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检索条件"主题词=Bat algorithm"

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GPU-powered bat algorithm for the Parameter Estimation of Biochemical Kinetic Values 13

GPU-powered Bat Algorithm for the Parameter Estimation of Bi...

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13th IEEE Annual Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)

作者： Tangherloni, Andrea Nobile, Marco S. Cazzaniga, Paolo Univ Milano Bicocca Dept Informat Syst & Commun I-20126 Milan Italy SYSBIO IT Ctr Syst Biol I-20126 Milan Italy Univ Bergamo Dept Human & Social Sci I-24129 Bergamo Italy

ISBN: (纸本)9781467394727

The emergent behavior of biochemical systems can be investigated by means of mathematical modeling and computational analyses, which usually require the automatic inference of the unknown values of the model's parameters. This problem, known as Parameter Estimation (PE), is usually tackled with bio-inspired meta-heuristics for global optimization, most notably Particle Swarm Optimization (PSO). In this work we assess the performances of PSO and bat algorithm with differential operator and Levy flights trajectories (DLBA). In particular, we compared these meta-heuristics for the PE using two biochemical models: the expression of genes in prokaryotes and the heat shock response in eukaryotes. In our tests, we also evaluated the impact on PE of different strategies for the initial positioning of individuals within the search space. Our results show that DLBA achieves comparable results with respect to PSO, but it converges to better results when a uniform initialization is employed. Since every iteration of DLBA requires three fitness evaluations for each bat, the whole methodology is built around a GPU-powered biochemical simulator (cupSODA) which is able to parallelize the process. We show that the acceleration achieved with cupSODA strongly reduces the running time, with an empirical 61 x speedup that has been obtained comparing a Nvidia GeForce Titan GTX with respect to a CPU Intel Core i7-4790K. Moreover, we show that DLBA always outperforms PSO with respect to the computational time required to execute the optimization process.

关键词： bat algorithm GPGPU Computing Parameter Estimation Particle Swarm Optimization

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An Improved bat algorithm with Variable Neighborhood Search for Global Optimization

An Improved Bat Algorithm with Variable Neighborhood Search ...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Wang, Gai-Ge Lu, Mei Zhao, Xiang-Jun Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

ISBN: (纸本)9781509006229

bat algorithm (BA) is newly proposed bio-inspired metaheuristic algorithm with the inspiration of the echolocation of bats in nature. Several experimental results have proven to the effectiveness and performance of BA. However, BA may fail to find the global optimal solution occasionally. In this paper, a kind of classical search technology, called variable neighborhood search (VNS), is incorporated into BA as a local search tool. An improved version of BA namely variable neighborhood bat algorithm (VNBA), is thus proposed. In VNBA, the classic BA as a global search tool searches the whole space globally, and this can significantly shrink the search space. Subsequently, VNS as a local search tool is implemented to find the final best solution within the small promising area. After that, the VNBA is benchmarked by sixteen standard benchmark functions. The experimental results imply that VNBA takes the absolute advantage over the basic BA.

关键词： Swarm intelligence Optimization bat algorithm Variable neighborhood search Benchmark functions

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A Novel Instantaneous Exploitation Based bat algorithm 24

A Novel Instantaneous Exploitation Based Bat Algorithm

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24th Iranian Conference on Electrical Engineering (ICEE)

作者： Ahmadi, A. H. Nikravesh, S. K. Y. Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran

ISBN: (纸本)9781467387897

The use of meta -heuristic algorithms for solving real world problems increases day by day. bat algorithm is a meta heuristic optimization algorithm based on the echolocation behavior of microbats. bat algorithm has advantage which claimed to provide very quick convergence at a very initial stage by automatic switching from exploration to exploitation. Hereby, algorithm loses exploration capability highly at the following iterations, and it may lead to premature convergence. To cope with this deficiency, this paper proposes a novel version of bat algorithm based on instantaneous exploitation feature. Conducted experiments on ten well-known benchmark test functions have shown that the proposed Instantaneous Exploitation Based bat algorithm can outperform the standard bat algorithm.

关键词： bat algorithm Good combination Levy flight Instantaneous exploitation Worst solution

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Research & Analysis of Advancements In bat algorithm 3

Research & Analysis of Advancements In BAT Algorithm

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sharma, Shabnam Luhach, Ashish Kr. Jyoti, Kiran Lovely Profess Univ Phagwara India Guru Nanak Dev Engn Coll Ludhiana Punjab India

ISBN: (纸本)9789380544199

Optimization is defined as finding the alternate solutions to a problem, tinder the given constraints. Maximizing the performance level is one of the objectives of optimization, which can be achieved by satisfying desirable factors and minimizing the undesirable ones. When a problem cannot be solved in polynomial time or consumes long time to solve, then alternative solutions of the problem will be explored and near optimal solution is accepted. To tackle these napes of problems, one of the three approaches are used: Heuristics, Meta-Heuristics and Hyper-Heuristics. bat algorithm is one of the meta-heuristic algorithms, which optimizes the solution using echolocation and applicable in solving various combinatorial problems. In this research work, analysis of various bat variants is done and further research areas are explored, in the field of meta heuristic approaches.

关键词： bat algorithm Exploration Exploitation Heuristic Meta-Heuristic

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A bat algorithm to solve the Manufacturing Cell Design Problem 11

A Bat Algorithm to solve the Manufacturing Cell Design Probl...

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11th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Soto, Ricardo Crawford, Broderick Zec, Carolina Alarcon, Andres Almonacid, Boris Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Univ San Sebastian Santiago Chile Univ Cent Chile Santiago Chile

ISBN: (纸本)9789899843462

Manufacturing Cell Design is a problem that is aimed at distributing the different machines of a center of production in cells, so that the parts of the final product to be manufactured with the least amount of travel in its manufacturing process. bat algorithm is an algorithm inspired by the behavior of echolocation in bats. Using a balance sheet of the frequency and automatic tuning of exploration and exploitation by controlling the rate of volume and emission pulses. The following work shows the resolution of the Manufacturing Cell Design, by means of bat algorithm, an algorithm that proved to be effective for this problem because it has reached the optimum in all problems with which the tests were conducted.

关键词： Manufacturing cell design problem bat algorithm Metaheuristics

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Design of Optimal Controller for Magnetic Levitation System Using Brownian bat algorithm

Design of Optimal Controller for Magnetic Levitation System ...

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International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES)

作者： Sundaravadivu, K. Ramadevi, C. Vishnupriya, R. St Josephs Coll Engn Dept Elect & Instrumentat Engn Madras 600119 Tamil Nadu India

ISBN: (纸本)9788132226567;9788132226543

This paper presents a method to design the One Degree Of Freedom(1DOF) and Two Degrees Of Freedom (2DOF) PID controllers for an unstable Magnetic Levitation System (MLS) using Brownian bat algorithm (BBA). Minimization of objective function (Jmin) is considered to guide the search towards the optimal controller parameters. A simulation study is carried to validate the performance of the proposed BBA with other successful heuristic algorithms, such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO) and Firefly algorithm (FA). This study confirms that the proposed method offers better result in reference tracking operation with reduced error for 1DOF and 2DOF PID structure compared with other algorithms considered in this work.

关键词： Magnetic levitation Degrees of freedom PID Brownian distribution bat algorithm

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Enhanced Shuffled bat algorithm (EShbat) 5

Enhanced Shuffled Bat Algorithm (EShBAT)

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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

ISBN: (纸本)9781509020287

bat algorithm (BA) is a simple and effective global optimization algorithm which has been applied to a wide range of real-world optimisation problems. Various extensions to bat algorithm have been proposed in the past;prominent amongst them being Shbat. Shbat is a hybrid between BA and Shuffled Frog Leaping algorithm -SFLA;a memetic algorithm based on food search behavior of frogs. Shbat integrates the shuffling and reorganization technique of SFLA to enhance the exploitation capabilities of bat. This paper proposes Enhanced Shuffled bat algorithm (EShbat) an extension to Shbat. In Shbat, different memeplexes evolve independently, with different cultures. EShbat improves the exploitation capabilities of Shbat by grouping together the best of each memeplex to form a super-memeplex. This super-memeplex evolves independently to further exploit the best solutions. The performance of EShbat is verified over 30 well-known benchmark functions. Experimental results indicate a significant improvement of EShbat over BA and Shbat.

关键词： bat algorithm Shuffled Frog Leaping algorithm Swarm intelligence Global Optimisation Numerical optimisation

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Dynamic Traffic Light Timing Optimization Model Using bat algorithm 2

Dynamic Traffic Light Timing Optimization Model Using Bat Al...

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2nd International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Jintamuttha, Konlapat Watanapa, Bunthit Charoenkitkarn, Nipon King Mongkuts Univ Technol Thonburi Sch Informat Technol Bangkok Thailand

ISBN: (纸本)9781467398725

Nowadays, traffic congestion is a crucial issue for many urban or even suburban areas. A potential but manageable cause of this effect is the inefficient control of traffic light timing cycle that is not proactively respond to the characteristics of the considering intersection and time-dependent condition of the traffic. In this paper, we propose a finite-interval model that finds effective green time for each of the four phases light timing in each cycle. The objective is to acquire satisficing solution, by means of bat algorithm (BA) to minimize spoilage time at an intersection. The effectiveness of model was validated using a simulation experiment on a crowded intersection in Bangkok that has imbalanced numbers of accommodated lanes and formless urbanism. The traffic condition is simulated by the microscopic traffic simulator called 'Simulation of Urban Mobility' (SUMO). The experimental result confirmed the effective improvement when compared with the traditional fix-time operational control.

关键词： bat algorithm traffic light timing model SUMO green light timing phase

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Optimization of Revenue Generated by Hydro Power Plant by bat algorithm(BA) 3

Optimization of Revenue Generated by Hydro Power Plant by Ba...

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Gupta, Shrey Sharma, Kapil Delhi Technol Univ Dept Comp Engn Delhi India

ISBN: (纸本)9789380544199

Electricity is prime driver of growth and has varied range of uses spanning transportation, production of goods, industries, domestic uses and many. In countries, like India, the demand for electricity has always been more than the supply and hence the country face power shortage. Electricity is generated by non-renewable resources like coal, oil, fossil fuels, nuclear power and natural gas and from renewable resources like wind energy, hydroelectric power plants, biomass, solar energy, reservoirs. Hydroelectric power plants are major source of electricity in india with presently installed capacity of 41,997.42 MW as on July 31, 2015 which is 15.22% of total electricity generation in India. Hydroelectricity means production of electrical power through the use of the gravitational force of falling or flowing water. It is significant to optimize the revenue generated by the hydroelectric power plant to meet the implementation cost and for expansion of hydroelectric power plants. A lot of research work has been done in optimization of hydroelectric flow. In this paper, a new calibrated revenue optimization model is developed with Multi-Objective bat algorithm (MOBA) for hydro power plants. This model can be used to increase the revenue generated by the hydroelectric flow from the dam wih respect to given constraints in less time. bat algorithm is the latest metaheuristic algorithm which is based on the behaviour of bats. bat algorithm has received increased attention in many research fields recently. bat algorithm based optimization model can predict the flow of water through turbine in hydroelectric power plant in order to maximize the revenue.

关键词： Application of bat-algorithm bat algorithm electricity production hydroelectric flow Optimization revenue maximization

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Four Adaptive Memetic bat algorithm Schemes for Bezier Curve Parameterization

Four Adaptive Memetic Bat Algorithm Schemes for Bezier Curve...

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15th International Conference on Cyberworlds

作者： Iglesias, Andres Galvez, Akemi Collantes, Marta Univ Cantabria Dept Appl Math & Computat Sci Avda Castros S-N E-39005 Santander Spain Toho Univ Fac Sci Dept Informat Sci Narashino Campus2-2-1 Miyama Funabashi Chiba 2748510 Japan

ISBN: (纸本)9783662530900;9783662530894

This paper is an extension of a previous one presented at the conference Cyberworlds 2015. In that work we addressed the problem to fit a given set of data points in the least-square sense by using a polynomial Bezier curve. This problem arises in many scientific and industrial domains, such as numerical analysis, statistical regression, computer-aided design and manufacturing, computer graphics, virtual reality, etc. A critical issue to address this least-squares minimization problem is that of curve parameterization. In our previous work we solve it by applying a powerful nature-inspired optimization method called the bat algorithm. Although we obtained pretty good results on a number of examples, the method can still be further improved by considering a memetic approach, in which the global search bat algorithm is hybridized with a local search procedure to enhance the exploitation phase of the minimization process. In this paper we extend our previous method through two local search strategies: Luus-Jaakola and ASSRS. In both cases, the adaptive and self-adaptive versions are considered, leading to four memetic schemes. A comparative analysis of our results on the previous benchmark for these four memetic schemes and our previous method has been carried out. It shows that the memetic approaches improve the efficiency of the previous method at different extent for all instances in our benchmark.

关键词： Memetic approach Local search procedure Data fitting bat algorithm Curve parameterization Self-adaptive approach

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