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A bat algorithm for SDN network scheduling

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2018年第1期2018卷 1页

作者： Lin, Rongheng Ye, Zezhou Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

With the development of SDN, end-to-end network reservation becomes a reality. Resource reservation can become an online service for network users, and the network faces new challenges on how to allocate users' resource dynamically. To solve the problem, this paper proposed a bat-based algorithm for SDN network scheduling along with a network request model. The network resource request model is for characterizing users' simultaneous network resource request in SDN. Based on the model, this paper transforms the resource reservation problem into a multiple-knapsack problem and proposes a bat algorithm to optimize the solution. Experiments show that the proposed algorithm is better than greedy and dynamic programming algorithm. The major contribution of this paper is to model the SDN users' resource requests and apply bat algorithm for the solution.

关键词： SDN Network scheduling bat algorithm

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Multi-objective bat algorithm for mining numerical association rules

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2018年第4期11卷 239-248页

作者： Heraguemi, Kamel Eddine Kamel, Nadjet Drias, Habiba Msila Univ Fac Technol Comp Sci Dept Msila Algeria Univ Ferhat Abbas Setif 1 Fac Sci Dept Comp Sci Setif Algeria USTHB LRIA Algiers Algeria

Numerical association rule mining problem attracts the attention of researchers because of the various applications and its importance in our world with the fast growth of the stored data. ARM is computationally very expensive because the number of rules grows exponentially as the number of items in the database increases. Generally, ARM is more complex when we introduce the quality criteria and the usefulness to the user. In this paper deals with the problem of numerical ARM. In which, we propose a new multi-objective meta-heuristic called multi-objective bat algorithm for association rules mining (MOB-ARM). To identify more useful and understandable rules, we introduce four quality measures of association rules: Support, confidence, comprehensibility, and interestingness, in two objective functions. A series of experiments are carried out on several well-known benchmarks in ARM field and the performance of our proposal are evaluated and compared with those of other recently published methods including mono-objective and multi-objective approaches. Also, the paper presents a comparative study with three other methods dealing with multi-objective association rule mining. The obtained results show that our method is competitive with other methods and extract useful and understandable rules.

关键词： numerical association rules mining ARM bat algorithm multi-objective optimisation support confidence comprehensibility interestingness

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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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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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Incorporating a modified uniform crossover and 2-exchange neighborhood mechanism in a discrete bat algorithm to solve the quadratic assignment problem

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EGYPTIAN INFORMATICS JOURNAL 2017年第3期18卷 221-232页

作者： Riffi, Mohammed Essaid Saji, Yassine Barkatou, Mohammed Chouaib Doukkali Univ Fac Sci Dept Comp Sci LAROSERI Lab El Jadida 24000 Morocco Chouaib Doukkali Univ Fac Sci Dept Math El Jadida 24000 Morocco

The bat algorithm is one of the recent nature-inspired algorithms, which has been emerged as a powerful search method for solving continuous as well as discrete problems. The quadratic assignment problem is a well-known NP-hard problem in combinatorial optimization. The goal of this problem is to assign n facilities to n locations in such a way as to minimize the assignment cost. For that purpose, this paper introduces a novel discrete variant of bat algorithm to deal with this combinatorial optimization problem. The proposed algorithm was evaluated on a set of benchmark instances from the QAPLIB library and the performance was compared to other algorithms. The empirical results of exhaustive experiments were promising and illustrated the efficacy of the suggested approach. (C) 2017 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license.

关键词： bat algorithm Quadratic assignment problem NP-hard problem Combinatorial optimization problem

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Well placement optimization using metaheuristic bat algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2017年 150卷 348-354页

作者： Naderi, Meysam Khamehchi, Ehsan Amirkabir Univ Technol Fac Petr Engn Tehran Polytech Hafez Ave Tehran Iran

The design of an optimal field development and production management is a complicating task because of influencing various factors on decision-making process. Typical factors include number and type of wells, well locations, production constraints, economic factors like capital expenditure, operating costs, and oil sale price. The situation is further complicated due to the uncertainty associated with various effective engineering and geological parameters. In this study, three meta heuristics algorithms of genetic algorithm (GA), particle swarm optimization (PSO) and bat inspired algorithm (BA) are used for optimal determination of six production well locations. Net present value (NPV) is used as an objective function in optimization process. PUNQ-S3 benchmark model is simulated in MATLAB environment in order to search the entire complex reservoir during optimization. Next, the effectiveness of algorithms will be compared in terms of convergence rate and NPV improvement over iterations. The simulation results show that the BA is superior since it reduces the number of functional evaluations and thus improving the computational efficiency. In addition, the BA provides better NPV improvement over PSO and GA. The results indicate that the BA increases NPV by 7.5% and 21.7% over PSO and GA respectively.

关键词： Well placement optimization Net present value Genetic algorithm Particle swarm optimization bat algorithm

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FPGA based hardware implementation of bat algorithm

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APPLIED SOFT COMPUTING 2017年 58卷 378-387页

作者： Ben Ameur, Mohamed Sadok Sakly, Anis Univ Monastir Lab Elect & Microelect Monastir Tunisia Univ Monastir Natl Engn Sch Monastir Res Unit ESIER Monastir Tunisia

Several meta-heuristics algorithms are used mainly for research of global optimal solutions for real and non-convex problems. Some of them are the Genetic algorithms (GA), Cuckoo Search algorithms (CS), Particle Swarm Optimization (PSO). Some algorithms have achieved satisfactory results but not all of them. Therefore, new algorithms give better optimization to solve many problems having continuous search space like bat algorithm (BA). Thats why we proposed a new hardware implementation on Field Programmable Gate Array (FPGA) of bat algorithm, it is a new proposed meta-heuristic for global optimization. The work presented in this article is designed to use new digital dedicated hardware solutions such as FPGAs that are available to generate a better implementation of bat algorithm. This circuit is well adapted to many applications because its material structure is molded with the requirements of calculations. Moreover the inherent parallelism of these new hardware solutions and their large computing capabilities makes the computing time negligible despite the complexity of these algorithms. (C) 2017 Published by Elsevier B.V All rights reserved.

关键词： bat algorithm Finite State Machine FPGA Parallele programming Optimisation

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Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a bat algorithm and Fuzzy Logic

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ENERGIES 2018年第1期11卷 161页

作者： Fayaz, Muhammad Kim, DoHyeun Jeju Natl Univ Dept Comp Engn Jeju City 63243 South Korea

Energy management in residential buildings has grabbed the attention of many scientists for the last few years due to the fact that the residential sector consumes the highest amount of total energy produced by different energy resources. To manage the energy in residential buildings effectively, an efficient energy control system is required, capable of decreasing the total energy consumption without compromising the user-preferred environment inside the building. In the literature, many approaches have been proposed to achieve the goals of minimizing the energy consumption and maximizing the user preferred comfort by keeping different parameters under consideration, but all these methods face some problems in resolving the issue properly. The bat algorithm is one of the most recently introduced optimization approaches that has drawn the attention of researchers to apply it for solving different types of optimization problems. In this paper, the bat algorithm is applied for energy optimization in residential buildings, which is one of the most focused optimization problems in recent years. Three environmental parameters, namely temperature, illumination and air quality are bat algorithm inputs and optimized values of these parameters are the outputs. The error difference between the environmental parameters and optimized parameters are inputs of the fuzzy controllers which give energy as output which in turn change the status of the concerned actuators. It is proven from the experimental results that the proposed approach has been effectively successful in managing the whole energy consumption management system.

关键词： bat algorithm comfort index energy optimization fuzzy logic membership functions (MFs) residential building

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Dynamic Perceptive bat algorithm Used to Optimize Particle Filter for Tracking Multiple Targets

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JOURNAL OF AEROSPACE ENGINEERING 2018年第3期31卷 4018015.1-4018015.1页

作者： Chen, Zhimin Bo, Yuming Tian, Mengchu Wu, Panlong Ling, Xiaodong China Satellite Maritime Tracking & Controlling D Jiangyin 214431 Peoples R China Hongjiaobeilu 243 Jiangyin 214431 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Xiaolingwei 200 Nanjing 210094 Jiangsu Peoples R China

Resampling of standard particle filters will cause particle depletion and require abundant particles in the state estimation, which can hardly meet the accuracy and velocity requirements of a modern radar tracking system. This paper proposes an improved multiple-maneuvering-target tracking algorithm based on a novel intelligent particle filter. The improved algorithm combines the bat algorithm and particle filters and takes particles as bats to simulate behavior of bats in pursuit of prey. By adjusting frequency, volume, and pulse rate, particle groups search for the optimal value and move to high likelihood areas intelligently under the guidance of the optimal particle. Meanwhile, it improves the optimization mechanism of the bat algorithm;dynamic control of searching velocity and perception range are proposed. It makes the algorithm seek optimization within a self-adaptive cognition range, and the optimizing rate can be adjusted dynamically to control the balance of global and local optimizing abilities. Furthermore, the improved algorithm combines interacting multiple model and joint probabilistic data association, which enables improved accuracy in target tracking and robustness in a complex environment by iterative optimization. Simulation results show that the improved algorithm enhances the performance of a multiple-maneuvering-target tracking system. (c) 2018 American Society of Civil Engineers.

关键词： Particle filter bat algorithm Dynamic perceptive Multiple targets

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Hybrid bat algorithm and direct search methods for solving minimax problems

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International Journal of Hybrid Intelligent Systems 2018年第4期14卷 209-223页

作者： Ali, Ahmed F. Tawhid, Mohamed A. Department of Computer Science Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Department of Mathematics and Statistics Faculty of Science Thompson Rivers University Kamloops BC Canada Department of Mathematics and Computer Science Faculty of Science Alexandria University Alexandria Egypt

The aim of this paper is to produce a new hybrid algorithm to solve minimax problems by combining the bat algorithm with direct search methods. The proposed algorithm is called hybrid bat direct search algorithm (HbatDS). In HbatDS, the global exploration and the local exploitation process are balanced. The bat algorithm has a good ability to make exploration and exploitation search. The exploitation capability of the proposed algorithm is increased by invoking the pattern search method as a local search method instead of the random walk method in the standard bat algorithm. In the final stage of the algorithm, the Nelder-Mead method is applied in order to refine the best found solution from the bat and pattern search method instead of running the algorithm more iterations without any improvements in the fitness function value. The performance of the HbatDS algorithm is investigated by applying it on 10 minimax problems and comparing it against 8 benchmark algorithms. The experimental results indicate that HbatDS is a promising algorithm and outperforms the other algorithms in most cases.

关键词： bat algorithm direct search methods pattern search Nelder-Mead method minimax problemsMathematics Subject Classification. 90C47 90C30 49K35 68U20 68W01.

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