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Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm

NEURAL COMPUTING & APPLICATIONS 2019年第6期31卷 1747-1763页

作者： Chatzipavlis, Antonios Tsekouras, George E. Trygonis, Vasilis Velegrakis, Adonis F. Tsimikas, John Rigos, Anastasios Hasiotis, Thomas Salmas, Constantinos Univ Aegean Dept Marine Sci Univ Hill Mitilini 81100 Greece Univ Aegean Dept Cultural Technol & Commun Univ Hill Mitilini 81100 Greece Univ Aegean Dept Math Karlovassi 83200 Samos Greece Univ Ioannina Dept Mat Sci & Engn GR-45110 Ioannina Greece

Beach realignment is caused by sediment movement across and along the beach due to the ever-changing incident waves. Its driving mechanisms are highly nonlinear and cannot be easily resolved by numerical process-response modeling which may also involve high computational costs. In this contribution, an alternative approach is developed to cope with the nonlinearity of beach realignment, which utilizes a neuro-fuzzy neural network optimized by a novel backtracking search algorithm. The network comprises multiple layers operating in sequence and establishes input-output relationships in terms of first-order fuzzy rules. The proposed backtracking search algorithm improves its performance by effectively modifying the existing mutation and crossover operations of the standard algorithm. A novel experimental setup was deployed in a touristic beach of Santorini island, Greece, to collect high frequency morphological and hydrodynamic information and generate a data set described by few representative input variables. Three additional networks were designed using the same data set. Standard criteria and nonparametric statistical analysis showed that the proposed approach outperforms all other tested methods in the case of modeling beach realignment, achieving also a significant improvement over previous efforts.

关键词： backtracking search algorithm Neuro-fuzzy network Shoreline realignment Beach morphodynamics

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CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2016年第4期28卷 719-749页

作者： Mallick, S. Kar, R. Mandal, D. Ghoshal, S. P. NIT Durgapur Dept Elect & Commun Engn Durgapur W Bengal India NIT Durgapur Dept Elect Engn Durgapur W Bengal India

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm backtracking search algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSADE- based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

关键词： CMOS circuit sizing two-stage operational amplifier differential amplifier backtracking search algorithm differential evolution evolutionary optimisation techniques

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Performance up-gradation of Symbiotic Organisms search by backtracking search algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第12期13卷 5505-5546页

作者： Nama, Sukanta Saha, Apu Kumar Sharma, Sushmita Maharaja Bir Bikram Univ Dept Appl Math Agartala Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India

Symbiotic Organisms search (SOS) algorithm is characterized based on the framework of relationships among the ecosystem species. Nevertheless, it is suffering from wasteful discovery, little productivity, and slack convergence rate. These deficiencies cause stagnation at the local optimum, which is hazardous in deciding the genuine optima of the optimization problem. backtracking search algorithm (BSA) is likewise another streamlining method for comprehending the non-direct complex optimization problem. Consequently, in the current paper, an endeavor has been made toward the expulsion of the downsides from the traditional SOS by proposing a novel ensemble technique called e-SOSBSA to overhaul the degree of intensification and diversification. In e-SOSBSA, firstly, the mutation operator of BSA with the self-adaptive mutation rate is incorporated to produce a mutant of population and leap out from the local optima. Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously. The suggested e-SOSBSA has been tested with 20 classical benchmark functions, IEEE CEC2014, CEC2015, CEC2017, and the latest CEC 2020 test functions. Statistical analyses, convergence analysis, and diversity analysis are performed to show the stronger search capabilities of the proposed e-SOSBSA in contrast with the component algorithms and several state-of-the-art algorithms. Moreover, the proposed e-SOSBSA is applied to find the optimum value of the seven problems of engineering optimization. The numerical investigations and examinations show that the proposed e-SOSBSA can be profoundly viable in tackling real-world engineering optimization problems.

关键词： Symbiotic Organisms search backtracking search algorithm Ensemble algorithm Function optimization CEC2014 CEC2015 CEC2017 CEC2020 Engineering Problem

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A backtracking search algorithm for the Simultaneous Size, Shape and Topology Optimization of Trusses

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2016年第15期13卷 2622-2651页

作者： Souza, Rafael R. Fadel Miguel, Leandro F. Iopez, Rafael H. Torii, Andre J. Miguel, Leticia F. F. Univ Fed Santa Catarina CORE R JoaoPio Duarte Sn BR-88034000 Florianopolis SC Brazil RuaCasteloBranco BR-55051900 Joao Pessoa PB Brazil Av SarmentoLeite 425 2 Andar BR-90050170 Porto Alegre RS Brazil

This paper presents a backtracking search Optimization algorithm (BSA) to simultaneously optimize the size, shape and topology of truss structures. It focuses on the optimization of these three aspects since it is well known that the most effective scheme of truss optimization is achieved when they are simultaneously considered. The minimization of structural weight is the objective function, imposing displacement, stress, local buckling and/or kinematic stability constraints. The effectiveness of the BSA at solving this type of optimization problem is demonstrated by solving a series of benchmark problems comparing not only the best designs found, but also the statistics of 100 independent runs of the algorithm. The numerical analysis showed that the BSA provided promising results for the analyzed problems. Moreover, in several cases, it was also able to improve the statistics of the independent runs such as the mean and coefficient of variation values.

关键词： backtracking search algorithm truss optimization topology optimization size optimization shape optimization mixed variable problem

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Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm

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JOURNAL OF CLEANER PRODUCTION 2017年 144卷 228-238页

作者： Lu, Chao Gao, Liang Li, Xinyu Pan, Quanke Wang, Qi Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Permutation flow shop scheduling problems (PFSPs) have been extensively studied because of its broad industrial applications. However, setup and transportation time are usually ignored in most research, which causes a huge gap between the theoretical research and practical application. Meanwhile, energy saving has attracted growing attention due to the advent of sustainable manufacturing. Thus, we investigate an energy-efficient PFSP with sequence-dependent setup and controllable transportation time from a real-world manufacturing enterprise. First of all, a novel multi-objective mathematical model considering both makespan and energy consumption is formulated based on a comprehensive investigation. Then, a hybrid multi-objective backtracking search algorithm (HMOBSA) is proposed to solve this problem. Furthermore, a new energy saving scenario is developed to simultaneously ensure the service span of machines and energy saving. Finally, to evaluate the effectiveness of the proposed HMOBSA and energy saving scenario, we compare our proposal with other two famous multi-objective algorithms including NSGA-II and MOEA/D by conducting a real-world case study. The experimental results indicate that the proposed HMOBSA is superior to NSGA-II and MOEA/D for this case. Additionally, the proposed energy saving scenario also outperforms its competitors. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Energy efficiency Permutation flow shop scheduling Controllable transportation time backtracking search algorithm Multi-objective optimization

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A Bio-Inspired Multi-Population-Based Adaptive backtracking search algorithm

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COGNITIVE COMPUTATION 2022年第2期14卷 900-925页

作者： Nama, Sukanta Saha, Apu Kumar MBB Univ Dept Math Agartala Tripura India Natl Inst Technol Agartala Dept Math Agartala Tripura India

backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm's performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm's execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.

关键词： backtracking search algorithm Mutation strategies Multi-population strategy CEC2014 CEC2017

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Solving non-convex economic dispatch problem via backtracking search algorithm

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ENERGY 2014年 77卷 372-381页

作者： Modiri-Delshad, Mostafa Abd Rahim, Nasrudin Wisma R&D Univ Malaya UM Power Energy Dedicated Adv Ctr UMPE DAC Kuala Lumpur 59990 Malaysia King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

This paper presents BSA (backtracking search algorithm) for solving of ED (economic dispatch) problems (both convex and non-convex) with both the valve-point effects in the generator cost function and the transmission network loss considered. BSA is a new evolutionary algorithm for solving of numerical optimization problems;it uses a single control parameter and two crossover and mutation strategies for powerful exploration of the problem's search space. Four test systems (with 3, 6, 20, and 40 generators) are the case studies verifying the method's robustness and effectiveness. The results confirm that compared with existing well-known methods and especially in large-scale test systems, the proposed algorithm is the better approach to solving ED problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch backtracking search algorithm Valve point effect Transmission loss Non-convex Evolutionary

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A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2014年第21期39卷 11165-11174页

作者： Askarzadeh, Alireza Coelho, Leandro dos Santos Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn BR-80060000 Curitiba Parana Brazil

Polarization curves are vital tools for simulation, evaluation, management, and optimization of proton exchange membrane fuel cells (PEMFCs). They are affected by various parameters related to the nonlinearities associated with the electrochemical processes governing fuel cells (FCs). However, the relative importance and effect of each parameter on the polarization curve is different. In order to efficiently estimate the unknown parameters of the PEMFC electrochemical-based model and obtain accurate solutions, in this paper, a backtracking search algorithm combined with Burger's chaotic map (BSABCM) is proposed and investigated. The results related to two real systems, the SR-12 Modular PEM Generator and the Ballard Mark V FC, illustrate the effectiveness of the proposed methodology. Furthermore, the BSABCM presented competitive results when compared with the results of the other metaheuristics. Copyright (C) 2014, Hydrogen Energy Publications, LLC: Published by Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cells backtracking search algorithm Parameter estimation

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An application of backtracking search algorithm in designing power system stabilizers for large multi-machine system

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NEUROCOMPUTING 2017年 237卷 175-184页

作者： Islam, Naz Niamul Hannan, M. A. Shareef, Hussain Mohamed, Azah Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Elect Power Engn Kajang 43000 Selangor Malaysia United Arab Emirates Univ Dept Elect Engn F-1 BldgLevel 1 Coll EngnPOB 15551 Al Ain U Arab Emirates

This paper deals with the backtracking search algorithm (BSA) optimization technique to solve the design problems of multi-machine power system stabilizers (PSSs) in large power system. Power system stability problem is formulated by an optimization problem using the LTI state space model of the power system. To conduct a comprehensive analysis, two test systems (2-AREA and 5-AREA) are considered to explain the variation of design performance with increase in system size. Additionally, two metaheuristic algorithms, namely bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) are accounted to evaluate the overall design assessment. The obtained results show that BSA is superior to find consistent solution than BFOA and PSO regardless of system size. The damping performance that achieved from both test systems are sufficient to achieve fast system stability. System stability in linearized model is ensured in terms of eigenvalue shifting towards stability regions. On the other hand, damping performance in the non-linear model is evaluated in terms of overshoot and setting times. The obtained damping in both test systems ate stable for BSA based design. However, BFOA and PSO based design perform worst in case of large power system. It is also found that the performance of BSA is not affected for large numbers of parameter optimization compared to PSO, and BFOA optimization techniques. This unique feature encourages recommending the developed backtracking search algorithm for PSS design of large multi-machine power system.

关键词： Power system stability Power system oscillations Power system damping Power system stabilizer backtracking search algorithm Multi-machine power system

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Dynamic reconfiguration of active distribution network based on improved backtracking search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 1469-1480页

作者： Li, Wenying Guo, Qinghong Wen, Ming Zhang, Yun Pan, Xin Xiao, Zhenfeng Yang, Shuzhi State Grid Hunan Prov Elect Power Co Econ & Technol Res Inst Changsha Peoples R China Hunan Prov Key Lab Energy Internet Supply & Dema Changsha Peoples R China State Grid Hunan Prov Elect Power Co Changsha Peoples R China

This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method is validated by an IEEE 30-node simulation distributed system.

关键词： Active distribution network distributed power sources demand-side response dynamic reconfiguration backtracking search algorithm

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