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A novel hybrid bat algorithm with a fast clustering-based hybridization

EVOLUTIONARY INTELLIGENCE 2020年第3期13卷 427-442页

作者： Eskandari, Sadegh Javidi, Mohammad Masoud Univ Guilan Fac Math Sci Rasht Iran Shahid Bahonar Univ Kerman Kerman Iran

bat algorithm (BA) is a new and promising metaheuristic search algorithm which could outperform existing algorithms. However, BA can be easily trapped in a local optimum regarded to low exploration ability. The present study proposed a new local-search-based hybrid heuristic to escape such scenario. The proposed hybrid BA (hBA) uses a clustering-based hybridization method which detects the early convergence of BA population by analyzing similarities among individuals. The main motivation for such an analysis is that when BA is continually converging, the similarity among individuals becomes higher. The proposed hBA is extensively evaluated on CEC2017 benchmark suite. The Experiments demonstrate that the algorithm achieves better results than continues variants of BA in every way. Moreover, as a case study, a binary version of the proposed hBA (hBBA) is applied to the well-known feature selection problem. The recorded results on 13 datasets demonstrate that hBBA would be considered as a new state-of-art in metaheuristic-based wrapper feature selection methods.

关键词： bat algorithm Metaheuristic search Nature inspired search Feature selection

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Research on Unmanned Vehicle Path Planning based on Improved bat algorithm

Research on Unmanned Vehicle Path Planning based on Improved...

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第二十一届分布式计算及其应用国际学术研讨会

作者： Yizhu Jiang Yefu Wu Miao Wang School of Computer and Artificial Intelligence Hubei Key Laboratory of Transportation Internet of ThingsWuhan University of Technology Chongqing Guoyuan Port Co. Ltd

Vehicle driving technology has developed in the direction of informatization and intelligence,and the research hotspot is unmanned driving and path planning .This paper proposes an improved bat algorithm（Improved bat algorithm,IBA）.First,a weighted speed update mechanism is introduced in BA,so that with the increase of the number of iterations,the speed changes adaptively to avoid the algorithm falling into local extreme values ***,in the subsequent iterations,the quadratic differential change mechanism is used to maintain the diversity of the bat population;and under the static road network,the optimal route is planned throu gh the MATLAB simulation experiment,which verifies that the IBA algorithm has better performance.

关键词： Unmanned vehicle Path planning bat algorithm

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Improved chaotic bat algorithm and its application in multi-objective operation of cascade reservoirs considering different ecological flow requirements

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ENVIRONMENTAL EARTH SCIENCES 2021年第21期80卷 709-709页

作者： Su, Lyuwen Yang, Kan Jiangsu Hydraul Res Inst Nanjing 210017 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China

With growing concerns on renewable energy and environment, the multi-objective operation (MOO), which considering the economic benefits and ecological benefits, becomes an important optimization problem. To handle this problem, a new multi-objective optimization approach named improved chaotic bat algorithm (ICBA) is proposed in this paper. In ICBA, chaos theory is used to generate initial population and update pulse emission rate to improve population diversity. The self-adaptive loudness update mechanism is designed to control the convergence speed according to the iterations process. Furthermore, the Montana Method with seasonal variation is proposed to calculate downstream ecological flow. The feasibility and effectiveness of the proposed ICBA method are demonstrated by the simulations of the Qingjiang cascade reservoirs in different hydrological years. Four scenarios are set up to compare the power generation results and the downstream ecological flow satisfaction rate under different ecological flow requirements. The results show that average annual operation schemes obtained by the ICBA can meet the minimum and suitable ecological flow requirements. Compared to the scenario 1 (optimization goal only consider the power generation requirement), the scenario 4 (optimization goal consider both power generation and ideal ecological flow requirement) proposed in this paper can improve the satisfaction rate of ideal ecological flow requirement, and has little influence on the average annual power generation. As compared with other several algorithms, the ICBA can obtain better operation results in different hydrological years and provide a new effective tool for designing reasonable operation schemes of cascade reservoirs.

关键词： bat algorithm Chaos Multi-objective operation Self-adaptive

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A discrete bat algorithm based on Levy flights for Euclidean traveling salesman problem

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 172卷 114639-114639页

作者： Saji, Yassine Barkatou, Mohammed Chouaib Doukkali Univ Fac Sci Dept Comp Sci El Jadida Morocco Chouaib Doukkali Univ Fac Sci Lab Innovat Sci Technol & Modeling El Jadida Morocco

bat algorithm is a swarm-intelligence-based metaheuristic proposed in 2010. This algorithm was inspired by echolocation behavior of bats when searching their prey in nature. Since it first introduction, it continues to be used extensively until today, owing to its simplicity, easy handling and applicability to a wide range of problems. However, sometimes the major challenge faced by this technique is can be trapped in a local optimum when facing large complex problems. In this research work, a new discrete bat algorithm is proposed to solve the famous traveling salesman problem as NP-hard combinatorial optimization problem. To enhance the searching strategy and to avoid getting stuck in local minima, random walks based on Le & acute;vy's flights are combined with bat's movement. In addition, to improve the diversity and convergence of the swarm, a neutral crossover operator is embedded to the proposed algorithm. To evaluate the performance of our proposal, two experiments are conducted on 38 benchmark datasets and the obtained results are compared with eight different approaches. Furthermore, the student's t-test, the Friedman's test and the post hoc Wilcoxon's test are performed to check whether there are significant differences between the proposed optimizer and the alternative techniques. The experimental results under comparative studies have shown that, in most cases, the proposed discrete bat algorithm yields significantly better results compared with its competitors.

关键词： bat algorithm Traveling salesman problem NP-hard combinatorial optimization problem Population-based metaheuristics Levy flights

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Optimal control parameters for bat algorithm in maximum power point tracker of photovoltaic energy systems

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第4期31卷

作者： Eltamaly, Ali M. King Saud Univ Sustainable Energy Technol Ctr Riyadh 11421 Saudi Arabia Mansoura Univ Dept Elect Engn Mansoura Egypt KA CARE Energy Res & Innovat Ctr Riyadh 11451 Saudi Arabia

Partial shading conditions generate multiple peaks in the P-V curve of photovoltaic (PV) arrays. Smart MPPT optimization techniques should be used to capture the global peak (GP) and avoid being trapped in one of the local peaks (LPs). The tracking of the GP should be fast and reliable to enhance the stability and increase the generated efficiency of the PV systems. bat algorithm (BA) is one of the fastest swarm optimization techniques. The BA control parameters (BA-CPs) have substantial effects on their performance. This paper introduced a nested BA strategy called BA-BA strategy to determine the optimal values of control parameters of BA for the lowest convergence time and failure convergence rate to be used in the online MPPT of PV systems. The inner BA loop used the BA as an MPPT of the PV system, meanwhile, the outer BA loop used the inner BA loop as a fitness function to determine the optimal BA-CPs for minimum convergence time and failure rate. Ten benchmark BA strategies, particle swarm optimization (PSO), and cuckoo search (CS) algorithm have been used to compare their results with the results obtained from the BA-BA strategy. The results of the BA-BA strategy reduced the convergence time of 250% of the time associated with the best benchmark BA strategy, 518%, and 395% as compared to the PSO, and CS algorithm, respectively. The simulation and experimental results obtained from the BA-BA strategy showed its superior for determining the optimal control parameters for BA in MPPT of PV systems or any other applications.

关键词： bat algorithm MPPT optimal control parameters optimization photovoltaic

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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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A novel discrete bat algorithm for heterogeneous redundancy allocation of multi-state systems subject to probabilistic common-cause failure

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 208卷 107338-107338页

作者： Xu, Yue Pi, Dechang Yang, Shengxiang Chen, Yang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

This paper focuses on a heterogeneous redundancy allocation problem (RAP) for multi-state series-parallel systems subject to probabilistic common-cause failure and proposes a novel discrete bat algorithm to solve it. Although abundant research studies have been published for solving multi-state RAPs, few of them have studied probabilistic common cause failure, which motivates this paper. Due to the insufficient data of components, an interval-valued universal generating function is utilized to evaluate the availability of components and the whole system. The challenge of solving this kind of RAPs lies in not only the reliability estimation, but also the solution method. This paper presents a novel discrete bat algorithm (BA) for effectively dealing with the proposed RAP and alleviating the premature convergence of BA. Two main features of the adaptation are Hamming distance-based bat movement (HDBM) and Q learning-based local search (QLLS). HDBM transfers the Hamming distance between the current bat and the best bat in the swarm to the movement rate. Then, QLLS utilizes Q-learning to adjust the local search strategies dynamically during the iterations. The computational results from extensive experiments demonstrate that the proposed algorithm is powerful, which is more efficient than other state-of-the-arts on this sort of problems.

关键词： Heterogeneous redundancy allocation problem Multi-state system Probabilistic common-cause failure bat algorithm Hamming distance Q learning Local search

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Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with bat algorithm

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WATER 2021年第3期13卷 256-256页

作者： Dong, Liming Zeng, Wenzhi Wu, Lifeng Lei, Guoqing Chen, Haorui Srivastava, Amit Kumar Gaiser, Thomas Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Nanchang Inst Technol Nanchang 330099 Jiangxi Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Univ Bonn Inst Crop Sci & Resource Conservat INRES Crop Sci Grp Katzenburgweg 5 D-53115 Bonn Germany

Accurate estimation of pan evaporation (E-p) is vital for the development of water resources and agricultural water management, especially in arid and semi-arid regions where it is restricted to set up the facilities and measure pan evaporation accurately and consistently. Besides, using pan evaporation estimating models and pan coefficient (kp) models is a classic method to assess the reference evapotranspiration (ET0) which is indispensable to crop growth, irrigation scheduling, and economic assessment. This study estimated the potential of a novel hybrid machine learning model Coupling bat algorithm (bat) and Gradient boosting with categorical features support (CatBoost) for estimating daily pan evaporation in arid and semi-arid regions of northwest China. Two other commonly used algorithms including random forest (RF) and original CatBoost (CB) were also applied for comparison. The daily meteorological data for 12 years (2006-2017) from 45 weather stations in arid and semi-arid areas of China, including minimum and maximum air temperature (T-min, T-max), relative humidity (RH), wind speed (U), and global solar radiation (R-s), were utilized to feed the three models for exploring the ability in predicting pan evaporation. The results revealed that the new developed bat-CB model (RMSE = 0.859-2.227 mm center dot d(-1);MAE = 0.540-1.328 mm center dot d(-1);NSE = 0.625-0.894;MAPE = 0.162-0.328) was superior to RF and CB. In addition, CB (RMSE = 0.897-2.754 mm center dot d(-1);MAE = 0.531-1.77 mm center dot d(-1);NSE = 0.147-0.869;MAPE = 0.161-0.421) slightly outperformed RF (RMSE = 1.005-3.604 mm center dot d(-1);MAE = 0.644-2.479 mm center dot d(-1);NSE = -1.242-0.894;MAPE = 0.176-0.686) which had poor ability to operate the erratic changes of pan evaporation. Furthermore, the improvement of bat-CB was presented more comprehensively and obviously in the seasonal and spatial performance compared to CB and RF. Overall, bat-CB has high accuracy, robust stability, and

关键词： pan evaporation machine learning bat algorithm CatBoost random forest

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A hybrid PCG-bat algorithm for 2D gravity inversion: Applications for ore deposits exploration and interpretation of sedimentary basins

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ORE GEOLOGY REVIEWS 2021年第PartA期139卷 104497-104497页

作者： Gobashy, Mohamed Abdelazeem, Maha Abdrabou, Mohamed Khalil, Mohamed H. Cairo Univ Fac Sci Geophys Dept Giza Egypt Natl Res Inst Astron & Geophys NRIAG Cairo Egypt

Potential field data plays a vital role in geophysical exploration especially for mineral and hydrocarbons. Inversion of such static fields, like gravity and magnetic is a proper approach to delineate the subsurface distribution of the physical property. In ore deposits prospection, studying the subsurface distributions of densities is of critical importance. A proper strategy is to subdivide the subsurface 2D model space into rectangular mesh of prisms of different densities. The problem is highly ill-posed. To solve for the unknown densities, a new hybrid scheme, using bat algorithm (BA) with the preconditioned conjugate gradient (PCG) technique, has been applied. The scheme benefits from the advantages of both techniques. The bat algorithm acts as a rapid build-up of the model, while PCG adapts the approximated subsurface model. The new approach is applied to a free-noise synthetic data and then to data with different levels and kinds of noise. Moreover, the technique is compared with other inversion methods (stochastic and deterministic ones) to test the efficiency and efficacy. Satisfying results are achieved through the new scheme. However, the sensitivity of the problem has been studied and presented for more understanding and testing for the inverted model. The applicability and validity of this algorithm are further verified by applying it to real residual gravity anomalies in southern California, USA, and Mexico-USA border. Also, two successful applications are done, for mineral prospection in China. Results are in good agreement with those obtained by published literature.

关键词： 2D Gravity Inversion bat algorithm Preconditioned Conjugate Gradient Mineral exploration Hybrid inversion Sensitivity

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Multi-Objective Electrical Power System Design Optimization Using a Modified bat algorithm

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ENERGIES 2021年第13期14卷 3956页

作者： Guerraiche, Khaled Dekhici, Latifa Chatelet, Eric Zeblah, Abdelkader Higher Sch Elect Engn & Energet Oran Dept Elect Engn Oran 31000 Algeria Univ Sci & Technol Oran USTO MB Dept Comp Sci Oran 31000 Algeria Univ Technol Troyes UR InSyTE 12 Rue Marie CurieCS 42060 F-10004 Troyes France Univ Djillali Liabes Sidi Bel Abbes Engn Fac Dept Elect Engn Sidi Bel Abbes 22000 Algeria

The design of energy systems is very important in order to reduce operating costs and guarantee the reliability of a system. This paper proposes a new algorithm to solve the design problem of optimal multi-objective redundancy of series-parallel power systems. The chosen algorithm is based on the hybridization of two metaheuristics, which are the bat algorithm (BA) and the generalized evolutionary walk algorithm (GEWA), also called BAG (bat algorithm with generalized flight). The approach is combined with the Ushakov method, the universal moment generating function (UMGF), to evaluate the reliability of the multi-state series-parallel system. The multi-objective design aims to minimize the design cost, and to maximize the reliability and the performance of the electric power generation system from solar and gas generators by taking into account the reliability indices. Power subsystem devices are labeled according to their reliabilities, costs and performances. Reliability hangs on an operational system, and implies likewise satisfying customer demand, so it depends on the amassed batch curve. Two different design allocation problems, commonly found in power systems planning, are solved to show the performance of the algorithm. The first is a bi-objective formulation that corresponds to the minimization of system investment cost and maximization of system availability. In the second, the multi-objective formulation seeks to maximize system availability, minimize system investment cost, and maximize the capacity of the system.

关键词： multi-objective optimization metaheuristics bat algorithm generalized fly reliability cost power system design Ushakov method

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