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A Novel Improved bat algorithm in UAV Path Planning

Computers, Materials & Continua 2019年第7期61卷 323-344页

作者： Na Lin Jiacheng Tang Xianwei Li Liang Zhao School of computer science Shenyang Aerospace UniversityShenyang110000China School of Information Engineering Suzhou UniversitySuzhou215000China Global Information and Telecommunication Institute Waseda UniversityTokyo169-0051Japan

Path planning algorithm is the key point to UAV path planning *** traditional path planning methods still suffer from low convergence rate and insufficient *** this paper,three main methods are contributed to solving these ***,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position ***,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat *** chaos strategy is proposed to avoid falling into a local *** with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence *** results show that the proposed algorithm also has great robustness for processing with path planning ***,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local *** the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.

关键词： UAV path planning bat algorithm the optimal success rate strategy the APF method chaos strategy

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Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm

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SOFT COMPUTING 2017年第3期21卷 667-685页

作者： Perez, Jonathan Valdez, Fevrier Castillo, Oscar Melin, Patricia Gonzalez, Claudia Martinez, Gabriela Tijuana Inst Technol Calzada Tecnol S-N Tijuana BC Mexico

We describe in this paper a proposed enhancement of the bat algorithm (BA) using interval type-2 fuzzy logic for dynamically adapting the BA parameters. The BAis a metaheuristic algorithm inspired by the behavior of micro bats that use the echolocation feature for hunting their prey, and this algorithm has been recently applied to different optimization problems obtaining good results. We propose a new method for dynamic parameter adaptation in the BA using interval type-2 fuzzy logic, where an especially design fuzzy system is responsible for determining the optimal values for the parameters of the algorithm. Simulations results on a set of benchmark mathematical functions with the interval type-2 fuzzy bat algorithm outperform the traditional bat algorithm and a type-1 fuzzy variant of BA. The proposed integration of the type-2 fuzzy system into the BA has the goal of improving the performance of BA for the future applicability of the algorithm in more complex optimization problems where higher levels of uncertainty need to be handled, like in the optimization of fuzzy controllers.

关键词： Optimization Dynamic parameter adaptation bat algorithm Type-1 fuzzy logic Type-2 fuzzy logic Benchmark mathematical functions

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A new Kriging-bat algorithm for solving computationally expensive black-box global optimization problems

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ENGINEERING OPTIMIZATION 2019年第2期51卷 265-285页

作者： Saad, Abdulbaset Dong, Zuomin Buckham, Brad Crawford, Curran Younis, Adel Karimi, Meysam Univ Victoria Mech Engn Dept Victoria BC Canada Australian Coll Kuwait Mech Engn Dept Kuwait Kuwait

Many global optimization (GO) algorithms have been introduced in recent decades to deal with the Computationally Expensive Black-Box (CEBB) optimization problems. The high number of objective function evaluations, required by conventional GO methods, is prohibitive or at least inconvenient for practical design applications. In this work, a new Kriging-bat algorithm (K-BA) is introduced for solving CEBB problems with further improved search efficiency and robustness. A Kriging surrogate model (SM) is integrated with the bat algorithm (BA) to find the global optimum using substantially reduced number of evaluations of the computationally expensive objective function. The new K-BA algorithm is tested and compared with other well-known GO algorithms, using a set of standard benchmark problems with 2 to 16 design variables, as well as a real-life engineering optimization application, to determine its search capability, efficiency and robustness. Results of the comprehensive tests demonstrated the suitability and superior capability of the new K-BA.

关键词： Global optimization bat algorithm Kriging model computation expensive black-box problems

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Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch

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APPLIED SOFT COMPUTING 2018年 72卷 189-217页

作者： Murugan, R. Mohan, M. R. Rajan, C. Christober Asir Sundari, P. Deiva Arunachalam, S. KCG Coll Technol Dept EEE Madras 600097 Tamil Nadu India Anna Univ Dept EEE Madras 600025 Tamil Nadu India Pondicherry Engn Coll Dept EEE Pondicherry 605014 India Jubail Ind Coll Dept Elect & Elect Engn Technol Jubail Industrial City 31961 Saudi Arabia

This paper presents a new algorithm based on hybridizing bat algorithm (BA) and Artificial Bee Colony (ABC) with Chaotic based Self-Adaptive (CSA) search strategy (CSA-BA-ABC) to solve the large-scale, highly non-linear, non-convex, non-smooth, non-differential, non-continuous, multi-peak and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence rate than the standard BA and ABC. The proposed algorithm works based on the three mechanisms. The first one is a novel adaptive search mechanism, in which one of the three search phases (BA phase, directed onlooker bee phase and modified scout bee phase) is selected based on the aging level of the individual's best solution (pbest). In this regard, ABC's phases can assist BA phase to search based on deeper exploration /exploitation pattern as an alternative. In periodic intervals, the second mechanism called as CSA updates algorithm control parameters using chaotic system based on prevailing search efficiency in the swarm. Lastly, the third mechanism is enhancing the algorithm performance by incorporating individual's directional information, habitat selection and self-adaptive compensation. The effectiveness and robustness of the proposed algorithm are tested on a set of 23 benchmark functions and three CHPED problems. The obtained results by the suggested algorithm in terms of quality solution, computational performance and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better cost- effective solution. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Combined heat and power economic dispatch Artificial bee colony algorithm bat algorithm Directional information Self-adaptive compensation Non-convex optimization

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Improved bat algorithm for roundness error evaluation problem

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第9期19卷 9388-9411页

作者： Li, Guowen Xu, Ying Chang, Chengbin Wang, Sainan Zhang, Qian An, Dong Shenyang Jianzhu Univ Sch Mech Engn Shenyang 110168 Peoples R China Guangdong Univ Technol Sch Electromech Engn Guangzhou 510006 Guangdong Peoples R China

In the production and processing of precision shaft-hole class parts, the wear of cutting tools, machine chatter, and insufficient lubrication can lead to changes in their roundness, which in turn affects the overall performance of the relevant products. To improve the accuracy of roundness error assessments, bat algorithm (BA) is applied to roundness error assessments. An improved bat algorithm (IBA) is proposed to counteract the original lack of variational mechanisms, which can easily lead BA to fall into local extremes and induce premature convergence. First, logistic chaos initialisation is applied to the initial solution generation to enhance the variation mechanism of the population and improve the solution quality;second, a sinusoidal control factor is added to BA to control the nonlinear inertia weights during the iterative process, and the balance between the global search and local search of the algorithm is dynamically adjusted to improve the optimization-seeking accuracy and stability of the algorithm. Finally, the sparrow search algorithm (SSA) is integrated into BA, exploiting the ability of explorer bats to perform a large range search, so that the algorithm can jump out of local extremes and the convergence speed of the algorithm can be improved. The performance of IBA was tested against the classical metaheuristic algorithm on eight benchmark functions, and the results showed that IBA significantly outperformed the other algorithms in terms of solution accuracy, convergence speed, and stability. Simulation and example verification show that IBA can quickly find the centre of a minimum inclusion region when there are many or few sampling points, and the obtained roundness error value is more accurate than that of other algorithms, which verifies the feasibility and effectiveness of IBA in evaluating roundness errors.

关键词： bat algorithm roundness error convergence speed solution accuracy

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Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

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APPLIED SOFT COMPUTING 2020年 90卷 106157-000页

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

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search method is computationally expensive for selecting the optimal thresholds. Therefore, a hybrid bat algorithm with genetic crossover operation and smart inertia weight (SGA-BA) is proposed to choose the optimal thresholds. Furthermore, between-class variance (the Otsu method) and Kapur's entropy are used as objective functions. In the novel SGA-BA, the smart inertia weight balances the SGA-BA's exploration and exploitation based on the number of iterations and fitness values. Moreover, the local search capability of SGA-BA is strengthened by the crossover operation of the genetic algorithm. Meanwhile, the random vector is replaced by the beta distribution, which updates the frequency of bat in a smart way. The proposed SGA-BA was evaluated by a set of benchmark images with various levels of thresholds. Additionally, SGA-BA was compared with some well-known and recent heuristic algorithms, such as the genetic algorithm (GA), gravitational search algorithm (GSA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved salp swarm algorithm (LSSA) and basic bat algorithm (BA). The experimental results show that the proposed SGA-BA provides better outcomes than the other algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multilevel image segmentation bat algorithm Smart inertia weight Crossover operation Beta distribution

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Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization

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JOURNAL OF SUPERCOMPUTING 2019年第5期75卷 2395-2422页

作者： Shehab, Mohammad Khader, Hamad Tajudin Laouched, Makhlouf Alomari, Osama Ahmad Univ Sains Malaysia Sch Comp Sci Gelugor Pulau Pinang Malaysia USTHB LRPE ParIMd Bab Ezzouar Algeria

The cuckoo search algorithm (CSA) is a promising metaheuristic algorithm for solving numerous problems in different fields. It adopts the Levy flight to guide the search process. Nonetheless, CSA has drawbacks, such as the utilization of global search;in certain cases, this technique may surround local optima. Moreover, the results cannot be guaranteed if the step size is considerably large, thereby leading to a slow convergence rate. In this study, we introduce a new method for improving the search capability of CSA by combining it with the bat algorithm (BA) to solve numerical optimization problems. The proposed algorithm, called CSBA, begins by establishing the population of host nests in standard CSA and then obtains a solution through particular part to identify a new solution in BA (i.e., further exploitation). Therefore, CSBA overcomes the slow convergence of the standard CSA and avoids being trapped in local optima. The performance of CSBA is validated by applying it on a set of benchmark functions that are divided into unimodal and multimodal functions. Results indicate that CSBA performs better than the standard CSA and existing methods in the literature, particularly in terms of local search functions.

关键词： Nature-inspired algorithms Cuckoo search algorithm Levy flight bat algorithm Slow convergence Local optima

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A binary bat algorithm with improved crossover operators and Cauchy mutation for unit commitment problem

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JOURNAL OF SUPERCOMPUTING 2024年第8期80卷 11261-11292页

作者： Pang, Aokang Liang, Huijun Lin, Chenhao Yao, Lei Hubei Minzu Univ Coll Intelligent Syst Sci & Engn 39 Xueyuan Rd Enshi 445000 Hubei Peoples R China Enshi Power Supply Co State Grid Hubei Elect Power Co 96 Hangkong Ave Enshi 445000 Hubei Peoples R China

Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches.

关键词： bat algorithm Binary algorithm Unit commitment problem Local mutation Crossover operator

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Sensitivity analysis based on variance decomposition for factors in bat algorithm

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ENGINEERING COMPUTATIONS 2019年第5期36卷 1608-1625页

作者： Yin, Shi Zhu, Ming Beihang Univ Beijing Peoples R China

Purpose This paper aims to quantify the dependence relationship of bat algorithm's (BA) behaviour on the factors that could possibly affect the outputs, and rank the importance of the various uncertain factors thus suggesting research priorities. Design/methodology/approach This paper conducts a sensitivity analysis based on variance decomposition of factors in both of original and improved BA. The data sets for sensitivity analysis are generated by optimal Latin hyper sampling in the design of experiment. The optimal factor sets are screened by stochastic error bar measures for the effective and robust implementation of BA. Findings The paper reveals the inner dependent relationship between factors and output in both of original and improved BA. It figures out the weakness in original BA and improves that. It suggests that uncertainty brought about by factors are mainly caused by the interaction effect and all the higher-order term in sensitivity indices for both of original and improved BA. It ranks the main effect and the total effect of factors and screens out some optimal factor sets for BA. Originality/value This paper quantifies the dependence relationship of BA's behaviour on the factors that could affect outputs using sensitivity analysis based on variance decomposition.

关键词： Uncertainty Sensitivity analysis Variance decomposition bat algorithm Metaheuristic algorithm Stochastic process

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New design of a linear double-sided printed dipole array based on bat algorithm for interference suppression in the first sidelobe direction

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IET MICROWAVES ANTENNAS & PROPAGATION 2020年第12期14卷 1371-1376页

作者： Truong, Luong Xuan Vu-Bang Giang, Truong Tuan, Tran Minh Vietnam Natl Univ Univ Engn & Technol Hanoi Vietnam Vietnam Natl Univ Hanoi Vietnam Minist Informat & Commun Hanoi Vietnam

This study proposes a design of a double-sided printed dipole (DSPD) antenna array with a low first sidelobe level (FSLL) for interference suppression. The proposed array consists of ten DSPD antennas based on the Rogers RO4003C substrate (e(r) = 3.55). The FSLL has been compressed by imposing a single null at the centre of the first sidelobe. The excitation power distribution of the array has been calculated by using the bat algorithm with the amplitude-only control technique and has been implemented by a series-fed network. A back reflector, which is based on an FR4 substrate (e(r) = 4.4), has been used to improve the maximum gain. The simulation results show that the FSLL can be reduced with a null deep level (NDL) of -40 dB in theE-plane at the frequency of 3.5 GHz, while the maximum gain of the array is around 17.7 dBi, and the bandwidth is 600 MHz (3.3-3.9 GHz) with -10 dB ofS(11). A prototype of the proposed DSPD antenna array has been fabricated and measured. A good agreement can be achieved between simulation and measurement results.

关键词： microstrip antenna arrays dipole antenna arrays antenna feeds antenna radiation patterns interference suppression reflector antennas excitation power distribution bat algorithm amplitude-only control technique FR4 substrate FSLL null deep level linear double-sided printed dipole array interference suppression sidelobe direction double-sided printed dipole antenna array Rogers RO4003C single null series-fed network back reflector E-plane DSPD antenna array frequency 3 3 GHz to 3 9 GHz bandwidth 600 0 MHz

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