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Improved Directional bat algorithm Based Electric Power Dispatch

ELECTRIC POWER COMPONENTS AND SYSTEMS 2021年第19-20期48卷 2089-2105页

作者： Singh, Diljinder Dhillon, Jaspreet Singh St Longowal Inst Engn & Technol Sangrur Punjab India

No single metaheuristic search algorithm can be adjudged universally best general-purpose optimizer. The performance of search algorithms mainly depends upon the weightage assigned to global and local search strategies. This paper proposed an improved directional bat optimizer to minimize the operating cost of the electric power dispatch (EPD) problem that establishes a balance between global and local search strategies. Improved directional bat algorithm exploits directional echolocation bat behavior, directional exploration, neighborhood search and opposition based learning for generation jumping. The directional bat algorithm acts as a global search tool whereas exploration in each direction and neighborhood search performs local search. Opposition learning improves convergence with diversity. An effect of valve-point loading introduces a discontinuity in cost characteristics. The EPD problem addresses energy balance, generator capacity, ramp-rate limits and prohibited operating zones (POZ) avoidance constraints. An iterative technique handles energy balance constraint. The generation is adjusted to avoid the violation of generation capacity, ramp-rate limit and POZ constraints. The proposed algorithm is verified on various electric power systems. The results verify that the proposed algorithm is a potential algorithm to solve EPD problems as it competes with recent existing algorithms undertaken for comparison

关键词： economic power dispatch directional echolocation bat algorithm exploration opposition-based learning local search

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Improved bat algorithm Based on Fast Diving Strategy

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JOURNAL OF INTERNET TECHNOLOGY 2023年第6期24卷 1249-1262页

作者： Geng, Yanxiang Zhang, Liyi Zhang, Yong Li, Zhixing Li, Jiahui Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

bat algorithm has good global search ability, but it has some problems, such as slow convergence speed in local search stage, low convergence accuracy, easy to fall into local optimization and can not escape. In view of the above defects, inspired by Harris Hawks's strategy of catching rabbits, this paper introduces the surrounding mechanism of prey, which can quickly approach the food and judge its quality, so as to achieve the purpose of rapid convergence and improve the convergence accuracy. The experiment shows that the improved algorithm of the fast diving strategy is tested by using the test function, and compared with the basic bat algorithm, backtracking bat algorithm and HABC. The improved bat algorithm of the fast diving strategy has better optimization accuracy, faster convergence speed, simple algorithm and higher success rate.

关键词： bat algorithm Fast diving strategy Encirclement mechanism Escape CEC06-2019

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Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem

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SOFT COMPUTING 2021年第8期25卷 6445-6467页

作者： Jiang, Manrui Liu, Weiyi Xu, Wen Chen, Wei Capital Univ Econ & Business Sch Management Engn Beijing Peoples R China Capital Univ Econ & Business Sch Finance Beijing Peoples R China

This paper deals with a multiperiod multiobjective fuzzy portfolio selectiossn problem based on credibility theory. A credibilistic multiobjective mean-VaR model is formulated for the multiperiod portfolio selection problem, whereby the return is quantified by the credibilistic mean and the risk is measured by the credibilistic VaR. We also consider liquidity, cardinality, and upper and lower bound constraints to obtain a more realistic model. Furthermore, to solve the proposed model efficiently, an improved multiobjective bat algorithm termed IMBA is designed, in which three new strategies, i.e., the global best solution selection strategy, candidate solution generation strategy, and competitive learning strategy, are proposed to increase the convergence speed and improve the solution quality. Finally, comparative experiments are presented to show the applicability and superiority of the proposed approaches from two aspects. First, the designed IMBA is compared with seven typical algorithms, i.e., multiobjective particle swarm optimization, multiobjective artificial bee colony, multiobjective firefly algorithm, multiobjective differential evolution, multiobjective bat, the non-dominated sorting genetic algorithm (NSGA-II) and strength pareto evolutionary algorithm 2 (SPEA2), on a number of benchmark test problems. Second, the applicability of the proposed model to practical applications of portfolio selection is given under different circumstances.

关键词： Multiperiod portfolio selection Credibility measure Multiobjective optimization Value at risk bat algorithm

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An Ensemble Method based on Selection Using bat algorithm for Intrusion Detection

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COMPUTER JOURNAL 2018年第4期61卷 526-538页

作者： Shen, Yanping Zheng, Kangfeng Wu, Chunhua Zhang, Mingwu Niu, Xinxin Yang, Yixian Beijing Univ Posts & Telecommun Sch CyberSpace Secur Beijing Peoples R China Inst Disaster Prevent Dept Disaster Informat Engn Beijing Peoples R China Hubei Univ Technol Sch Comp Wuhan Hubei Peoples R China

Machine learning plays an important role in constructing intrusion detection models. However, the information era is an era of data. With the continuous increase in data size and the growth of data dimensions, the ability of a single classifier is becoming limited in predicting samples. In this paper, we present an ensemble method using random subspace in which an extreme learning machine (ELM) is chosen as the base classifier. To optimize the ensemble model, an ensemble pruning method based on the bat algorithm (BA) is proposed. Meanwhile, a fitness function based on the accuracy and diversity of an ensemble is defined in the BA to obtain an improved classifier subset. Three public datasets, the KDD99, NSL and Kyoto datasets, are adopted to assess the robustness of the method. The empirical results indicate that the ensemble method based on random subspace can improve the accuracy and robustness over the use of an individual ELM. The results also show that compared with when all the sub-classifiers are used in the ensemble, the pruning framework can not only achieve comparable or better performance but also save substantial computing resources in an intrusion detection system (IDS).

关键词： extreme learning machine bat algorithm ensemble pruning intrusion detection

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QoS multicast routing for wireless mesh network based on a modified binary bat algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 3057-3073页

作者： Meraihi, Yassine Acheli, Dalila Ramdane-Cherif, Amar Univ MHamed Bougara Boumerdes Automat Dept 35000 Ave Independence Boumerdes Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France

The quality of service multicast routing problem is a very important research issue for transmission in wireless mesh networks. It is known to be NP-complete problem, so many heuristic algorithms have been employed for solving the multicast routing problem. This paper proposes a modified binary bat algorithm applied to solve the QoS multicast routing problem for wireless mesh network which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate to get low-cost multicasting tree. The binary bat algorithm has been modified by introducing the inertia weight w in the velocity update equation, and then the chaotic map, uniform distribution and gaussian distribution are used for choosing the right value of w. The aim of these modifications is to improve the effectiveness and robustness of the binary bat algorithm. The simulation results reveal the successfulness, effectiveness and efficiency of the proposed algorithms compared with other algorithms such as genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization algorithm, bacteria foraging-particle swarm optimization, bi-velocity discrete particle swarm optimization and binary bat algorithm.

关键词： bat algorithm Binary bat algorithm Quality of service (QoS) Multicast routing Multicast tree

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Fuzzy entropy based optimal thresholding using bat algorithm

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APPLIED SOFT COMPUTING 2015年 31卷 381-395页

作者： Ye, Zhi-Wei Wang, Ming-Wei Liu, Wei Chen, Shao-Bin Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China

Image segmentation is a very significant process in image analysis. Much effort based on thresholding has been made on this field as it is simple and intuitive, commonly used thresholding approaches are to optimize a criterion such as between-class variance or entropy for seeking appropriate threshold values. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. This paper considers image thresholding as a constrained optimization problem and optimal thresholds for 1-level or multi-level thresholding in an image are acquired by maximizing the fuzzy entropy via a newly proposed bat algorithm. The optimal thresholding is achieved through the convergence of bat algorithm. The proposed method has been tested on some natural and infrared images. The results are compared with the fuzzy entropy based methods that are optimized by artificial bee colony algorithm (ABC), genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO);moreover, they are also compared with thresholding methods based on criteria of between-class variance and Kapur's entropy optimized by bat algorithm. It is demonstrated that the proposed method is robust, adaptive, encouraging on the score of CPU time and exhibits the better performance than other methods involved in the paper in terms of objective function values. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image segmentation bat algorithm Fuzzy entropy Thresholding

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A Hybrid Multiobjective bat algorithm for Fuzzy Portfolio Optimization with Real-World Constraints

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2019年第1期21卷 291-307页

作者： Chen, Wei Xu, Wen Capital Univ Econ & Business Sch Informat Beijing Peoples R China

This paper investigates a fuzzy portfolio selection problem in the framework of multiobjective optimization. A multiobjective mean-semivariance-entropy model with fuzzy returns is proposed for portfolio selection. Specifically, it simultaneously optimizes the return, risk and portfolio diversification, taking into account transaction costs, liquidity, buy-in thresholds, and cardinality constraints. Since this kind of mixed-integer nonlinear programming problems cannot be efficiently solved by the conventional optimization approaches, a new metaheuristic method termed as the hybrid BA-DE is developed by combining features of the bat algorithm (BA) and differential evolution (DE). In order to demonstrate the effectiveness of the proposed approaches, we also provide a numerical example.

关键词： Portfolio selection Fuzzy variables Multiobjective optimization bat algorithm Differential evolution

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Integration of bat algorithm and salp swarm intelligence with stochastic difference variants for global optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第8期27卷 10777-10818页

作者： Li, Hongye Wang, Jianan Zhu, Yanjie Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China

This paper introduces a novel hybrid optimization algorithm named bat-salp swarm algorithm (BASSA). BASSA integrates the local exploitation capability of bat algorithm (BA) and the global exploration capability of salp swarm algorithm (SSA). Firstly, by introducing the echolocation of BA, the follower updating strategy of SSA is improved. Secondly, the algorithm selects between BA and SSA based on specific conditions. Finally, individuals undergo random differential mutation to increase population diversity, thereby avoiding local optima. To verify the effectiveness of the algorithm, we carry out experiments BASSA on 23 benchmark functions with different dimensions and compare it with 7 optimization algorithms, including BA, SSA, and 7 enhanced versions of SSA. Simulation results indicate that BASSA outperforms standard BA, SSA, and other enhanced algorithms in terms of mean and standard deviation. This suggests a significant improvement in optimization performance, with higher solution accuracy and faster convergence speed. Additionally, through performance evaluation on three real engineering problems, the results indicate that BASSA possesses strong optimization capabilities.

关键词： bat algorithm Salp swarm algorithm Stochastic difference variants Hybrid algorithm

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A novel modified bat algorithm hybridizing by differential evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 141卷 112949-000页

作者： Ylidizdan, Gulnur Baykan, Omer Kaan Selcuk Univ Kulu Vocat Sch Kulu Konya Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn Konya Turkey

The bat algorithm (BA) is one of the metaheuristic algorithms that are used to solve optimization problems. The differential evolution (DE) algorithm is also applied to optimization problems and has successful exploitation ability. In this study, an advanced modified BA (MBA) algorithm was initially proposed by making some modifications to improve the exploration and exploitation abilities of the BA. A hybrid system (MBADE), involving the use of the MBA in conjunction with the DE, was then suggested in order to further improve the exploitation potential and provide superior performance in various test problem clusters. The proposed hybrid system uses a common population, and the algorithm to be applied to the individual is selected on the basis of a probability value, which is calculated in accordance with the performance of the algorithms;thus, the probability of applying a successful algorithm is increased. The performance of the proposed method was tested on functions that have frequently been studied, such as classical benchmark functions, small-scale CEC 2005 benchmark functions, large-scale CEC 2010 benchmark functions, and CEC 2011 real-world problems. The obtained results were compared with the results obtained from the standard BA and other findings in the literature and interpreted by means of statistical tests. The developed hybrid system showed superior performance to the standard BA in all test problem sets and produced more acceptable results when compared to the published data for the existing algorithms. In addition, the contribution of the MBA and DE algorithms to the hybrid system was examined. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Heuristic algorithms bat algorithm Differential evolution algorithm Continuous optimization Large-scale optimization

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Hybrid momentum accelerated bat algorithm with GWO based optimization approach for spam classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第9期83卷 26929-26969页

作者： Dhal, Pradip Azad, Chandrashekhar Natl Inst Technol ITER Dept Comp Sci & Engn Jamshedpur 831014 Jharkhand India Siksha O Anusandhan Deemed Be Univ Dept Comp Sci & Engn Bhubaneswar 751030 Odisha India

Spam emails have become more prevalent, necessitating the development of more effective and reliable anti-spam filters. Internet users face security threats, and youngsters are exposed to inappropriate content while receiving spam emails. The gigantic data flow between billions of people and the tremendous number of features (attributes) makes the task more tiresome and complex. Feature Selection (FS) technique is essential for overwhelming accuracy, time and spatial complexity when we have high dimensional data (i.e., the number of features is very large). Spam emails have been successfully filtered and detected using Machine Learning (ML) methods by various researchers nowadays. This work proposes a hybrid binary Metaheuristic algorithm (MA) based Feature Selection (FS) approach for classifying email spam. The proposed FS approach is based upon two MA, i.e., bat algorithm (BA) with Grey Wolf Optimization(GWO). A novel concept of bat momentum has been introduced here, replacing the previous bat velocity. Two quantity, i.e., velocity and momentum, has an entirely different effect on the particle (i.e. bats). But they always follow the exact directions for both of them. To provide the best possible set of features for the FS process, the proposed approach uses an amalgamation technique to reach both the global and local optimum solution. To get the global optimum solution, a new momentum-based equation has been added to the BA, substituting the velocity equation from the prior BA. The GWO property has been added to the momentum-based equation mentioned above to improve the FS process search capabilities. Here a novel concept convergence timer has been introduced, which can eliminate the convergence issue in the iterative algorithm if it arises. A novel GWO based levy flight update has been introduced here to produce the local optimum solution. We have evaluated our proposed method on two benchmark spam corpora (Spambase, SpamAssassin) having different significant pro

关键词： Spam detection Feature selection bat algorithm Grey wolf optimization

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