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Improving Computational Efficiency of bat-inspired algorithm in Optimal Structural Design

ADVANCES IN STRUCTURAL ENGINEERING 2015年第7期18卷 1003-1015页

作者： Hasanebi, O. Azad, S. Kazemzadeh Middle E Tech Univ Dept Civil Engn TR-06531 Ankara Turkey Atilim Univ Dept Civil Engn Ankara Turkey

bat-inspired (BI) algorithm is a recent metaheuristic optimization technique that simulates echolocation behavior of bats in seeking a design space. Along the same line with almost all metaheuristics, this algorithm also entails a large number of time-consuming structural analyses in structural design optimization applications. This study is focused on improving computational efficiency of the BI algorithm in optimum structural design. The number of structural analyses required by BI algorithm in the course of design optimization is reduced considerably by incorporating an upper bound strategy (UBS) into the solution procedure. The performance of the resulting algorithm, i.e. UBS integrated BI algorithm (UBI), is evaluated in discrete sizing optimization of large-scale steel skeletal structures designed for minimum weight according to American Institute of Steel Construction-Allowable Stress Design provisions. The numerical results verify that the UBI results in a significant gain in the computational efficiency of the standard algorithm.

关键词： structural optimization discrete optimization optimum sizing metaheuristic search techniques bat-inspired algorithm steel structures

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A novel gene selection method using modified MRMR and hybrid bat-inspired algorithm with β-hill climbing

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APPLIED INTELLIGENCE 2018年第11期48卷 4429-4447页

作者： Alomari, Osama Ahmad Khader, Ahamad Tajudin Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Univ Sains Malaysia Sch Comp Sci George Town Malaysia Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine

This paper proposed a new gene selection method based on modified Minimum Redundancy Maximum Relevancy (MRMR) as a filtering approach and hybrid bat algorithm with beta-hill climbing as an efficient wrapper approach. The gene selection is a process of selecting the discriminative genes that aid in the development of efficient cancer diagnosis and classification. In general, the current filter-based approaches produced gene subset according to its discriminative power. However, one of the deficiencies of single filter approaches is that it has high variability of the classification results. Accordingly, this study aim to improve MRMR through incorporating its with ensemble of filters to increase the robustness and the stability of MRMR. The result of filtering-based approach is a set of discriminative genes. The wrapper-based approach considers the results from the filtering-based approach to formulate the gene selection search space. In wrapper approach, bat algorithm is tailored for gene selection problem and hybridized with a powerful local search method called beta hill climbing to further stress the deep learning side in the search space navigation and thus find a very robust and stable discriminative genes. bat-inspired algorithm (BA) is a recent swarm-based optimization method while beta-hill climbing is an exploratory local search. The proposed method is called Robust MRMR and Hybrid bat-inspired algorithm (rMRMR-HBA). To evaluate the proposed method, ten well-known microarray datasets are experimented with. These datasets are varies in terms of number of genes, samples, and classes. For performance evaluation, the proposed filtering-based approach (i.e., rMRMR) is initially tested against the standard MRMR and other well-regard filtering approaches. Thereafter, the wrapper-based approach (i.e., HBA) is evaluated by studying the convergence behavior of BA with and without beta-hill climbing. For comparative evaluation, the results of the proposed rMRMR-HBA we

关键词： Ensemble feature selection bat-inspired algorithm Optimization Gene selection MRMR SVM Classification

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Recent advances of bat-inspired algorithm, its versions and applications

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NEURAL COMPUTING & APPLICATIONS 2022年第19期34卷 16387-16422页

作者： Alyasseri, Zaid Abdi Alkareem Alomari, Osama Ahmad Al-Betar, Mohammed Azmi Makhadmeh, Sharif Naser Abu Doush, Iyad Awadallah, Mohammed A. Abasi, Ammar Kamal Elnagar, Ashraf Univ Kufa ECE Dept Fac Engn POB 21 Najaf Iraq Univ Warith Al Anbiyaa Coll Engn Karbala Iraq Univ Kufa Informat Res & Dev Ctr ITRDC Najaf Iraq Univ Sharjah MLALP Res Grp Sharjah U Arab Emirates Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Amer Univ Kuwait Dept Comp Coll Engn & Appl Sci Salmiya Kuwait Yarmouk Univ Dept Comp Sci Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related co

关键词： bat-inspired algorithm Metaheuristics Optimization Swarm intelligence

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A new enhanced bat-inspired algorithm for finding linear supply function equilibrium of GENCOs in the competitive electricity market

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ENERGY CONVERSION AND MANAGEMENT 2013年 76卷 1015-1028页

作者： Niknam, Taher Sharifinia, Sajjad Azizipanah-Abarghooee, Rasoul Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

This paper proposes a new enhanced bat-inspired algorithm to find out linear supply function equilibrium of Generating Companies (GENCOs) in a network-constrained electricity market where they have incomplete information about other rivals. The model enables a GENCO to link its bidding price with the bidding quantity of its product. In this regard, the social welfare maximization is applied to clearing the market and nodal pricing mechanism is utilized to calculate the GENCO's profit. It is formulated as a bi level optimization problem, where the higher level problem maximizes GENCO's payoff and the lower level problem solves the independent system operator's market clearing problem based on the maximization of social welfare. Due to non-convexity nature of the proposed bi level optimization problem, the mathematical-based optimization approach is incapable to solve the problem and obtain the nearly global optima. In order to overcome the obstacle of the conventional approaches, this study suggests a new meta-heuristic bat-inspired algorithm (BA) to achieve the nearly global solution of the hi level optimization problem. In addition a novel self-adaptive learning mechanism is utilized on the original BA to improve the population diversity and global searching capability. Numerical examples are applied to three test systems in order to evaluate the performances of the presented framework. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： bat-inspired algorithm Bidding strategies Bi-level optimization problem Generating company Incomplete information game Linear supply function equilibrium

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A scalable memory-enhanced swarm intelligence optimization method: fractional-order bat-inspired algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第6期15卷 2179-2197页

作者： Esfandiari, Ahmad Khaloozadeh, Hamid Farivar, Faezeh Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran KN Toosi Univ Technol Fac Elect & Comp Engn Dept Syst & Control Tehran Iran Islamic Azad Univ Dept Comp & Mechatron Engn Sci & Res Branch Tehran Iran

The bat-inspired algorithm (BA), as one of the swarm intelligence algorithms, has a high potential for solving global optimization problems. This algorithm possesses an additional inherent capability compared to other swarm intelligence algorithms, which is the inclusion of a local search mechanism. Although this increases the convergence speed of the algorithm, excessive focus on exploitation, especially in the initial iterations, may lead to premature convergence and stagnation at the local optimum. In this paper, to address this drawback and strengthen the exploration capability while also achieving a balance between it and the exploitation capability, an improved version of BA, called fractional-order BA (FOBA), is introduced. The development of velocity and position vectors in FOBA, using the concept of fractional-order derivatives, extends the memory related to the previous behaviors of artificial bats and controls the convergence of the algorithm. To evaluate the proposed algorithm, ten well-known benchmark functions are used, and the results are compared with standard and state-of-the-art metaheuristic algorithms that have been introduced recently. Experimental results show that FOBA performs better than all compared algorithms. Furthermore, the proposed algorithm is used to optimize the weights and bias of the MLP neural network using six classification datasets. The results demonstrate the effectiveness and efficiency of FOBA.

关键词： Fractional calculus bat-inspired algorithm Swarm intelligence Exploration Exploitation Multi-layer perceptron

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Optimization of neural network model using modified bat-inspired algorithm

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APPLIED SOFT COMPUTING 2015年 37卷 71-86页

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Hamdan, Abdul Razak Natl Univ Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Data Min & Optimizat Res Grp DMO Bangi 43600 Selangor Malaysia

The success of an artificial neural network (ANN) strongly depends on the variety of the connection weights and the network structure. Among many methods used in the literature to accurately select the network weights or structure in isolate;a few researchers have attempted to select both the weights and structure of ANN automatically by using metaheuristic algorithms. This paper proposes modified bat algorithm with a new solution representation for both optimizing the weights and structure of ANNs. The algorithm, which is based on the echolocation behaviour of bats, combines the advantages of population-based and local search algorithms. In this work, ability of the basic bat algorithm and some modified versions which are based on the consideration of the personal best solution in the velocity adjustment, the mean of personal best and global best solutions through velocity adjustment and the employment of three chaotic maps are investigated. These modifications are aimed to improve the exploration and exploitation capability of bat algorithm. Different versions of the proposed bat algorithm are incorporated to handle the selection of the structure as well as weights and biases of the ANN during the training process. We then use the Taguchi method to tune the parameters of the algorithm that demonstrates the best ability compared to the other versions. Six classifications and two time series benchmark data sets are used to test the performance of the proposed approach in terms of classification and prediction accuracy. Statistical tests demonstrate that the proposed method generates some of the best results in comparison with the latest methods in the literature. Finally, our best method is applied to a real-world problem, namely to predict the future values of rainfall data and the results show satisfactory of the method. (C) 2015 Elsevier B.V. All rights reserved.

关键词： bat-inspired algorithm Artificial neural network Chaotic map Time series prediction Classification Real world rainfall data

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bat-CLARA: bat-inspired algorithm for Clustering LARge Applications

BAT-CLARA: BAT-inspired algorithm for Clustering LARge Appli...

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8th IFAC Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Aboubi, Yasmine Drias, Habiba Kamel, Nadjet USTHB LRIA BP 32 El Alia Bab Ezzouar Algiers Algeria

bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of nficrobats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. Clustering remains one of the most difficult challenges in data mining. In this paper, an overview of literature methods is undertaken followed by the presentation of a new algorithm called bat-CLARA for clustering large data sets. It is based on bat behavior and k-medoids partitioning. The new technique is compared to the well-know partitioning algorithms PAM, CLARA, CEARANS and CLAM, a recent algorithm found in the literature. Experimental results show that, for the same tested datasets, bat-CLARA is more effective and more efficient than previous clustering methods. (C) 2016, IFAC (Informational rederation of Automatic Control) Hosting Elsevier Ltd. All rights reserved.

关键词： clustering algorithms bat-inspired algorithm metaheuristics medoids

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A bat-inspired algorithm for Router Node Placement with Weighted Clients in Wireless Mesh Networks 9

A Bat-inspired Algorithm for Router Node Placement with Weig...

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9th International Conference on Communications and Networking in China (CHINACOM 2014)

作者： Lin, Chun-Cheng Li, Yan-Sing Deng, Der-Jiunn Natl Chiao Tung Univ Dept Ind Engn & Management Hsinchu 300 Taiwan Natl Changhua Univ Educ Dept Comp Sci & Informat Engn Changhua 500 Taiwan

ISBN: (纸本)9781479959709

This paper considers the problem of the dynamic router node placement (dynRNP) with weighted clients in wireless mesh networks (WMNs), in which both mesh clients and mesh router have mobility, and mesh clients can switch on or off their network access at different times. The main objective of the conventional dynRNP problem is to maximize both network connectivity and client coverage, i.e., the size of the greatest subgraph component of the WMN topology and the number of the clients within radio coverage of mesh routers, respectively. Furthermore, this paper considers a more complicated dynRNP problem in WMNs, in which each client is associated with a weighted value (i.e., the mesh clients with higher weights values are served with larger priority), so that the two objective measures are redefined with consideration of clients' weights. We resolve the weighted problem by a bat-inspired algorithm (BA) with dynamic local search selection scheme, which simulates the echolocation of bats to find the optimal solution. Finally, the performance of the proposed BA is compared with the original BA and PSO, and the effects of weights are analyzed.

关键词： wireless mesh network bat-inspired algorithm dynamic router node placement

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A Hybrid algorithm Based on bat-inspired algorithm and Differential Evolution for Constrained Optimization Problems

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2015年第4期29卷 1559007-1559007页

作者： Pei, Shengyu Ouyang, Aijia Tong, Lang Guangxi Univ Finance & Econ Fangchenggang Coll Fangchenggang 538100 Peoples R China Changsha Nanfang Profess Coll Dept Aviat Serv Changsha 410208 Hunan Peoples R China Hunan Inst Traff Engn Dept Elect & Informat Engn Hengyang 421001 Hunan Peoples R China

How to solve constrained optimization problems (COPs) is a significant research issue and we combine the bat-inspired algorithm (BA) with differential evolution (DE) into a new hybrid algorithm called BA-DE for solving the COPs. Traditional BAs are prone to sink into stagnation or local optima when no bat individual founds a better location than the past locations for several generations. DE is adopted for updating the past location of bat individuals to force BA to jump out of stagnation or local optima, since it has a great local searching capability. The performance of BA-DE algorithm is improved by the proposed hybrid mechanism. We use 24 well-known benchmark functions to verify the overall performance of our proposed algorithm. Comparisons show that BA-DE outperforms most advanced methods in terms of the final solution's quality.

关键词： bat-inspired algorithm BA-DE constrained optimization constraint-handling method differential evolution

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Particle swarm and bat-inspired optimization algorithm-based parameter identification of Krasnosel’skii-Pokrovskii model for piezoelectric hysteresis

Particle swarm and bat-inspired optimization algorithm-based...

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第五届国际仿生工程学术大会(ICBE2016)

作者： Rui Xu Miaolei Zhou Yantao Tian Department of control science and engineering Jilin University

The piezoelectric ceramics is a kind of novel intelligent materials and possesses advantages of ultrafine resolution, high stiffness and fast frequency response. It is an excellent choice as an actuator to be applied widely to micro-nano positioning system. However, one of the drawbacks of the piezoelectric actuator is the obvious hysteresis nonlinearity to limit the accuracy of positioning. The present study focuses on describing the hysteresis nonlinearity between input voltage and output displacement of piezoelectric ceramics. In order to investigate the hysteresis nonlinearity of piezoelectric ceramics, a series of models are presented, such as BoucWen model, Preisach model, Krasnosel’skii-Pokrovskii model and so on. However, the main difficulty is how to select an effective method to identify the unknown parameters of these hysteresis nonlinearity models. In this paper, we propose a novel hybrid optimization algorithm of particle swarm and bat-inspired to identify the density function of Krasnosel’skii-Pokrovskii model. The proposed hybrid optimization algorithm has the fast convergence of bat-inspired and the global search ability of particle swarm. During operation of the optimization algorithm, we divided the swarm into two different parts randomly as the initial value. One set of data is optimized by particle swarm algorithm and find the optimal value i.e. best1, the other set of data is optimized by bat-inspired algorithm and the optimal value is best2. Then we select the bigger one between best1 and best2 i.e. Gbest as the global optimal value of the two algorithms for the next iteration *** taking advantage of the optimization algorithm, an objective function is defined firstly. Then the density function of Krasnosel’skii-Pokrovskii model is identified by using bat-inspired algorithm and hybrid optimization algorithm of particle swarm and bat-inspired respectively. The simulations show that the displacement error of identification of Kras

关键词： particle swarm optimization algorithm bat-inspired algorithm Krasnosel’skii-Pokrovskii model

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