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bat algorithm for dam-reservoir operation

ENVIRONMENTAL EARTH SCIENCES 2018年第13期77卷 510页

作者： Ethteram, Mohammad Mousavi, Sayed-Farhad Karami, Hojat Farzin, Saeed Deo, Ravinesh Othman, Faridah Binti Chau, Kwok-wing Sarkamaryan, Saeed Singh, Vijay P. El-Shafie, Ahmed Semnan Univ Dept Water Engn & Hydraul Struct Fac Civil Engn Semnan Iran Univ Southern Queensland Sch Agr Computat & Environm Sci Inst Agr & Environm Toowoomba Qld Australia Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Chamran Univ Dept Water Engn & Hydraul Struct Fac Civil Engn Ahvaz Iran Texas A&M Univ Dept Biol & Agr Engn Zachry Dept Civil Engn College Stn TX USA Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur Malaysia

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, bat algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.

关键词： Water supply Hydropower generation Optimization models Reservoir operation Rule curve bat algorithm

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bat algorithm for constrained optimization tasks

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NEURAL COMPUTING & APPLICATIONS 2013年第6期22卷 1239-1255页

作者： Gandomi, Amir Hossein Yang, Xin-She Alavi, Amir Hossein Talatahari, Siamak Islamic Azad Univ Cent Tehran Branch Young Researchers Club Tehran Iran Natl Phys Lab Teddington TW11 0LW Middx England Islamic Azad Univ Mashhad Branch Young Researchers Club Mashhad Iran Univ Tabriz Marand Fac Engn Tabriz Iran

In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.

关键词： bat algorithm Constraint optimization Metaheuristic algorithm

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bat algorithm assisted by ordinal optimization for solving discrete probabilistic bicriteria optimization problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2019年 166卷 346-364页

作者： Horng, Shih-Cheng Lin, Shieh-Shing Chaoyang Univ Technol Dept Comp Sci & Informat Engn 168 Jifong E Rd Taichung 41349 Taiwan St Johns Univ Dept Elect Engn Taipei Taiwan

The discrete probabilistic bicriteria optimization problem (DPBOP) is a discrete optimization problem with probabilistic criteria which should be optimized simultaneously. The DPBOP belongs to a class of NP-hard problems because the computing time increases much faster when the size of the solution space increases. To solve the DPBOP efficiently, an algorithm that used bat algorithm (BA) assisted by ordinal optimization (OO), abbreviated as BAOO, is proposed to determine an outstanding solution within an acceptable time. The BAOO algorithm comprises three parts, surrogate model, exploration and exploitation. In surrogate model, the support vector regression is utilized as a fitness evaluation of a solution. In exploration, an amended bat algorithm is adopted to select N superior solutions from the whole solution space. In exploitation, an intensified optimal computing budget allocation scheme is adopted to decide an outstanding solution from the N superior solutions. The above three parts substantially decrease the required computing overhead of DPBOP. Finally, the BAOO algorithm is applied to a facility-sizing optimization problem in factory, which is formulated as a DPBOP. Three different size problems are considered as test examples. The BAOO algorithm is compared with three general optimization methods, particle swarm optimization, genetic algorithm and evolutionary strategy. Experimental results illustrate that the BAOO algorithm yields an outstanding solution with a higher quality and efficiency than three general optimization methods. (C) 2019 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Probabilistic bicriteria optimization bat algorithm Ordinal optimization Surrogate model Support vector regression Optimal computing budget allocation Facility-sizing optimization Installation cost Risk-level

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bat algorithm based feature selection: Application in credit scoring

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期41卷 5561-5570页

作者： Tripathi, Diwakar Reddy, B. Ramachandra Reddy, Y. C. A. Padmanabha Shukla, Alok Kumar Kumar, Ravi Kant Sharma, Neeraj Kumar Thapar Inst Engn & Technol Patiala Patiala 147001 Punjab India SRM Univ AP Andhra Pradesh Amaravati India Madanapalle Inst Technol & Sci Madanapalle Madanapalle Andhra Pradesh India VIT AP Univ Amaravati Andhra Pradesh India

Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants' credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive performance of credit scoring model. Towards to feature selection, Binary bat optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with "Radial Basis Function Neural Network (RBFN)", "Support Vector Machine (SVM)" and "Random Forest (RF)" for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring.

关键词： bat algorithm credit score feature selection

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bat algorithm-based weighted Laplacian probabilistic neural network

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NEURAL COMPUTING & APPLICATIONS 2020年第4期32卷 1157-1171页

作者： Naik, Shraddha M. Jagannath, Ravi Prasad K. Kuppili, Venkatnareshbabu Natl Inst Technol Goa Dept Comp Sci & Engn Farmagudi Goa India Natl Inst Technol Goa Dept Appl Sci Farmagudi Goa India

Probabilistic neural network (PNN) is a single-pass feed-forward neural network with the capability of providing nonlinear decision boundaries. In this work, we propose the modifications to the existing PNN approach. Contributions are threefold: First, symmetric Laplace distribution has been used instead of Gaussian distribution in the pattern layer of the PNN approach. Second, a new weight coefficients' estimation method has been introduced between the pattern and summation layers of PNN. Third, a novel convex fitness function has been designed for the bat algorithm to obtain an optimal smoothing parameter vector. The designed fitness function maintains a balance between the sensitivity and specificity values. The performance of the proposed algorithm "bat algorithm-based weighted Laplacian probabilistic neural network" is compared with that of conventional PNN, weighted PNN, extreme learning method, optimal pruned extreme learning machine and K-nearest neighbor. Experimental evaluation is carried out on eleven benchmark data sets using different performance measures such as accuracy, sensitivity, specificity and Youden's index. The comparative performance evaluation of the proposed model has been made with the PNN, and other standard algorithms outperform the compared approaches in terms of classification accuracy. Friedman test is used for statistical evaluation of the proposed approach.

关键词： Probabilistic neural network Symmetric Laplace distribution bat algorithm Classification

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bat algorithm with Weibull walk for solving global optimisation and classification problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第3期15卷 159-170页

作者： Rauf, Hafiz Tayyab Hadi, Muhammad Rehman, Abdur Univ Gujrat Dept Comp Sci Gujrat Pakistan

bat algorithm (BA) becomes the most widely employed meta-heuristic algorithm to interpret the diverse kind of optimisation and real-world classification problems. BA suffers from one of the influential challenges called local minima. In this study, we carry out two modifications in the original BA and proposed a modified variant of BA called bat algorithm with Weibull walk (WW-BA) to solve the premature convergence issue. The first modification involves the introduction of Weibull descending inertia weight for updating the velocity of bats. The second modification approach updates the local search strategy of BA by replacing the Random walk with the Weibull Walk. The simulation performed on 19 standard benchmark functions represent the competence and effectiveness of WW-BA compared to the state of the art techniques. The proposed WWBA is also examined for classification problem. The empirical results reveal that the proposed technique outperformed the classical techniques.

关键词： bat algorithm premature convergence exploration exploitation Weibull walk inertia weight

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bat algorithm for multi-objective optimisation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2011年第5期3卷 267-274页

作者： Yang, Xin-She Univ Cambridge Dept Engn Cambridge CB2 1PZ England Natl Phys Lab Teddington TW11 0LW Middx England

Engineering optimisation is typically multi-objective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimisation algorithms. Recently, Xin-Shc Yang proposed a bat-inspired algorithm for solving non-linear, global optimisation problems. In this paper, we extend this algorithm to solve multi-objective optimisation problems. The proposed multi-objective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multi-objective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.

关键词： bat algorithm BA cuckoo search firefly algorithm optimisation multi-objective optimisation

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bat algorithm: literature review and applications

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2013年第3期5卷 141-+页

作者： Yang, Xin-She He, Xingshi Middlesex Univ Sch Sci & Technol London NW4 4BT England Xian Polytech Univ Sch Sci Xian 710048 Peoples R China

bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last three years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarised briefly here. In addition, we also discuss the essence of an algorithm and the links between algorithms and self-organisation. Further research topics are also discussed.

关键词： algorithm bat algorithm cuckoo search firefly algorithm eagle strategy nature-inspired algorithm optimisation metaheuristics

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bat algorithm-based beamforming for mmWave massive MIMO systems

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第2期33卷 e4182-e4182页

作者： Shahjehan, Waleed Riaz, Asad Khan, Imran Sadiq, Ali Safaa Khan, Suleman Khan, Muhammad Khurram Univ Engn & Technol Dept Elect Engn Peshawar Pakistan Univ Wolverhampton Sch Math & Comp Sci Wolverhampton Cyber Res Inst Wulfruna St Wolverhampton WV1 1LY W Midlands England Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England King Saud Univ Ctr Excellence Informat Assurance Riyadh Saudi Arabia

In this paper, an optimized analog beamforming scheme for millimeter-wave (mmWave) massive MIMO system is presented. This scheme aims to achieve the near-optimal *** searching for the optimized combination of analog precoder and combiner. In order to compensate for the occurrence of attenuation in the magnitude of mmWave signals, the codebook-dependent analog beamforming in conjunction with precoding at transmitting end and combining signals at the receiving end is utilized. Nonetheless, the existing and traditional beamforming schemes involve a more difficult and complicated search for the optimal combination of analog precoder/combiner matrices from predefined codebooks. To solve this problem, we have referred to a modified bat algorithm to find the optimal combination value. This algorithm will explore the possible pairs of analog precoder/combiner as a way to come up with the best match in order to attain near-optimal performance. The analysis shows that the optimized beamforming scheme presented in this paper can improve the performance that is very close to the beam steering benchmark that we have considered.

关键词： bat algorithm beamforming optimization massive MIMO millimeter-wave

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bat algorithm: a novel approach for global engineering optimization

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ENGINEERING COMPUTATIONS 2012年第5-6期29卷 464-483页

作者： Yang, Xin-She Gandomi, Amir Hossein Natl Phys Lab Teddington TW11 0LW Middx England Tafresh Univ Dept Civil Engn Tafresh Iran

Purpose - Nature-inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature-inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks. Design/methodology/approach - The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature. Findings - BA has been carefully implemented and carried out optimization for eight well-known optimization tasks;then a comparison has been made between the proposed algorithm and other existing algorithms. Originality/value - The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.

关键词： Iterative methods Programming and algorithm theory Optimization techniques bat algorithm Engineering optimization Metaheuristic algorithm

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