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An Enhanced Discrete Artificial Bee Colony algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-11页

作者： Deng, Guanlong Yang, Hongyong Zhang, Shuning Ludong Univ Sch Informat & Elect Engn Yantai 264025 Peoples R China

This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms.

关键词： bees algorithm FLOW shop scheduling BUFFER solutions COMPUTER mathematics COMBINATIONS (Mathematics)

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Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing

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JOURNAL OF MANUFACTURING SYSTEMS 2016年 41卷 86-101页

作者： Xu, Wenjun Shao, Luyang Yao, Bitao Zhou, Zude Duc Truong Pham Wuhan Univ Technol Sch Informat Engn Luoshi Rd 122 Wuhan 430070 Peoples R China Minist Educ Key Lab Fiber Opt Sensing Technol & Informat Proc Luoshi Rd 122 Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Luoshi Rd 122 Wuhan 430070 Peoples R China Univ Birmingham Sch Engn Dept Mech Engn Birmingham B15 2TT W Midlands England

Both sustainable manufacturing and manufacturing service are the trends in industry because they are regarded as ways to reduce the resource cost and energy consumption in manufacturing process, to improve the flexibility and responding speed to customers' demand, and to improve the production efficiency. In order to improve the sustainability of manufacturing equipment services in job shop, this paper presents a multi-objective joint model of energy consumption and production efficiency. The model is related to multi-conditions of manufacturing equipment services. The conditions are monitored in real-time to drive a multi-objective dynamic optimized scheduling of manufacturing services. In order to solve the multi-objective problem, an enhanced Pareto-based bees algorithm (EPBA) is proposed. In order to ensure the variety of population, to prevent the premature convergence, and to improve the searching speed, several key technologies are utilized such as variable neighborhood searching, mutation and crossover operation, fast non-dominated ranking, critical path local search, archive Pareto set, critical path taboo set, etc. Finally, the proposed method is evaluated and shows better performance in static and dynamic scenarios compared with the existing optimization algorithms. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Sustainable manufacturing Manufacturing equipment service Perception data-driven bees algorithm Optimized scheduling

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Bee algorithms for parallel two-sided assembly line balancing problem with walking times

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APPLIED SOFT COMPUTING 2016年 39卷 275-291页

作者： Tapkan, Pinar Ozbakir, Lale Baykasoglu, Adil Erciyes Univ Dept Ind Engn Kayseri Turkey Dokuz Eylul Univ Dept Ind Engn Izmir Turkey

Production lines, designed in a serial manner, have attracted the attention of researchers for many years. Line efficiency, throughput time and workload balancing are the main concerns regarding assembly lines, due to the high volume production. Recently, specific assembly line configurations, such as two-sided and parallel lines, have been addressed by researchers within the operational levels of production. Parallel two-sided assembly lines, rather a new research area, allocate more flexible workers and reduce throughput time by incorporating the advantages of two-sided and parallel assembly lines. Since parallel two-sided assembly lines are utilized to produce large-scale products, such as automobiles, trucks, and buses in industry;they also require a significant worker movement between parallel lines because of the unavoidable walking distances between lines. In this respect, different from the existing literature, walking distances have been included in parallel two-sided assembly line balancing problem. The main purpose of this paper is to introduce parallel two-sided assembly line balancing problem with walking times and to propose the implementation of bees algorithm and Artificial Bee Colony algorithm due to the NP-hardness of the problem. An extensive computational study is also carried out and the comparative results are presented. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Parallel two-sided assembly line balancing problem Walking times bees algorithm Artificial bee colony algorithm Mathematical programming model

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An Efficient Approach for Energy Consumption Optimization and Management in Residential Building Using Artificial Bee Colony and Fuzzy Logic

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-13页

作者： Wahid, Fazli Kim, Do Hyeun Jeju Natl Univ Dept Comp Engn Jeju 690756 South Korea

The energy management in residential buildings according to occupant's requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user's comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithmis to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.

关键词： DWELLINGS ENERGY consumption FUZZY logic bees algorithm ENERGY management POWER resources

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A New Approach Based on Honeybee to Improve Intrusion Detection System Using Neural Network and bees algorithm

A New Approach Based on Honeybee to Improve Intrusion Detect...

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2nd International Conference on Software Engineering and Computing Systems (ICSECS 2011)

作者： Ali, Ghassan Ahmed Jantan, Aman Univ Sains Malaysia George Town Malaysia

ISBN: (纸本)9783642222023

A new approach inspired by bees defensive behaviour in nature is proposed to improve Intrusion Detection System (IDS). In honeybee colonies, guards discriminate nestmates from non-nestmates at a hive entrance using an approach contains Undesirable-Absent (UA) or Desirable-Present (DP), and Filtering Decision (FD) methods. These methods are used to detect intruder and classify its type. In the proposed approach, the UA detector is responsible for detecting pre-defined attacks based on their attack signatures. Neural network trained by bees algorithm (BA) was used to learn the patterns of attacks given in training dataset and use these patterns to find specific attacks in test dataset. The DP detector is responsible for detecting anomalous behaviours based on the trained normal behaviour model. Finally, FD method is used to train the UA detector in real-time to detect new intrusions. The performance of the proposed IDS is evaluated by using KDD'99 dataset, the benchmark dataset used by IDS researchers. The experiments show that the proposed approach is applied successfully and able to detect many different types of intrusions, while maintaining a low false positive rate.

关键词： Intrusion detection system honeybee approach neural networks bees algorithm

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Heuristic Optimization Methods Applied to Improve the Performance of Controlled DC-DC High Boost Converter with Coupled Inductors 19

Heuristic Optimization Methods Applied to Improve the Perfor...

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19th International Conference on Electrical Machines and Systems (ICEMS)

作者： Marsala, Giuseppe Ragusa, Antonella UOS Palermo CNR ISSIA Palermo Italy

ISBN: (纸本)9784886860989

This paper presents the optimization of the performances of a controlled zero ripple input current high boost dc-dc converter with coupled inductors. In particular, an optimization of PID and LQR controllers design is presented, using three heuristic methods: Genetic, Particle Swarm and bees algorithms. A comparison between the PID and LQR controllers, with and without optimization, is presented together with the analysis of effectiveness of the three heuristic algorithms to design the control strategies.

关键词： DC-DC converter coupled inductors control strategies genetic algorithm particle swarm optimization bees algorithm

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Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications

Exploring the High Performance Computing-Enablement of a Sui...

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10th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

作者： Li, Zhenya Sinnott, Richard O. Choon, Yee Wen Sjaugi, Muhammad Farhan Mohammad, Mohd Saberi Deris, Safaai Napis, Suhaimi Omatu, Sigeru Manuel Corchado, Juan Ibrahim, Zuwairie Yusof, Zulkifli Md Univ Melbourne Dept Comp & Informat Syst Melbourne Vic Australia Univ Teknol Malaysia Fac Comp Johor Baharu Malaysia Perdana Univ Sch Data Sci Serdang Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Kota Baharu Malaysia Univ Putra Malaysia Fac Biotechnol & Biomol Sci Serdang Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka Japan Univ Salamanca BISITE Res Grp Biomed Res Inst Salamanca Salamanca Spain Univ Malaysia Pahang Fac Mfg Engn Fac Elect & Elect Gambang Malaysia

ISBN: (纸本)9783319401263;9783319401256

Genetic engineering provides methods to modify the genes of microorganisms to achieve desired effects. This can be done for improved organism growth rate or increasing production yield of a desired gene product. Gene knockout is a technique that can improve the specific characteristics of microorganisms by disabling selected sets of genes. However, microorganisms are complex and predicting the effects of gene modification is difficult. Several algorithms have been proposed to support a range of gene knockout strategies, including BAFBA, BHFBA and DBFBA. In this paper, scaling these algorithms and methods to utilise High Performance Computing (HPC) resources have been explored. The applications have been parallelized on HPC and the scalability and performance of these approaches were explored and documented.

关键词： High performance computing HPC Gene knockout Genetic engineering bees algorithm Flux balance

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Artificial Bee Colony Optimization for Multi Objective Economic Load Dispatch of a Modern Power system

Artificial Bee Colony Optimization for Multi Objective Econo...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Rao, R. Kameswara Srinivas, P. Divakar, M. S. M. Venkatesh, G. S. N. M. Raghu Engn Coll Dept EEE Visakhapatnam Andhra Pradesh India Raghu Inst technol Dept EEE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781467399395

This paper presents using the bees algorithm approach to multi-objective optimal economic dispatch of electrical power systems. The generation of electricity from the fossil fuel releases several contaminants, such as sulfur oxides, nitrogen oxides and carbon dioxide, into the atmosphere. Genetic algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. In the Multi-objective problem formulation, four important non-commensurable objectives in an electrical thermal power system are considered. These are economy and environmental impacts because of NOx, SO2, CO2 emissions. The suitability of the proposed approach is described to multiobjective optimal economic dispatch of electrical power systems.

关键词： optimization genetic algorithms economic dispatch bees algorithm

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A New Modified Artificial Bee Colony algorithm with Exponential Function Adaptive Steps

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016年第1期2016卷 1-13页

作者： Mao, Wei Lan, Heng-you Li, Hao-ru Sichuan Univ Sci & Engn Dept Math Zigong 643000 Sichuan Peoples R China Key Lab Higher Educ Sichuan Prov Enterprise Infor Zigong 643000 Sichuan Peoples R China Sichuan Univ Sci & Engn Sch Automat & Elect Informat Zigong 643000 Sichuan Peoples R China

As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the newmodified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions.

关键词： bees algorithm BEE colonies SWARM intelligence BIODIVERSITY algorithm research

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Robust Parallel Machine Scheduling Problem with Uncertainties and Sequence-Dependent Setup Time

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SCIENTIFIC PROGRAMMING 2016年第1期2016卷 1-13页

作者： Hu, Hongtao Ng, K. K. H. Qin, Yichen Shanghai Maritime Univ Logist Engn Coll Shanghai Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

A parallel machine scheduling problem in plastic production is studied in this paper. In this problem, the processing time and arrival time are uncertain but lie in their respective intervals. In addition, each job must be processed together with a mold while jobs which belong to one family can share the same mold. Therefore, time changing mold is required for two consecutive jobs that belong to different families, which is known as sequence-dependent setup time. This paper aims to identify a robust schedule by min-max regret criterion. It is proved that the scenario incurring maximal regret for each feasible solution lies in finite extreme scenarios. A mixed integer linear programming formulation and an exact algorithm are proposed to solve the problem. Moreover, a modified artificial bee colony algorithm is developed to solve large-scale problems. The performance of the presented algorithm is evaluated through extensive computational experiments and the results show that the proposed algorithm surpasses the exact method in terms of objective value and computational time.

关键词： PRODUCTION scheduling PLASTICS industries LINEAR programming bees algorithm PROBLEM solving

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