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3rd IEEE International Conference on Electro-Technology for National Development (NIGERCON)

作者： Haruna, Zaharuddeen Mu'azu, Muhammad B. Abubilal, Kabir A. Tijani, Salawudeen A. Ahmau Bello Univ Dept Elect & Comp Engn Zaria Nigeria

ISBN: (纸本)9781509064229

This research work presents the development of a modified bat algorithm (mBA) using elite opposition - based learning. The bat algorithm (BA), which is a nature inspired meta-heuristic algorithm, works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The mBA is developed by modifying the BA with elite opposition - based learning (EOBL) in order to diversify the solution search space and the inertial weight in order to improve its exploitation capability. The performance of the proposed mBA was compared with that of the standard BA using seven benchmark optimization test functions. The simulation results showed that the mBA is superior to the standard BA by obtaining global optimal result of most of the test functions. All simulations were carried out using MATLAB R2013b.

关键词： bat algorithm elite opposition - based learning inertia weight optimization test functions global optimal

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A bat algorithm for Job Scheduling in Ceramics Production Lines

A Bat Algorithm for Job Scheduling in Ceramics Production Li...

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International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)

作者： Tupia, Manuel Cueva, Rony Guanira, Miguel Pontificia Univ Catolica Peru Dept Engn Ave Univ 1801 San Miguel Lima Peru

ISBN: (纸本)9781538605141

The wide array of products manufactured in ceramics production lines and their manufacturing limitations fall into the job scheduling environment. Task scheduling involved in this problem 's variant has already been solved by several authors approaches [1]. That said, this paper intends to propose the use of a relatively new metaheuristics called bat algorithm [3] ( BA) to solve such scenarios. Our study includes a numerical experimentation process which confronts the proposed BA algorithm against a Genetic algorithm [2] and a GRASP algorithm [1].

关键词： bat algorithm Job scheduling Production Scheduling Meta heuristics Bio-inspired algorithm

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A Multiobjective Chaotic bat algorithm for Economic and Emission Dispatch

A Multiobjective Chaotic Bat Algorithm for Economic and Emis...

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Chinese Automation Congress (CAC)

作者： Liang, Huijun Liu, Yungang Shen, Yanjun Li, Fengzhong Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

ISBN: (纸本)9781538635247

Environment problem is becoming a greatly concerned focus with more and more fossil fuels consumed now. How to balance economic dispatch and pollution gas emission in power systems is really a multiobjective optimization problem, which has been studied by many researchers. In this paper, we propose a novel multiobjective optimization algorithm to solve this problem by integrating bat algorithm and chaotic map together. The mathematical model of this optimization problem is analyzed and some related constraints are also given. Because the cost functions used in this paper are both convex functions, classical weighted sum method is blended with the proposed algorithm. Some works only get a single optimal solution for the multiobjective optimization problem with some rules, but it is usually not enough in practice. In order to describe the two conflicting optimization objectives, i.e., minimizing the fuel cost and NOx emission simultaneously, the Pareto optimal front is used. A price penalty factor is applied in the mathematical model to overcome the drawback by the usage of weighted sum method. Simulation results indicate the good tradeoff characteristic of the two optimization objectives through the generated Pareto optimal front.

关键词： Multiobjective optimization bat algorithm chaotic map economic and emission dispath

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Study of Parameter Sensitivity on bat algorithm 17th

Study of Parameter Sensitivity on Bat Algorithm

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17th International Conference on Computational Science and its Applications (ICCSA)

作者： Carvalho, Iago Augusto da Rocha, Daniel G. Rocha Silva, Joao Gabriel Vieira, Vinicus da Fonseca Xavier, Carolina Ribeiro Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil Univ Fed Sao Joao Rei Dept Comp Sci Sao Joao Del Rei Brazil Univ Fed Juiz Fora Postgrad Program Computat Modeling Juiz De Fora Brazil

ISBN: (纸本)9783319623924;9783319623917

Heuristics and metaheuristics are known to be sensitive to input parameters. bat algorithm (BA), a recent optimization metaheuristic, has a great number of input parameters that need to be adjusted in order to increase the quality of the results. Despites the crescent number of works with BA in literature, to the best of our knowledge, there is no work that aims the fine tuning of the parameters. In this work we use benchmark functions and more than 9 millions tests with BA in order to find the best set of parameters. Our experiments shown that we can have almost 14000% of difference in objective function value between the best and the worst set of parameters. Finally, this work shows how to choose input parameters in order to make bat algorithm to achieve better results.

关键词： bat algorithm Sensitivity Parameter analysis Nonlinear optimization Unconstrained optimization

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Shuffled Multi-Population bat algorithm (SMPbat)

Shuffled Multi-Population Bat Algorithm (SMPBat)

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

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

ISBN: (纸本)9781509063673

bat algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimisation problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different hybrids of BA. This paper proposes Shuffled Multi Population bat algorithm (SMPbat), a hybrid between two recently proposed variants of BA:- Enhanced Shuffled bat algorithm (EShbat) and bat algorithm with Ring Master-Slave strategy (batRM-S). batRM-S is a multi-population variant of BA which partitions it's population according to a combination of the ring and master-slave strategies. EShbat incorporates shuffling into BA. The proposed algorithm, SMPbat combines the population partitioning strategies of these two algorithms to enhance the exploitation and exploration capabilities of BA. The evolution strategy of SMPbat also strives to retain the improved solutions. The standard BA replaces a solution with a new solution around the best. However, in this process, the information gained by that solution so far is completely lost. SMPbat changes this exploitation technique used by BA. SMPbat is compared to batRM-S, EShbat and BA, over 30 well-known optimisation functions. Results establish SMPbat as a significant improvement over BA, EShbat and batRM-S.

关键词： bat algorithm Master-Slave strategy Multi Population algorithms Swarm algorithms Numerical optimisation

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Implementation of bat algorithm in Economic Dispatch for Units with Multiple Fuels and Valve-Point Effect

Implementation of Bat Algorithm in Economic Dispatch for Uni...

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IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE)

作者： Bhattacharya, Sayantan Kuanr, Biswa Ranjan Routray, Aliva Dash, Abhishek Jadavpur Univ Power Engn Dept Kolkata W Bengal India VSSUT Elect Engn Dept Burla Odisha India Natl Inst Technol Elect Engn Dept Kozhikode Kerala India

ISBN: (纸本)9781509049967

In this paper a solution technique based on bat algorithm (BA) is implemented for solving the economic load dispatch problem in a power system in which every unit utilize multiple fuels for producing power. The economic load dispatch (ELD) problem is modeled as a complex mathematical function that takes cost coefficients of all the possible fuel options as well as effects of valve-point loading into consideration. Various operating constraints of the system include equality constraint on power balance and inequality constraint on generation capacity. The proposed methodology is successfully applied on a test system consisting of ten thermal units. A qualitative comparison of the obtained result with other standard population based meta-heuristic techniques manifests proposed technique's superiority both in terms of result quality and computation time.

关键词： Economic Load dispatch bat algorithm Multiple Fuel Unit Valve-point effects

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Optimization of Association Rule Mining Using Hybridized Artificial Bee Colony (ABC) with bat algorithm 7

Optimization of Association Rule Mining Using Hybridized Art...

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7th IEEE International Advance Computing Conference (IACC)

作者： Neelima, S. Satyanarayana, N. Murthy, P. Krishna JNTUH Dept CSE Hyderabad Andhra Pradesh India Nagole Inst Technol & Sci Dept CSE Hyderabad Andhra Pradesh India Swarna Bharathi Inst Sci & Technol Khammam India

ISBN: (纸本)9781509015603

One of the major tasks of data mining is association rule mining, which is used for finding the interesting relationships among the items in itemsets of huge database. Aproiri is the familiar algorithm of association rule mining for generating frequent itemsets. Apriori uses minimum support threshold to find frequent items. In this paper, an algorithm called hybridization of ABC with bat algorithm is proposed which is used for optimization of association rules. Instead of onlooker bee phase of ABC, random walk of bat is used in order to increase the exploration. Hybridized ABC with bat algorithm is applied on the rules generated from apriori algorithm, for optimizing association rules. The experiments are performed on datasets taken from UCI repository which show the proposed work performance and proposed methodology can effectively optimize association rules when compared to the existing algorithms. In the paper, we also proved that the rules generated using proposed work are simple and comprehensible.

关键词： Apriori algorithm Artificial Bee Colony algorithm Association Rule Mining bat algorithm

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Multi-directional bat algorithm for solving unconstrained optimization problems

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OPSEARCH 2017年第4期54卷 684-705页

作者： Tawhid, Mohamed A. Ali, Ahmed F. Thompson Rivers Univ Dept Math & Stat Fac Sci Kamloops BC V2C 0C8 Canada Alexandria Univ Dept Math & Comp Sci Fac Sci Alexandria 21511 Egypt Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismailia Egypt Thompson Rivers Univ Dept Math & Stat Fac Sci Kamloops BC V2C 0C8 Canada

In this paper, we propose a new hybrid algorithm for solving unconstrained global optimization problems by hybridizing the bat algorithm with multi-directional search algorithm (MDS). We call the proposed algorithm by multi-directional bat algorithm (MDbat). In MDbat algorithm, we try to overcome the slow convergence of the bat algorithm as a metaheuristic algorithm by invoking one of the promising direct search algorithm which is called MDS algorithm. The bat algorithm has a good ability to make exploration and exploitation search while the MDS has a good ability for accelerating convergence on the region of optimal response. In the beginning, the standard bat algorithm starts the search for number of iterations then the MDS algorithm starts its search from bat algorithm found so far. The combination between the standard bat algorithm and the MDS algorithm helps the MDS algorithm to start the search from a good solution instead of the random initial solution. The MDS algorithm can accelerate the search of the proposed algorithm instead of letting the algorithm running for more iterations without any improvement. We investigate the general performance of the MDbat algorithm by applying it on 16 unconstrained global optimization problems and comparing it against 8 benchmark algorithms. The experimental results indicate that MDbat is a promising algorithm and outperforms the other algorithms in most cases.

关键词： bat algorithm Multi-directional search algorithm Global optimization Unconstrained optimization problems

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Modified bat algorithm With Cauchy Mutation and Elite Opposition-Based Learning

Modified Bat Algorithm With Cauchy Mutation and Elite Opposi...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Paiva, Fabio A. P. Silva, Claudio R. M. Leite, Izabele V. O. Marcone, Marcos H. F. Costa, Jose A. F. IFRN Acad Direct Parnamirim Brazil Univ Fed Rio Grande do Norte Dept Commun Engn Natal RN Brazil Univ Fed Rio Grande do Norte Dept Chem Engn Natal RN Brazil Univ Fed Rio Grande do Norte Sci & Technol Natal RN Brazil Univ Fed Rio Grande do Norte Dept Elect Engn Natal RN Brazil

ISBN: (纸本)9781538637340

Metaheuristics can be used to solve optimization complex problems because they offer approximate and acceptable solutions. In recent years, nature has been a source of inspiration for many computer scientists when proposing new metaheuristics such as the algorithms inspired by swarm intelligence. They are based on the behavior of animals that live in groups such as birds, fishes and bats. In this context, bat algorithm (BA) is a recent metaheuristic inspired by echolocation of bats during their flights. However, a problem that this algorithm faces is the loss of the ability to generate diversity and, consequently, the chances of finding the global solution are reduced. This paper proposes a modification to the original BA using two methods known as Cauchy mutation operator and Elite Opposition-Based Learning. The new variant aims generate diversity of the algorithm and increases its convergence velocity. It was compared to the original BA and another variant found in the literature. For this comparison, the proposed variant used four benchmark functions, during 30 independent runs. After the experiments, the superiority of the new variant is highlighted when the results are compared to the original BA.

关键词： Metaheuristic Swarm Intelligence bat algorithm Elite Opposition-Based Learning Cauchy Mutation

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Sparse Reconstruction with bat algorithm and Orthogonal Matching Pursuit 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Chunmei Cai, Xingjuan Shi, Zhentao Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China

ISBN: (数字)9783319633091

ISBN: (纸本)9783319633091;9783319633084

Sparse reconstruction problem is a typical research topic in compressed sensing theory and applications, and many algorithms are designed to solve it. As one of the most well-known reconstruction algorithm, orthogonal matching pursuit (OMP) is widely used in many applications. However, due to the limited global search capability, it is often fallen into the local optimal. In this paper, bat algorithm, a new population-based swarm intelligent algorithm, is incorporated into the methodology of OMP to increase the global search capability. To test the performance, two different signals are employed to compare, and simulation results show our algorithm increases the performance significantly.

关键词： Sparse reconstruction bat algorithm Orthogonal matching pursuit

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