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Optimal path planning of mobile robot using the hybrid cuckoo-bat algorithm in assorted environment

INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2019年第1期7卷 35-52页

作者： Gunji, Balamurali Deepak, B. B. V. L. Saraswathi, M. B. L. Mogili, Umamaheswara Rao Natl Inst Technol Rourkela Rourkela India

Purpose The purpose of this paper is to obtain an optimal mobile robot path planning by the hybrid algorithm, which is developed by two nature inspired meta-heuristic algorithms, namely, cuckoo-search and bat algorithm (BA) in an unknown or partially known environment. The cuckoo-search algorithm is based on the parasitic behavior of the cuckoo, and the BA is based on the echolocation behavior of the bats. Design/methodology/approach The developed algorithm starts by sensing the obstacles in the environment using ultrasonic sensor. If there are any obstacles in the path, the authors apply the developed algorithm to find the optimal path otherwise reach the target point directly through diagonal distance. Findings The developed algorithm is implemented in MATLAB for the simulation to test the efficiency of the algorithm for different environments. The same path is considered to implement the experiment in the real-world environment. The ARDUINO microcontroller along with the ultrasonic sensor is considered to obtain the path length and time of travel of the robot to reach the goal point. Originality/value In this paper, a new hybrid algorithm has been developed to find the optimal path of the mobile robot using cuckoo search and BAs. The developed algorithm is tested with the real-world environment using the mobile robot.

关键词： Mobile robot Path planning bat algorithm Cuckoo search algorithm Hybrid algorithm

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An Improved bat algorithm for Job Shop Scheduling Problem 16

An Improved Bat Algorithm for Job Shop Scheduling Problem

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16th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Chen, Xiaohan Zhang, Beike Gao, Dong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100020 Peoples R China

ISBN: (纸本)9781728116976

Job shop scheduling problem is one of the hardest combinatorial optimization problems. Many approaches such as Genetic algorithm, Particle Swarm Optimization algorithm and heuristic algorithm have been used to solve this problem. Unfortunately, those algorithms are easy to get a local optimal solution. In this paper, an Improved bat algorithm was proposed to solve job shop scheduling problem, it can effectively avoid premature convergence. Moreover, it can speed up the convergence and improves the ability to find global optimal solution. Compared with bat algorithm and Particles Swarm algorithm, the simulation results show that the Improved bat algorithm is efficacious to minimize makespan and accurate to find the optimal solution.

关键词： bat algorithm Job Shop Scheduling Problem Multi-objects Optimization

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Free-Form Parametric Fitting of Van der Waals Binodal and Spinodal Curves with bat algorithm 13

Free-Form Parametric Fitting of Van der Waals Binodal and Sp...

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13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) / International Workshop on Applied Artificial Intelligence (AI Maldives)

作者： Campuzano, Almudena Iglesias, Andres Galvez, Akemi Oregon State Univ Coll Engn Sch Chem Bio & Env Engn CBEE Corvallis OR 97331 USA Univ Cantabria Sch Ind Engn Santander Spain Toho Univ Fac Sci Dept Informat Sci Funabashi Chiba Japan Univ Cantabria Dept Appl Math & Comp Sci Santander Spain

ISBN: (纸本)9781728127415

This paper concerns the Van der Waals (VdW) equation of state, originally conceived to be a generalization of the ideal gas law. For practical use, it is often necessary to compute two characteristic curves of VdW, called binodal and spinodal curves. They are usually constructed through polynomial fitting from a collection of 2D points in the pressure-volume plane by using standard numerical procedures. However, the resulting models are still limited and can be further enhanced. In this paper, we carry out this task through least-squares approximation of sets of 2D points using free-form Bezier curves. This requires to perform data parameterization in addition to computing the poles of the curves. To this aim, we apply a powerful nature-inspired swarm intelligence method for continuous optimization called the bat algorithm. To test the performance of this new approach, it has been applied to real data of a gas. Our experimental results show that the method can reconstruct the characteristic curves with very good accuracy. In addition, the computing times are also very good, given the complexity of this problem. These remarkable features make this approach very promising in the field. Furthermore, it is actually ready to be applied to real-world instances of chemical components and mixtures.

关键词： equation of state binodal curve spinodal curve data fitting swarm intelligence bat algorithm Bezier curves

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A critical analysis of the bat algorithm

ENGINEERING REPORTS

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ENGINEERING REPORTS 2020年第8期2卷

作者： Gagnon, Iannick April, Alain Abran, Alain Ecole Technol Super Software & Informat Technol Engn Dept 1100 Notre Dame W Montreal PQ H3C 1K3 Canada

This article presents an analysis of the bat algorithm (BA) based on elementary mathematical analysis and statistical comparisons of the first hitting time performance metric distributions obtained on a test set comprising five carefully selected objective functions. The findings show that the BA is not an original contribution to the metaheuristics literature and that it is not generally superior to the Particle Swarm Optimization algorithm when fair comparisons are made. It is also shown that some components of the BA can be either replaced by simpler alternatives or be removed entirely to increase performance. Finally, the results suggest that the best version of the BA is in fact a simple hybrid between Particle Swarm Optimization and Simulated Annealing. To encourage more transparency in metaheuristics research, the entirety of the MATLAB code used in this article is available in a GitHub repository for suggestions and/or corrections.

关键词： bat algorithm Metaheuristics particle swarm optimization

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Parameter Estimation of a Nonlinear Hydrologic Model for Channel Flood Routing with the bat algorithm 10th

Parameter Estimation of a Nonlinear Hydrologic Model for Cha...

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10th International Conference on Swarm Intelligence (ICSI)

作者： Sanchez, Rebeca Suarez, Patricia Galvez, Akemi Iglesias, Andres Univ Cantabria Dept Appl Math & Computat Sci Ave Castros S-N Santander 39005 Spain Toho Univ Fac Sci Dept Informat Sci Narashino Campus2-2-1 Miyama Funabashi Chiba 2748510 Japan

ISBN: (纸本)9783030263683;9783030263690

Flood routing is a methodology to predict the changes of the flow of water as it moves through a natural river, an artificial channel, or a reservoir. It is widely used in fields such as flood prediction, reservoir design, geographic planning, and many others. One of the most popular and widely used flood routing techniques is the Muskingum model, as it is conceptually simple and only depends on a few parameters that can be estimated from historical inflow/outflow records. However, the estimation of such parameters for the nonlinear case is still a challenging task. In this paper we present a method based on a powerful swarm intelligence technique called bat algorithm to solve the parameter estimation problem of the nonlinear Muskingum model for channel routing. The method is applied to an illustrative example used as a benchmark in the field with very good results. We also show that our method outperforms other state-of-the-art methods in the field such as PSO.

关键词： Swarm intelligence Global optimization bat algorithm Flood routing Hydrologic models Parameter estimation

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Torque Observer of SRM Based on BP Neural Network Optimized by bat algorithm 22

Torque Observer of SRM Based on BP Neural Network Optimized ...

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22nd International Conference on Electrical Machines and Systems (ICEMS)

作者： Han, Lele Xu, Aide Zhu, Jingwei Zhang, Wen Dalian Maritime Univ Inst Informat Sci & Technol Dalian Peoples R China Dalian Maritime Univ Inst Marine Elect Engn Dalian Peoples R China

ISBN: (纸本)9781728133980

The traditional torque observation of switched reluctance motor (SRM) has disadvantages such as low accuracy and complex calculations, which may easily lead to poor control influence on minimizing torque ripple. To solve this problem, a new method based on BP neural network optimized by the bat algorithm (BA-BP) to establish the torque observer of SRM is proposed. The bat algorithm (BA) is used to optimize the initial weights and thresholds of BP neural network to achieve a much more accurate non-linear fitting of switched reluctance motor current, rotor position angle to torque, and construct a torque observer to achieve accurate SRM torque calculation. Finally, the direct torque control (DTC) system of switched reluctance motor is built, the designed neural network torque observer is applied to estimate the torque and minimize the torque ripple of the motor. Simulation results show that the proposed torque observer method can calculate the torque with much higher accuracy than the traditional BP neural network torque observer. Compared with DTC based on torque look-up table method or traditional BP neural network torque observer, DTC based on BA-BP neural network torque observer has better effect on minimizing the torque ripple of SRM.

关键词： switched reluctance motor bat algorithm BP neural network torque observer direct torque control torque ripple

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Sensor Node Activation Using bat algorithm for Connected Target Coverage in WSNs

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SENSORS 2020年第13期20卷 3733.-3733.页

作者： Kim, Jaemin Yoo, Younghwan Pusan Natl Univ Sch Elect & Comp Engn Busan 46241 South Korea

This paper proposes a sensor node activation method using the nature-inspired algorithm (NIA) for the target coverage problem. The NIAs have been used to solve various optimization problems. This paper formulates the sensor target coverage problem into an object function and solves it with an NIA, specifically, the bat algorithm (BA). Although this is not the first attempt to use the BA for the coverage problem, the proposed method introduces a new concept called bat couple which consists of two bats. One bat finds sensor nodes that need to be activated for sensing, and the other finds nodes for data forwarding from active sensor nodes to a sink. Thanks to the bat couple, the proposed method can ensure connectivity from active sensor nodes to a sink through at least one communication path, focusing on the energy efficiency. In addition, unlike other methods the proposed method considers a practical feature of sensing: The detection probability of sensors decreases as the distance from the target increases. Other methods assume the binary model where the success of target detection entirely depends on whether a target is within the threshold distance from the sensor or not. Our method utilizes the probabilistic sensing model instead of the binary model. Simulation results show that the proposed method outperforms others in terms of the network lifetime.

关键词： wireless sensor networks probabilistic sensing model connected target coverage bat algorithm sensor node activation

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DYNAMIC PROGRAMMING algorithm AND bat algorithm BASED STORM NODES SCHEDULING IN EDGE COMPUTING

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2020年第3期16卷 1021-1033页

作者： Zhang, Lingling Beihua Univ Dept Comp Sci & Technol 3999 Binjiang East Rd Jilin 132013 Peoples R China

The high communication delay and uneven load among heterogeneous edge nodes are affecting the performance of edge computing, and they are almost impossible to be solved by the traditional cloud computing platforms. In this paper, we address these problems by studying the scheduling optimization method for the storm nodes in edge computing environments. At first, a storm scheduling model is established, where server cluster structure and schedule workflow are formulated. Then, a heuristic dynamic programming algorithm is proposed to address the general scheduling issue and a bat-based scheduling strategy is proposed to address a special case faced by the heuristic dynamic programming algorithm. Finally, the experimental results show that the proposed algorithm can minimize the communication cost and guarantee the minimum scheduling requirements.

关键词： Edge computing Storm nodes Scheduling Dynamic programming bat algorithm

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Design of Fuzzy Controller rule base using bat algorithm 6

Design of Fuzzy Controller rule base using Bat Algorithm

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6th International Conference on Emerging and Renewable Energy - Generation and Automation (ICEREGA)

作者： Talbi, Nesrine Jijel Univ Fac Sci & Technol LAJ Res Lab Dept Elect Jijel Algeria

The work in this paper revolves fundamentally around thc main axes of fuzzy control of thc type Takagi-Sugcno (T-S) zero order for dynamic, complex nonlinear systems. In this paper, we present method for designing Fuzzy controller rule base using a new swarm intelligence algorithm, which is based on the bat algorithm. The bat algorithm is one of the most recent swarm intelligence based algorithms that simulates the intelligent hunting behavior of the bats found in nature. The main objective is to design the fuzzy rule base of fuzzy controller respecting the desired performance. To demonstrate the efficiency of the suggested approach, a control of a Magnetic Ball Suspension System is selected. Simulation results shows that the proposed approach could be employed as a simple and effective optimization method for achieving optimum determination of fuzzy rule base parameters. (C) 2019 The Authors. Published by Elsevier Ltd

关键词： Fuzzy rule base bat algorithm Fuzzy controller Magnetic Ball Suspension System

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Computing Rational Border Curves of Melanoma and Other Skin Lesions from Medical Images with bat algorithm 19

Computing Rational Border Curves of Melanoma and Other Skin ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Galvez, Akemi Fister, Iztok, Jr. Osaba, Eneko Fister, Iztok Del Ser, Javier Iglesias, Andres Univ Cantabria Santander Spain Toho Univ Tokyo Japan Univ Maribor Maribor Slovenia TECNALIA Derio Spain Univ Basque Country UPV EHU TECNALIA Bilbao Spain

ISBN: (纸本)9781450367486

Border detection of melanoma and other skin lesions from images is an important step in the medical image processing pipeline. Although this task is typically carried out manually by the dermatologists, some recent papers have applied evolutionary computation techniques to automate this process. However, these works are only focused on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bezier curves by applying the bat algorithm, a popular bio-inspired swarm intelligence technique for optimization. Experimental results on two examples of medical images of melanomas show that this method is promising, as it outperforms the polynomial approach and can be applied to medical images without further pre/post-processing.

关键词： Healthcare swarm intelligence bio-inspired optimization bat algorithm rational curves border detection medical images

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