Authors: Ammara Mehmood, Aneela Zameer, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor RajaAbstract:Abstract In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global Search competency of Backtracking Search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel’s inequality coefficient as well as complexity measures.
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Backtracking Search integrated with sequential quadratic programming for nonlinear active noise control systems
Applied Soft Computing, 2018Co-Authors: Wasim Ullah Khan, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor RajaAbstract:Abstract In the present work, integrated strength of Backtracking Search algorithm (BSA) and sequential quadratic programming (SQP) is exploited for nonlinear active noise control (ANC) systems. Legacy of approximation theory in mean squared sense is utilized to construct a cost function for ANC system based on finite impulse response (FIR) and Volterra filtering procedures. Global Search efficacy of BSA aided with rapid local refinements with SQP is practiced for effective optimization of fitness function for ANC systems having sinusoidal, random and complex random signals under several variants based on linear/nonlinear and primary/secondary paths. Statistical observations demonstrated the worth of stochastic solvers BSA and BSA-SQP by means of accuracy, convergence and complexity indices.
Yiying Zhang - One of the best experts on this subject based on the ideXlab platform.
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Backtracking Search algorithm with specular reflection learning for global optimization
Knowledge-Based Systems, 2021Co-Authors: Yiying ZhangAbstract:Abstract Benefiting from population, randomness and simple structures, metaheuristic methods show excellent performance for solving global optimization problems. However, in some cases, in order to get promising solutions, the existing metaheuristic methods usually need to be modified. This work reports a new technique, called specular reflection learning, for improving the optimization performance of metaheuristic methods. Specular reflection learning is motivated by specular reflection phenomenon in physics. Note that, there is a close relationship between opposition-based learning and specular reflection learning. Opposition-based learning can be seen as a special case of specular reflection learning. In order to investigate the effectiveness of specular reflection learning, specular reflection learning is employed to improve Backtracking Search algorithm (BSA). The performance of the proposed Backtracking Search algorithm with specular reflection learning is evaluated by 88 test functions extracted from the well-known CEC 2013, CEC 2014 and CEC 2017 test suites, and two constrained engineering design problems. Experimental results confirm that specular reflection learning is a more effective technique for improving BSA compared with opposition-based learning, which establishes the foundation for the applications of specular reflection learning on other metaheuristics. In addition, the source code of this work can be found from https://www.mathworks.com/matlabcentral/fileexchange/79030-bsa_srl .
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Backtracking Search algorithm with Lévy flight for estimating parameters of photovoltaic models
Energy Conversion and Management, 2020Co-Authors: Yiying Zhang, Zhigang Jin, Xiaofang Zhao, Qiuling YangAbstract:Abstract An accurate mathematical model plays an important role for simulation, evaluation and optimization of photovoltaic (PV) models. The characteristic current equations describing the PV models are implicit, nonlinear and transcendental. Given the features of the characteristic current equations, traditional optimization algorithms are usually easy to converge to local optimal solutions. Thus using metaheuristic methods called modern optimization algorithms to estimate parameters of PV models has been a reSearch hotspot in recent years. Although many metaheuristic methods have been employed to solve this problem, it is still necessary for reSearchers to propose new optimization algorithms to obtain more accuracy and reliability solutions. This paper presents a new metaheuristic algorithm called Backtracking Search algorithm with Levy flight (LFBSA) to estimate the parameters of PV models. Compared with the basic Backtracking Search algorithm (BSA), LFBSA has the following two remarkable features. Firstly, an information sharing mechanism with Levy flight is built to enhance population diversity. Secondly, mutation operator based on the hunting mechanism of grey wolves is introduced to increase the chance of LFBSA to escape from local minima. LFBSA is used to estimate parameters of three different PV models. Experimental results show the proposed LFBSA is superior to BSA and the other compared algorithms in terms of accuracy and reliability.
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Backtracking Search algorithm with reusing differential vectors for parameter identification of photovoltaic models
Energy Conversion and Management, 2020Co-Authors: Yiying Zhang, Caifeng Huang, Zhigang JinAbstract:Abstract In order to simulate, control and optimize photovoltaic (PV) systems, how to accurately identify the unknown parameters of PV models is a major challenge. To overcome this challenge, this work reports a very simple but efficient optimization method called Backtracking Search algorithm with reusing differential vectors (BSARDVs). BSARDVs has a very simple structure and only needs the essential population size and stopping criterion for optimization. Mutation operator is employed to generate new individuals in the Search process of Backtracking Search algorithm (BSA), which guides the Search direction of population by the differential vectors between history population and current population. To enhance the global Search ability of BSA, BSARDVs first archives some most promising difference vectors from history population and then reuses these differential vectors for generating next generation population. The performance of BSARDVs is investigated for parameter identification of three PV models, i.e. single diode model, double diode model and PV module model. Experimental results reveal BSARDVs can find the better solution than the compared algorithms on double diode model. In addition, for single diode model and PV module model, the solutions of BSARDVs are the same solutions with those of some compared algorithms while BSARDVs consumes less function evaluations than these algorithms. This proves the effectiveness of reusing differential vectors in BSA for parameter identification of PV models.
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Backtracking Search algorithm with competitive learning for identification of unknown parameters of photovoltaic systems
Expert Systems with Applications, 2020Co-Authors: Yiying Zhang, Zhigang JinAbstract:Abstract Metaheuristic algorithms have been successfully used to parameter identification of photovoltaic systems. However, this still faces the following two challenges. Firstly, most of the applied algorithms are complex and need some extra control parameters except the essential population size and stopping criterion, which is against their applications in photovoltaic systems with different characteristics. Secondly, how to obtain model parameters with higher accuracy and reliability has been a very valuable topic. To address the two challenges, this paper presents a new optimization method called Backtracking Search algorithm with competitive learning (CBSA) for parameter identification of photovoltaic systems. The remarkable features of CBSA are that it has a very simple structure and only needs the essential parameters. The core idea of CBSA is to increase the chance of Backtracking Search algorithm (BSA) to jump out of the local optimum by the designed competitive learning mechanism. In CBSA, the population is first divided into two subpopulations by built competitive mechanism. Then each subpopulation is optimized by the different Search operators with multiple learning strategies. In order to test the performance of CBSA, CBSA is first employed to solve five challenging engineering design optimization problems and then is used to estimate the unknown parameters of three photovoltaic models. Experimental results show the solutions offered by CBSA outperform those of the compared algorithms including BSA, two recently proposed variants of BSA and other some state-of-the-art algorithms on nearly all test problems, which proves the effectiveness of the improved strategies.
