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Optimal control of a service system with emergency vacation using bat algorithm

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2020年 364卷 112332-000页

作者： Shekhar, Chandra Varshney, Shreekant Kumar, Amit Birla Inst Technol & Sci Pilani Dept Math Pilani Campus Pilani 333031 Rajasthan India

The novelty of this research article is to study the effect of new introduced emergency vacation of a single service provider in service system via queueing-theoretic approach and comparative analysis of some metaheuristic and heuristic optimization techniques for optimal control. According to this vacation policy, the working state server takes a vacation in an emergency without completing the ongoing service of the waiting customer in the system. For the modeling purpose, it is assumed that the inter-arrival times, service times and vacation times are exponentially distributed. A cost optimization problem is developed to obtain the optimal values of system design parameters. The comparative analysis of proposed metaheuristic technique bat algorithm (BA) with another well-known metaheuristic technique: particle swarm optimization (PSO), and heuristic technique: Quasi-Newton method has been done to achieve the optimal operating conditions with minimal expected cost. Finally, numerical simulations and illustrations are provided to get an understanding of the mathematical modeling in detail and performance analysis of the queueing measures is also done. Numerical results are summarized in tables and graphs to provide a quick insight into critical issues related to the studied model. Concluding remarks are also drawn along with the future scope of the present study. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Emergency vacation bat algorithm Particle swarm optimization Quasi-Newton method

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Mobile Robot Localization Based on the Novel Leader-Based bat algorithm

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2019年第3期30卷 337-346页

作者： Neto, Wolmar A. Pinto, Milena F. Marcato, Andre L. M. da Silva, Ivo C., Jr. Fernandes, Daniel de A. Univ Fed Juiz de Fora Fac Engn BR-36036330 Juiz De Fora Brazil

This work proposes a new approach to the well-known method bat algorithm for solving the mobile robots global localization problem. The proposed method is leader-based bat algorithm (LBBA). The LBBA uses a small number of better micro-bats as leaders to influence the colony in the search for the best position, dealing satisfactorily with ambiguities during the localization process. The tests covered different scenarios aiming at comparing the proposed algorithm with other methods, such as the standard BA, the particle swarm optimization and particle filter. The results outperformed the compared methods, presenting a fast response and errors below the intended tolerance. The algorithm was tested in the robot kidnapping scenario and shows fast recovery in both simulation and in a real environment. In addition, the proposed technique showed 21% lower average error when compared with an algorithm that presents a variable quantity of particles, i.e. the adaptive Monte Carlo localization algorithm.

关键词： bat algorithm Evolutionary computation Localization algorithm Mobile robots

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A Manhattan distance-based binary bat algorithm vs. integer ant colony optimisation for intrusion detection in the audit trails

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第4期18卷 424-437页

作者： Guendouzi, Wassila Boukra, Abdelmadjid USTHB LSI Lab Fac Elect & Comp Sci Algiers Algeria

Intrusion detection system (IDS) is the process of monitoring and analysing security activities occurring in computer or network systems. The detection method can perform either anomaly-based or misuse-based detection. The misuse mechanism aims to detect predefined attack scenarios in the audit trails, whereas the anomaly detection mechanism aims to detect deviations from normal user behaviour. In this paper, we deal with misuse detection. We propose two approaches to solve the NP-hard security audit trail analysis problem. Both rely on the Manhattan distance measure to improve the intrusion detection quality. The first proposed method, named enhanced binary bat algorithm (EBBA), is an improvement of bat algorithm (BA). The second one, named enhanced integer ant colony system (EIACS), is a combination of two metaheuristics: ant colony system (ACS) and simulated annealing (SA). Experiment results indicate that, for large problem size, the performance of EIACS is more significant than EBBA.

关键词： intrusion detection security audit trail analysis combinatorial optimisation problem NP-hard Manhattan distance bat algorithm ant colony system ACS simulated annealing

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A multirobot target searching method based on bat algorithm in unknown environments

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 141卷 112945-000页

作者： Tang, Hongwei Sun, Wei Yu, Hongshan Lin, Anping Xue, Min Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Shaoyang Univ Hunan Prov Key Lab Grids Operat & Control Multipo Shaoyang 422000 Peoples R China Hunan Univ Shenzhen Res Inst Shenzhen 518057 Guangdong Peoples R China

Multirobot target searching in unknown environments is a currently trending topic of discussion. In this paper, an improved bat algorithm (BA) for multirobot target searching in unknown environments, named adaptive robotic bat algorithm (ARBA), is proposed;it acts as the controlling mechanism for robots. The obstacle avoidance problem is considered in the proposed ARBA. The adaptive inertial weight strategy helps ARBA improve its diversity and provides an effective mechanism for escaping from local optima. In addition, the Doppler effect is introduced to improve ARBA;the effect can be adaptively compensated when the robot moves and helps robots avoid premature convergence. Moreover, the location of the target in an unknown environment is unknown, and a multi-swarm strategy is introduced into the ARBA to improve the diversity and expand the search space of robots so that robots can find the location of the target as well as the target itself faster than the existing algorithms. Experiments were conducted in three aspects to verify the effectiveness and efficiency of AREA. We compared ARBA with the other algorithms in this field;the experimental results demonstrate that ARBA exhibits better performance in multirobot target searching and can be applied to multirobot intelligent systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： bat algorithm Adaptive robotic BA Doppler effect Multirobot Target searching

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Multi-point shortest path planning based on an Improved Discrete bat algorithm

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APPLIED SOFT COMPUTING 2020年 95卷 106498-106498页

作者： Liu, Lijue Luo, Shuning Guo, Fan Tan, Shiyang Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China

Multi-point shortest path planning problem is a typical problems in discrete optimization. The bat algorithm is a nature-inspired metaheuristic optimization algorithm that is used in a wide range of fields. However, there is one problem with the BA, which is easy to premature. To solve multi-point shortest path planning problem, an improved discrete bat algorithm (IDBA) is proposed in this paper. In this algorithm, the Floyd-Warshall algorithm is first used to transform an incomplete connected graph into a complete graph whose vertex set consists of a start point and necessary points. Then the algorithm simulates the bats' foraging and obstacle avoidance process to find the shortest path in the complete graph to satisfy the constraints. Finally, the path is transferred to the original incomplete graph to get the solution. In order to overcome the premature phenomenon of a discrete bat algorithm, the modified neighborhood operator is proposed. To prove the effectiveness of our method, we compared its performance in 26 instances with the results obtained by three different algorithms: DBA, IBA and GSA-ACS-PSOT. We also performed a sensitivity analysis on the parameters. The results indicate that the improved bat algorithm outperforms all the other alternatives in most cases. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-point path planning bat algorithm Floyd-Warshall algorithm Incomplete connected graph

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Improved Hybrid bat algorithm with Invasive Weed and Its Application in Image Segmentation

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019年第11期44卷 9221-9234页

作者： Yue, Xiaofeng Zhang, Hongbo Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Jilin Peoples R China

As one of the most popular and effective image segmentation methods, multi-level thresholding is widely used. However, too much computation is needed to select the optimal thresholds with basic ergodic method. In order to solve this problem, a hybrid bat algorithm (IWBA) which incorporates bat algorithm with invasive weed optimization (IWO) is employed to choose the optimal thresholds. In IWBA algorithm, the local search ability is enhanced by integrating with IWO algorithm. Furthermore, a new inertia weight based on Lagrange interpolation is proposed to balance exploration and exploitation. In IWBA algorithm, scale parameter of normal distribution is adjusted according to the value of fitness. It is established that IWBA algorithm is able to segment the image in more efficient and accurate way than other algorithms. More importantly, IWBA algorithm can also be applied to other fields.

关键词： Image segmentation Invasive weed optimization bat algorithm Lagrange interpolation Scale parameter

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Parameter extraction of photovoltaic models using an enhanced Levy flight bat algorithm

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ENERGY CONVERSION AND MANAGEMENT 2020年 221卷 113114-113114页

作者： Pires Deotti, Lucas Meirelles Rezende Pereira, Jose Luiz da Silva Junior, Ivo Chaves Fed Univ Juiz de Fora UFJF Dept Elect Engn Juiz De Fora Brazil

In this paper, a modified version of the bat algorithm (BA), called enhanced Levy flight bat algorithm (ELBA), is proposed for accurate and efficient parameter extraction of different photovoltaic (PV) models from experimental data. Typically, it is formulated as a multimodal nonlinear optimization problem in which the objective function is to minimize the root mean square error verified between the real data and the simulated ones by the PV model at hand, considering certain values for its parameters. In addition, the constraints are associated to the lower and upper bounds of these parameters. From the computational perspective, the main innovations of ELBA lies in the: (i) introduction of a specific mathematical expression to enhance the diversification of new solutions;(ii) adoption of a mathematical expression based on the Levy flight to perform an effective local search;and (iii) selection of new equations for updating certain control parameters, which provide a better balance between the exploration and exploitation mechanisms of the algorithm. Simulation results comprehensively demonstrate that ELBA has a very competitive performance in terms of effectiveness, robustness, stability, convergence speed and time of simulation, in relation to other state-of-the-art metaheuristic algorithms. Therefore, the major contribution of this paper is the ELBA, a modified metaheuristic algorithm which proves to be a promising tool for parameter extraction of different PV models from experimental data.

关键词： Photovoltaic modeling Parameter estimation Experimental data Metaheuristic algorithm bat algorithm

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Adaptive inertia weight bat algorithm with Sugeno-Function fuzzy search

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APPLIED SOFT COMPUTING 2020年 90卷 106159-000页

作者： Rauf, Hafiz Tayyab Malik, Sumbal Shoaib, Umar Irfan, Muhammad Naeem Lali, M. Ikramullah Univ Gujrat Dept Comp Sci Gujrat Pakistan Ryerson Univ Toronto ON Canada Univ Educ Div Sci & Technol Dept Informat Sci Lahore Pakistan

bat algorithm (BA) turns into the most generally utilized meta-heuristic algorithm to solve the different sort of global optimization problems. In the optimization of continuous data, BA experiences one of the prominent difficulties called premature convergence. In order to tackle premature convergence, this study exhibits a new version of BA called Adaptive inertia weight bat algorithm with Sugeno-Function Fuzzy Search (ASF-BA). The proposed algorithm ASF-BA brings two major adjustments in the standard BA. Firstly, we incorporated an adaptive inertia weight to boost up the velocity rate of bats effectively. Secondly, we replaced the random searching method of standard BA with Sugeno-Function fuzzy search, which used Sugeno-Function decline curves to dynamically adjust the fitness of each bat according to their own experience and experience of their neighbour bats. We compared ASF-BA with several old fashioned and new fashioned optimization algorithms. ASF-BA is also tested against top hybridized and enhanced version of DE algorithms. The CEC 2017 benchmark (30 real parameter numerical optimization problems), CEC 2017 (28 constrained optimization problems) and 19 additional benchmark problems have been used to examine and compare the performance of ASF-BA with other state of the art variants. Contrasted with the existing BA and other leading variants of BA, DE, and PSO on CEC 2017 constrained and numerical benchmarks, the ASF-BA is excellent to the state-of-art variants of BA, DE, and PSO in terms of stability, convergence speed and solution quality. The ASF-BA sets stable support for resolving optimization problems of intelligent and expert systems. Furthermore, we also examined the performance of proposed ASF-BA for the weight optimization of Feed Forward Neural Networks (FFNN) and compared ASF-BA with Back Propagation algorithm (BPA), BA and PSO. ASF-BA achieved 94 % of maximum accuracy. The experimental outcomes reveal that the suggested algorithm executed espec

关键词： bat algorithm Adaptive inertia weight Neural networks Sugeno-Function

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A unified heuristic bat algorithm to optimize the LEACH protocol

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第9期32卷 e5619-e5619页

作者： Cai, Xingjuan Geng, Shaojin Wu, Di Wang, Lei Wu, Qidi Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligent Lab Taiyuan 030024 Shanxi Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai Peoples R China

Wireless sensor networks (WSN) have high value in the field of wireless communications. As the earliest WSN clustering protocol, Low Energy Adaptive Clustering Hierarchy (LEACH) can effectively reduce the energy consumption of data transmission in sensor networks. However, LEACH has some problems such as cluster head nodes are unevenly distributed. In this paper, a unified heuristic bat algorithm (UHBA) is proposed to optimize elections in cluster heads. This algorithm guarantees that the election of cluster heads can freely transform both global search and local search. Meanwhile, comparing with several other variants of the bat algorithm in CEC2013 test suite, it can be seen from results that UHBA has better performance. Moreover, the application of the algorithm on LEACH is better than other algorithms, which further proves that the algorithm has better results.

关键词： bat algorithm global search local search Low Energy Adaptive Clustering Hierarchy (LEACH) wireless sensor networks (WSN)

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Hybrid Wolf-bat algorithm for Optimization of Connection Weights in Multi-layer Perceptron

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2020年第1期16卷 37-37页

作者： Agrawal, Utkarsh Arora, Jatin Singh, Rahul Gupta, Deepak Khanna, Ashish Khamparia, Aditya Maharaja Agrasen Inst Technol Delhi India Lovely Profess Univ Punjab India

In a neural network, the weights act as parameters to determine the output(s) from a set of inputs. The weights are used to find the activation values of nodes of a layer from the values of the previous layer. Finding the ideal set of these weights for training a Multi-layer Perceptron neural network such that it minimizes the classification error is a widely known optimization problem. The presented article proposes a Hybrid Wolf-bat algorithm, a novel optimization algorithm, as a solution to solve the discussed problem. The proposed algorithm is a hybrid of two already existing nature-inspired algorithms, Grey Wolf Optimization algorithm and bat algorithm. The novel introduced approach is tested on ten different datasets of the medical field, obtained from the UCI machine learning repository. The performance of the proposed algorithm is compared with the recently developed nature-inspired algorithms: Grey Wolf Optimization algorithm, Cuckoo Search, bat algorithm, and Whale Optimization algorithm, along with the standard Back-propagation training method available in the literature. The obtained results demonstrate that the proposed method outperforms other bio-inspired algorithms in terms of both speed of convergence and accuracy.

关键词： Neural network hybrid bat algorithm grey wolf optimization optimization connection weights nature inspired MLP

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