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An improved bat algorithm for collaborative dynamic target tracking and path planning of multiple UAV

COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期118卷

作者： Shujuan, Huang Wenqi, Chen Beixuan, Lu Feng, Xiao Chao, Shen Wenjuan, Zhang Xian Technol Univ Sch Comp Sci & Engn 2 XueFu Middle Rd Xian 710072 Shaanxi Peoples R China Xian Technol Univ Sch Sci 2 XueFu Middle Rd Xian 710072 Shaanxi Peoples R China

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively.

关键词： Multi-UAV bat algorithm Hungarian algorithm Dynamic function Genetic algorithm Dynamic interference coefficient

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A new modified bat algorithm for global optimization

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RAIRO-OPERATIONS RESEARCH 2023年第5期57卷 2659-2685页

作者： Adil, Nouhaila Lakhbab, Halima Hassan II Univ Fac Sci Ain Chock Dept Math & Comp Sci Casablanca Morocco

bat algorithm, is an evolutionary computation technique based on the echolocation behaviour of microbats while looking for their prey. It is used to perform global optimization. It was developed by Xin-She Yang in 2010. Since then, it has extensively been applied in various optimization problems because of its simple structure and robust performance. Continuous, discrete, or binary, many variants were proposed over the last few years, with applications to solve real-world cases in different fields. Yet, it has one major drawback: its premature convergence due to a lack in its exploration ability. In this paper, we introduce a selection-based improvement and three other modifications to the standard version of this metaheuristic in order to enhance the diversification and intensification capabilities of the algorithm. The newly proposed method has been then tested on 20 standard benchmark functions and the CEC2005 benchmark suit. Some non-parametric statistical tests were also used to compare the New bat algorithm with other algorithms, and results indicate that the new method is very competitive and outperforms some of the state-of-the-art algorithms.

关键词： bat algorithm metaheuristics swarm intelligence roulette wheel selection

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A Complex-valued Encoding bat algorithm for Solving 0-1 Knapsack Problem

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NEURAL PROCESSING LETTERS 2016年第2期44卷 407-430页

作者： Zhou, Yongquan Li, Liangliang Ma, Mingzhi Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

This paper proposes a novel complex-valued encoding bat algorithm (CPBA) for solving 0-1 knapsack problem. The complex-valued encoding method which can be considered as an efficient global optimization strategy is introduced to the bat algorithm. Based on the two-dimensional properties of the complex number, the real and imaginary parts of complex number are updated separately. The proposed algorithm can effectively diversify bat population and improving the convergence performance. The CPBA enhances exploration ability and is effective for solving both small-scale and large-scale 0-1 knapsack problem. Finally, numerical simulation is carried out, and the comparison results with some existing algorithms demonstrate the validity and stability of the proposed algorithm.

关键词： Complex-valued encoding bat algorithm Knapsack problem

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PI controller Optimization for a Heat Exchanger Through Metaheuristic bat algorithm, Particle Swarm Optimization, Flower Pollination algorithm and Cuckoo Search algorithm

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IEEE LATIN AMERICA TRANSACTIONS 2017年第9期15卷 1801-1807页

作者： Damasceno, N. C. Filho, O. G. Univ Potiguar Engn Automacao Proc Gas Nat Natal RN Brazil Univ Fed Rio Grande do Norte Natal RN Brazil

This paper presents the modeling and computer simulation of a control system for a shell and tube heat exchanger, using bat algorithms, Particle Swarm Optimization, Flower Pollination algorithm and Cuckoo Search algorithm. To evaluate the performance of different methods of tuning, we compared the values of the transient of the response to step in eight mesh settings generated. It has also established a comparison between these two types of mesh using the performance indices proposed in the literature, with optimized system by bat algorithms got the best values of transient in relation to the Particle Swarm Optimization, Cuckoo Search algorithm and Flower Pollination algorithm. Performance indices FPA and PSO obtained better results.

关键词： PI controller bat algorithm PSO Flower Pollination algorithm Heat Exchanger Optimization

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A novel complex-valued bat algorithm

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NEURAL COMPUTING & APPLICATIONS 2014年第6期25卷 1369-1381页

作者： Li, Liangliang Zhou, Yongquan Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Guangxi Peoples R China Guangxi Key Lab Hybrid Computat & Integrated Circ Nanning 530006 Guangxi Peoples R China

bat algorithm is a recent optimization algorithm with quick convergence, but its population diversity can be limited in some applications. This paper presents a new bat algorithm based on complex-valued encoding where the real part and the imaginary part will be updated separately. This approach can increase the diversity of the population and expands the dimensions for denoting. The simulation results of fourteen benchmark test functions show that the proposed algorithm is effective and feasible. Compared to the real-valued bat algorithm or particle swarm optimization, the proposed algorithm can get high precision and can almost reach the theoretical value.

关键词： Complex-valued encoding bat algorithm Diploid Test functions

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Hybrid Cloud Resource Provisioning (HCRP) algorithm for Optimal Resource Allocation Using MKFCM and bat algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2020年第2期111卷 1171-1185页

作者： Kalaiselvi, S. Selvi, C. S. Kanimozhi Kongu Engn Coll Dept Comp Technol Erode Tamil Nadu India Kongu Engn Coll Dept Comp Sci & Engn Erode Tamil Nadu India

The basic purpose of resource allocation is to make the most efficient allocation of available resources. It contains resources and the number of tasks. The proposed methodology has two types there are resource discovery and resource allocation. The Multiple Kernel Fuzzy C Means Clustering algorithm (MKFCM) is utilized for resource discovery process. Depends on the MKFCM algorithm the recommended method is group the available resources. Thereafter the resources are allocated with the help of a hybrid optimization technique. Here, resource provisioning algorithm is hybrid with bat algorithm for hybridization approach. The experimental analysis of the proposed mechanism is evaluated based on cost value, memory utilization and time. The prospective strategies have been experimented using the Cloud simulator with Java as the working platform.

关键词： Resource discovery Resource allocation Multiple Kernel Fuzzy C Means Clustering Cloud resource provisioning bat algorithm

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Research on image classification method of strip steel surface defects based on improved bat algorithm optimized BP neural network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期41卷 1509-1521页

作者： Yue, Xiaofeng Ma, Guoyuan Liu, Fuqiuxuan Gao, Xueliang Changchun Univ Technol Mech & Elect Engn Changchun Jilin Peoples R China

Due to the complexity and variety of textures on Strip steel, it is very difficult to detect defects on rigid surfaces. This paper proposes a metal surface defect classification method based on an improved bat algorithm to optimize BP neural network. First, this paper uses the Local Binary Pattern(LBP) algorithm to extract features from six types of defect images including inclusion, patches, crazing, pitted, rolled-in, and scratches, and build a feature sample library with the extracted feature values. Then, the WG-BA-BP network is used to classify the defect images with different characteristics. The weighted experience factor added by the network can control the flight speed of the bat according to the number of iterations and the change of the fitness function. And the gamma distribution is added in the process of calculating loudness, which enhances the local searchability. The BP network optimized by this method has higher accuracy. Finally, to verify the effectiveness of the method, this article introduces the five evaluation indicators of accuracy, precision, sensitivity, specificity, and Fl value under the multi-class model. To prove that this algorithm is more feasible and effective compared with other swarm intelligence algorithms. The best prediction performance of WG-BA-BP is 0.010905, and the accuracy rate can reach 0.9737.

关键词： Image classification BP neural network bat algorithm weighted experience factor Gamma distribution

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A novel bat algorithm with dynamic membrane structure for optimization problems

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APPLIED INTELLIGENCE 2021年第4期51卷 1992-2017页

作者： Alsalibi, Bisan Abualigah, Laith Khader, Ahamad Tajudin Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan

To improve the optimization efficiency for different optimization problems and take advantage of the dynamic membrane computing framework, this paper proposes an improved bat algorithm, namely, Dynamic Membrane-driven bat algorithm (DMBA). The dynamic construction of the DMBA algorithm aims at enhancing population diversity by balancing the exploration-exploitation tradeoff. Unlike the static membrane algorithms, the membranes in DMBA will be dynamically evolved by using merging and separation rules which help in maintaining the diversity of the population. The experimental results on a set of well-known benchmark functions including CEC 2005, CEC 2011, and CEC 2017 clearly prove the effectiveness of the proposed DMBA algorithm in terms of maintaining the diversity and exploitation capabilities compared to that of the others. It is shown that the proposed DMBA algorithm is superior to recent variants of the bat algorithm and other well-known algorithms in terms of solution accuracy and convergence speed.

关键词： Dynamic membrane structure Parallel membrane framework bat algorithm Optimization problems

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Online control of enumeration strategies via bat algorithm and black hole optimization

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NATURAL COMPUTING 2017年第2期16卷 241-257页

作者： Soto, Ricardo Crawford, Broderick Olivares, Rodrigo Niklander, Stefanie Johnson, Franklin Paredes, Fernando Olguin, Eduardo Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile Univ Autonoma Chile Santiago Chile Univ Espiritu Santo Guayaquil Ecuador Univ Playa Ancha Valparaiso Chile Univ Diego Portales Escuela Ingn Ind Santiago Chile Univ San Sebastian Santiago Chile

Constraint programming is an efficient and powerful paradigm for solving constraint satisfaction and optimization problems. Under this paradigm, problems are modeled as a sequence of variables and a set of constraints. The variables have a non-empty domain of candidate values and constraints restrict the values that variables can adopt. The solving process operates by assigning values to variables in order to produce potential solutions which are then evaluated. A main component in this process is the enumeration strategy, which decides the order in which variables and values are chosen to produce such potential solutions. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Unfortunately, selecting the proper strategy is known to be a hard task, as its behavior during search is generally unpredictable and certainly depends on the problem at hand. A recent trend to handle this concern, is to interleave a set of different strategies instead of using a single one during the whole process. The strategies are evaluated according to process indicators in order to use the most promising one on each part of the search process. This process is known as online control of enumeration strategies and its correct configuration can be seen itself as an optimization problem. In this paper, we present two new systems for online control of enumeration strategies based on recent nature-inspired metaheuristics: bat algorithm and black hole optimization. The bat algorithm mimics the location capabilities of bats that employ echoes to identify the objects in their surrounding areas, while black hole optimization inspires its behavior on the gravitational pull of black holes in space. We perform different experimental results by using different enumeration strategies and well-known benchmarks, where the proposed approaches are able to noticeably outperform previous work on online

关键词： Constraint programming Autonomous search Bio-inspired Metaheuristics bat algorithm Black hole

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Resilient wide-area multi-mode controller design based on bat algorithm for power systems with renewable power generation and battery energy storage systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第10期13卷 1884-1894页

作者： Setiadi, Herlambang Mithulananthan, N. Shah, Rakibuzzaman Lee, Kwang Y. Krismanto, Awan Uji Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England CQ Univ Sch Engn & Technol Perth WA 6000 Australia Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

Modern power systems consist of power electronics devices, which are used in renewable energy (RE) conversion. However, these devices, associated controllers, and uncertainty in RE output could bring new challenges to power system stability, especially oscillatory stability. Hence, the integration of battery energy storage systems (BESSs) is being developed to minimise the uncertainty and variability in renewables. Furthermore, to tackle the complex dynamics and inertia-less characteristics of wind and PV plants additional controllers such as power oscillation damping (POD) control and virtual inertia scheme are sought. However, the primary challenges associated with the wide-area oscillation damping controller are signal transmission delay, loss of communication signal, data drops, and others. This paper proposes a bat algorithm (BA) based resilient wide-area multi-mode controller (MMC) for enhancing oscillatory stability margin with high penetration of renewable power generations (RPGs) and BESSs. The Java 500 kV Indonesian grid is used to evaluate the performance of the resilient wide-area MMC. From the results, it is found that the proposed controller effectively damp the critical mode of oscillation in the system even under communication failure as well as certain damping controller failures.

关键词： battery storage plants optimisation power system stability power system control power system measurement renewable energy sources power grids damping control system synthesis renewable power generation bat algorithm practical power system resilient wide-area MMC damping controller failures resilient wide-area multimode controller design battery energy storage systems modern power systems power electronics devices energy conversion renewable energy sources power system stability renewable energy generation power output solar power plants additional controllers power oscillation damping control wide-area signals wide-area oscillation damping controller oscillatory stability problems BESS POD control virtual inertia scheme voltage 500 0 kV

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