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An adaptive reinforcement learning-based bat algorithm for structural design problems

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第2期14卷 114-124页

作者： Meng, Xian-Bing Li, Han-Xiong Gao, Xiao-Zhi Cent South Univ Coll Mech & Elect Engn Changsha Hunan Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Univ Eastern Finland Sch Comp Kuopio Finland Lappeenranta Univ Technol Machine Vis & Pattern Recognit Lab Lappeenranta Finland

A reinforcement learning-based bat algorithm is proposed for solving structural design problems. By incorporating reinforcement learning, the algorithm's performance feedback is formulated to adaptively select between algorithm's different operators. To improve the solution diversity, a new metric of individual difference is designed. The individual difference-based strategies are proposed to adaptively tune the algorithm's parameters. The variations of the pulse rates and loudness are newly designed to formulate their effects on the local search and foraging efficiency. Simulations and comparisons based on ten structural design problems with continuous/discrete variables demonstrate the superiority of the proposed algorithm.

关键词： bat algorithm reinforcement learning individual difference adaptive tuning

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Improved self-adaptive bat algorithm with step-control and mutation mechanisms

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JOURNAL OF COMPUTATIONAL SCIENCE 2019年 30卷 65-78页

作者： Lyu, Shilei Li, Zhen Huang, Yonglin Wang, Jianhua Hu, Jie South China Agr Univ Coll Elect Engn Guangzhou 510642 Guangdong Peoples R China Guangdong Engn Res Ctr Monitoring Agr Informat Guangzhou 510642 Guangdong Peoples R China China Agr Res Syst Div Citrus Machinery Guangzhou 510642 Guangdong Peoples R China

The bat algorithm (BA) is a new search optimization algorithm, inspired by bats' echolocation behavior. However, it is prone to fall into local optima and has low solution accuracy. This study proposes an improved self-adaptive bat algorithm (SABA) with adaptive step-control and mutation mechanisms. The step-control mechanism uses two frequencies to adapt the step sizes used for the global and local searches, and the mutation mechanism could improve the algorithm's ability to avoid local optima. SABA's parameters are analyzed to ensure convergence. Its optimization and convergence performance are experimentally studied using 12 unimodal and multimodal functions;compared with a range of baseline algorithms, it can effectively avoid local optima and exhibits high solution accuracy. Further, its practical performance is evaluated using engineering optimization problems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： bat algorithm Self-adaptive Step-control mechanism Mutation mechanism

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Swarm bat algorithm with improved search (SBAIS)

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SOFT COMPUTING 2019年第22期23卷 11461-11491页

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci New Delhi India Univ Delhi Dyal Singh Coll New Delhi India

bat algorithm (BA) is a powerful nature-inspired swarm algorithm which finds applicability to a diverse range of problem domains. Though it is efficient, it suffers from two handicaps: possibility of being trapped in local optima and lost convergence speed as the algorithm progresses. This paper proposes swarm bat algorithm with improved search (SBAIS). SBAIS gains superior exploration capabilities by employing swarming characteristics inspired by shuffled complex evolution (SCE) algorithm. Best bats of the population are kept in a super-swarm, while all other bats are partitioned according to SCE. The super-swarm uses the search mechanism of bat algorithm with improved search to perform refined search around the best solution, which makes sure that the convergence speed of the algorithm is not lost. Every other swarm gets one solution from the super-swarm before starting their evolution process. These swarms evolve using standard bat algorithm, helping the algorithm to escape any possible local optima. SBAIS further keeps a check on the overall diversity of the population. If the diversity drops below a given threshold value, new random solutions are added to the population. Performance of SBAIS is validated by comparing it to BA and fourteen recent variants of bat algorithm over 30 standard benchmark optimization functions, CEC'05 and CEC'14 function sets. Results established the superiority of SBAIS over the compared algorithms.

关键词： bat algorithm bat algorithm with improved search Shuffled complex evolution algorithm Numerical optimization

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A novel chaotic bat algorithm based on catfish effect for engineering optimization problems

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ENGINEERING COMPUTATIONS 2019年第5期36卷 1744-1763页

作者： Xiao, Wensheng Liu, Qi Zhang, Linchuan Li, Kang Wu, Lei China Univ Petr Coll Mech & Elect Engn Qingdao Shandong Peoples R China

Purpose bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel chaotic bat algorithm based on catfish effect (CE-CBA), which can effectively deal with optimization problems in real-world applications. Design/methodology/approach Incorporating chaos strategy and catfish effect, the proposed algorithm can not only enhance the ability for local search but also improve the ability to escape from local optima traps. On the one hand, the performance of CE-CBA has been evaluated by a set of numerical experiment based on classical benchmark functions. On the other hand, five benchmark engineering design problems have been also used to test CE-CBA. Findings The statistical results of the numerical experiment show the significant improvement of CE-CBA compared with the standard algorithms and improved bat algorithms. Moreover, the feasibility and effectiveness of CE-CBA in solving engineering optimization problems are demonstrated. Originality/value This paper proposed a novel BA with two improvement strategies including chaos strategy and catfish effect for the first time. Meanwhile, the proposed algorithm can be used to solve many real-world engineering optimization problems with several decision variables and constraints.

关键词： bat algorithm Catfish effect Chaotic initialization Chaotic mutation Engineering optimization problems

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A Comparison of bat and Firefly algorithm in Neighborhood based Collaborative Filtering

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第9期12卷 643-649页

作者： Hartatik Sejati, Bayu Permana Hamdani, Hamdani Syafrianto, Andri Univ Amikom Yogyakarta Fac Comp Sci Yogyakarta Indonesia Univ Mulawarman Dept Informat Samarinda Indonesia STMIK El Rahma Dept Informat Yogyakarta Indonesia

The recommender system is a knowledge-based filtering system that predicts the users' rating and preference for what they might desire. Simultaneously, the neighborhood method is a promising approach to perform predictions, resulting in a high accuracy based on the common items. This method, furthermore, could affect the resulting accuracy value because when each user provides limited data and sparsity, the accuracy of value might be narrow down as a consequence. In this research, we use the Swarm Intelligent (SI) technique in the recommender system to overcome this problem, whereby SI will train each feature to optimal weight. This technique's main objective is to form better groups of similar users and improve recommendations' accuracy. The intelligent swarm technique used to compare its accuracy to help provide recommendations is the Firefly and bat algorithm. The results show that the Firefly algorithm has slightly better performance than the bat algorithm, with a difference in the mean absolute error of 0.02013333. The significance test using the independent t-test method states that no statistically significant difference between bat and Firefly algorithm.

关键词： bat algorithm firefly algorithm collaborative filtering recommender system swarm intelligent

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Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and bat algorithm

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IEEE ACCESS 2020年 8卷 204744-204762页

作者： Shah, Abdul Salam Nasir, Haidawati Fayaz, Muhammad Lajis, Adidah Ullah, Israr Shah, Asadullah Univ Kuala Lumpur UniKl MIIT Dept Comp Engn Kuala Lumpur 50250 Malaysia Univ Cent Asia Naryn Dept Comp Sci Naryn 722918 Kyrgyzstan Virtual Univ Pakistan Dept Comp Sci Lahore 54792 Pakistan Int Islamic Univ Malaysia IIUM Dept Informat Syst Kulliyyah ICT Kuala Lumpur 50728 Malaysia

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes' growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (degrees F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2);when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system's overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maxim

关键词： Artificial neural network bat algorithm deep extreme learning machine energy optimization energy prediction optimization algorithms smart building smart home

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A multi-objective bat algorithm for community detection on dynamic social networks

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APPLIED INTELLIGENCE 2019年第6期49卷 2119-2136页

作者： Messaoudi, Imane Kamel, Nadjet Univ Sci & Technol Houari Boumediene LRIA Lab Dept Comp Sci BP 32 El Alia 16111 Bab Ezzouar Algiers Algeria Univ Ferhat Abbas Setif 1 Dept Comp Sci Fac Sci Setif Algeria

Many evolutionary algorithms have been proposed to deal with the problem of community detection in social dynamic networks. Some algorithms need to fix parameters in advance;others use a random process to generate the initial population and to apply the algorithm operators. These drawbacks increase the search space and cause a high spatial and temporary complexity. To overcome these weaknesses, we propose in this paper a novel multi-objective bat algorithm that uses Mean Shift algorithm to generate the initial population, to obtain solutions of high quality. In our proposal, bat algorithm simultaneously optimizes the modularity density and the normalized mutual information of the solutions as objective functions. The operators of the algorithm are applied to the problem of community detection in social dynamic networks by giving another sense to the velocity, frequency, loudness and the pulse rate of natural bat. The algorithm keeps the principal of the Mean Shift algorithm to generate new solution and avoid the random process by defining a new mutation operator. The algorithm does not need to the non-dominated sorted approach or the crowding distance, but it attributes a weight to each objective function. The method is tested on artificial and real dynamic networks and the experiments show satisfactory results in terms of normalized mutual information, modularity and error rate.

关键词： Community detection Modularity density Mean shift bat algorithm Dynamic network

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Determination of the optimal drill path sequence using bat algorithm and analysis of its optimization performance

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2019年第2期36卷 97-112页

作者： Diyaley, Sunny Biswas, Aniket Burman Chakraborty, Shankar Sikkim Manipal Inst Technol Dept Mech Engn Mazitar India Jadavpur Univ Dept Prod Engn Kolkata India

Generation of circular holes with varying dimensions in diverse parts/products is one of the most common operations in any manufacturing industry. Determination of the optimal drill path sequence is an important problem in hole-drilling operation using CNC machines. This problem's structure is quite analogous to travelling salesman problem and hence, is NP-complete belonging to both NP and NP-hard complexity classes. Due to exponential increase in number of possible solutions when the number of holes to be drilled increases, various evolutionary algorithms are seemed to be the viable choices in solving this type of optimization problem. In this paper, an almost unexplored swarm-based algorithm, in the form of bat algorithm, is applied to determine the optimal path sequences for different layouts consisting of 5 x 5, 7 x 7, 9 x 9 and 11 x 11 matrices of holes, and a 14-hole benchmark model, taking into account machining time, machining cost and non-productive cost of the related drilling operation.

关键词： Optimization Drill path bat algorithm Meta-heuristic algorithms

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Enhanced convergence of bat algorithm based on dimensional and inertia weight factor

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2019年第4期31卷 452-458页

作者： Ramli, M. R. Abas, Z. Abal Desa, M. I. Abidin, Z. Zainal Alazzam, M. B. Univ Teknikal Malaysia Melaka Dept Informat & Commun Technol C ACT Johor Baharu Malaysia Univ Teknikal Malaysia Melaka Optimisat Modelling Anal Simulat & Scheduling Opt Johor Baharu Malaysia

Heuristic optimisation method typically hinges on the efficiency in exploitation and global diverse exploration. Previous research has shown that bat algorithm could provide a good exploration and exploitation of a solution. However, bat algorithm can be get trapped in a local minimum in some multi-dimensional functions. Thus, the phenomenon of slow convergence rate and low accuracy still exits. This paper aims to modify the exploitation of bat algorithm in optimising the solution by modifying dimensional size and providing inertia weight. Benchmark test function is then performed for the basic bat algorithm and the modified bat algorithm (MBA) for comparison. The result is analysed according to the number of iteration needed for a convergence toward the objective. From simulations, it is found that the modified dimension and additional inertia weight factor of bat algorithm proves to be more effective than the basic bat algorithm in terms of searching for a solution while improving quality of results in all cases or significantly improving convergence speed. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： bat algorithm Iteration Exploration and exploitation Metaheuristic

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A dynamically adapted and weighted bat algorithm in image enhancement domain

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EVOLVING SYSTEMS 2019年第2期10卷 129-147页

作者： Dhal, Krishna Gopal Das, Sanjoy Midnapore Coll Autonomous Dept Comp Sci & Applicat Paschim Medinipur W Bengal India Univ Kalyani Dept Engn & Technol Studies Kalyani W Bengal India

This paper proposed one improved bat algorithm (BA) by incorporating one novel dynamic inertia weight and proposed self-adaptive strategies over algorithm's parameters. Chaotic sequence and developed population diversity metric are employed over BA to perform the local search and generate one improved initial population respectively. The efficacy of the proposed BA is verified by applying it to set the parameters properly of the proposed histogram equalization (HE) variant;called weighted and thresholded Bi-HE (WTBHE). The proper setting of these parameters is time consuming but crucially effects WTBHE's image enhancement ability. One novel co-occurrence matrix based objective function has been also formulated which facilitates the proposed BA for finding the optimal parameters of WBTHE which produces original brightness preserved enhanced images. Experimental results prove that the proposed BA is superior to simple BA in terms of convergence speed, robustness and maximization of objective function and WBTHE is better than some existing well-known HE variants in brightness preserving image enhancement field.

关键词： Contrast enhancement bat algorithm Dynamic inertia weight Chaotic sequence Co-occurrence matrix Population creation

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