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An intelligent design optimization of a permanent magnet synchronous motor by artificial bee colony algorithm

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第3期24卷 1826-1837页

作者： Mutluer, Mumtaz Bilgin, Osman Selcuk Univ Fac Engn Dept Elect Elect Engn Konya Turkey

The artificial bee colony algorithm is one of the latest stochastic methods based on swarm intelligence. The algorithm simulates the foraging behavior of honeybees. The structure of the algorithm is quite simple and its coding is very easy. This paper proposes a design optimization based on geometrical variables to obtain a highly efficient surface mounted permanent magnet synchronous motor with concentrated winding by use of the artificial bee colony algorithm. Input parameters for the algorithm are the geometrical variables of the motor. This approach is more advantageous than finite element analysis requiring a long period of time. Results of the artificial bee colony algorithm are compared with results of a genetic algorithm and checked with a commercial design program. The results emphasize the effectiveness of the algorithm on the design optimization of the permanent magnet synchronous motor.

关键词： artificial bee colony algorithm design optimization genetic algorithm permanent magnet synchronous motor

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NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering

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NEUROCOMPUTING 2016年 216卷 514-533页

作者： Kishor, Avadh Singh, Pramod Kumar Prakash, Jay ABV IIITM Computat Intelligence & Data Min Res Lab Gwalior 474015 India

This paper presents a non-dominated sorting based multi-objective artificial bee colony algorithm NSABC to solve multi-objective optimization problems. It is an extension of the artificial bee colony algorithm ABC, which is a single objective optimization algorithm, to the multi-objective optimization domain. It uses a novel approach in the employee bee phase to steer the solutions to simultaneously achieve both the orthogonal goals in the multi-objective optimization - convergence and diversity. The onlooker bee phase is similar to the ABC except for the fitness computation to exploit the promising solutions whereas there is no change in the scout bee phase, which is used to get rid of bad solutions and add diversity in the swarm by introducing random solutions. Along with a novel way of exploring new solutions, it uses non-dominated sorting and crowding distance, inspired by the NSGA-II, to maintain the best and diverse solutions in the swarm. It is tested on the 10 two-objective and three-objective unconstrained benchmark problems of varying nature and complexities from the CECO9 suite of test problems and is found better than or commensurable to thirteen state-of-the-art significant multi-objective optimization algorithms as well as other multi-objective variants of the ABC. Further, it is tested on the nine real-life data clustering problems considered from the UCI machine learning repository and proved itself better in comparison to the NSGA-II, MOVGA, and a recent multi-objective variant of the ABC named MOABC. Thus, it is observed that the NSABC is comparatively a simple, light, and powerful algorithm to solve multi-objective problems. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization artificial bee colony algorithm Non-dominated sorting Crowding distance Augmented population

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A multi-objective placement of phasor measurement units using fuzzified artificial bee colony algorithm, considering system observability and voltage stability

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JOURNAL OF EXPERIMENTAL & THEORETICAL artificial INTELLIGENCE 2016年第1-2期28卷 113-136页

作者： Jeyaraj, K. Arul Rajasekaran, V. Kumar, S. K. Nandha Chandrasekaran, K. PSNA Coll Engn & Technol Dept Elect Engn Dindigul Tamil Nadu India Natl Inst Technol Puducherry Dept Elect Engn Karaikal India

This paper presents Multi-objective Optimal Placement of Phasor measurement units (MOPP) to improve the performance of power system monitoring and control. It is proposed for achieving complete observability with a minimal number of phasor measurement units (PMUs) and maximising the voltage stability level of the system, simultaneously. As the above mentioned objectives are conflicting in nature, a fuzzified binary artificial bee colony algorithm is considered to solve the MOPP problem to offer a good tradeoff solution between the competing objectives. Here, fuzzy membership for each objective function is designed and proposed to decide the best solution of MOPP problem. Initially, weak buses are identified using the fast voltage stability index calculation for their close and reliable monitoring. This is done in order to prevent the outage of these buses. The conventional rule is used to minimise the number of PMUs and a new rule is proposed to maximise both the observability and voltage stability levels of the system. The efficiency of the artificial bee colony method is validated on IEEE 14, 30 and 57 bus test systems and it is verified by comparing the performance of proposed method with earlier works.

关键词： optimal placement fuzzy set theory artificial bee colony algorithm voltage stability evaluation phasor measurement unit

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An improved artificial bee colony algorithm

An improved artificial bee colony algorithm

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The 2nd World Conference on Humanities and Social Sciences(WCHSS 2017)

作者： Yanfei Jia Xin Wang Xiaodong Yang College of Electrical and Information Beihua University College of Information and Communication Engineering Harbin Engineering University Grid information & telecommunication group Beijing China power information technology CO.LTD Collaborative Research Center Meisei University

To improve the performance of artificial bee colony algorithm, a modified artificial bee colony algorithm is proposed. The modified ABC algorithm takes advantage of chaotic mapping to enhance diversity in the bee population, uses information of a better solution to guide the search of candidate solutions toward a promising direction that improves exploitation, and introduces a global solution into the scout stage in order to jump out of the local optimal value. The modified ABC algorithm balances exploration and exploitation, which contradict each other, so that it can achieve optimal performance. Simulation results show the proposed method has more accuracy than the other methods.

关键词： artificial bee colony algorithm chaotic mapping local optimal value exploration

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Optimal sizing of grid integrated hybrid PV-biomass energy system using artificial bee colony algorithm

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IET RENEWABLE POWER GENERATION 2016年第5期10卷 642-650页

作者： Singh, Shakti Kaushik, Subhash Chandra Thapar Univ Dept Elect & Instrumentat Engn Patiala 147004 Punjab India Indian Inst Technol Ctr Energy Studies Delhi India

This study presents an optimal sizing methodology for a stand-alone and grid connected PV-biomass hybrid energy system that serves the electricity demand of a typical village. However, this method is scalable and can be used in any test system. A recently developed artificial bee colony (ABC) algorithm is used to detect out the optimum hybrid system configuration with the least levelised cost of energy while minimising annualised cost of the system. It has been observed from the results that a grid connected hybrid PV-biomass system is cost effective and reliable choice for rural electrification as compared with stand-alone hybrid PV-biomass energy system. It has been emerged from this study that the proposed system offers reliable and affordable electricity in a sustainable way by harnessing locally available natural resources. A brief comparison of results obtained from the ABC algorithm and hybrid optimisation model for electric renewable (HOMER) has been carried out. Moreover, it is also observed from the results that the ABC algorithm provides better results as compared with HOMER.

关键词： photovoltaic power systems hybrid power systems bioenergy conversion optimisation grid integrated hybrid PV-biomass energy system artificial bee colony algorithm electricity demand ABC algorithm hybrid optimisation model for electric renewable HOMER

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An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 52-67页

作者： Gao, Kai Zhou Suganthan, Ponnuthurai Nagaratnam Pan, Quan Ke Chua, Tay Jin Chong, Chin Soon Cai, Tian Xiang Liaocheng Univ Sch Comp Liaochheng 252059 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Singapore Inst Mfg Technol Nanyang Dr Singapore 638075 Singapore

This study addresses flexible job-shop scheduling problem (FJSP) with fuzzy processing time. An improved artificial bee colony (IABC) algorithm is proposed for FJSP cases defined in existing literature and realistic instances in remanufacturing where the uncertainty of the processing time is modeled as fuzzy processing time. The objectives are to minimize the maximum fuzzy completion time and the maximum fuzzy machine workload, respectively. The goal is to make the scheduling algorithm as part of expert and intelligent scheduling system for remanufacturing decision support. A simple and effective heuristic rule is developed to initialize population. Extensive computational experiments are carried out using five benchmark cases and eight realistic instances in remanufacturing. The proposed heuristic rule is evaluated using five benchmark cases for minimizing the maximum fuzzy completion time and the maximum fuzzy machine workload objectives, respectively. IABC algorithm is compared to six meta-heuristics for maximum fuzzy completion time criterion. For maximum fuzzy machine workload, IABC algorithm is compared to six heuristics. The results and comparisons show that IABC algorithm can solve FJSP with fuzzy processing time effectively, both benchmark cases and real-life remanufacturing instances. For practical remanufacturing problem, the schedules by IABC algorithm can satisfy the requirement in real-life shop floor. The IABC algorithm can be as part of expert and intelligent scheduling system to supply decision support for remanufacturing scheduling and management. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Flexible job-shop scheduling Fuzzy processing time Heuristic

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Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

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APPLIED MATHEMATICAL MODELLING 2016年第21-22期40卷 9302-9315页

作者： Yin, Peng-Yeng Chuang, Ya-Lan Natl Chi Nan Univ Dept Informat Management 470 Univ Rd Nantou 545 Taiwan

Cross-docking distribution is becoming increasingly prevalent in supply chain management (SCM) due to the replacement of storage and order-picking with efficient product consolidation. Several successful cross-docking applications have been put into practice by companies such as Wal-Mart, FedEx, and Home Depot. Cross-docking consists of interrelated operations which require proper synchronization. Only a few early attempts have addressed the vehicle routing problem in this context. Moreover, the social consciousness of sustainable development has brought up the issue of green SCM which is not only environment-friendly but also beneficial to business values. This paper first addresses a previously studied problem and then proposes a new problem version for planning the least cost green vehicle routing in which the deployed vehicles transport the final products from suppliers to customers through a cross-dock subject to a CO2 intensity constraint. We develop an adaptive memory artificial bee colony (AMABC) algorithm to tackle both problems. Compared to a Tabu search proposed in the literature, the AMABC algorithm can reach higher fuel efficiency by managing the loading along the route and yield less cost and CO2 intensities. Statistical tests of simulation and geographic data show that the AM ABC method is robust against the problem size and convergence of the objective value is guaranteed with high confidence. (C) 2016 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Adaptive memory programing Cross-docking Green supply chain management Vehicle routing

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Inverse analysis of geomechanical parameters by the artificial bee colony algorithm and multi-output support vector machine

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2016年第7期24卷 1266-1281页

作者： Zhao, Hongbo Yin, Shunde Henan Polytech Univ Sch Civil Engn Jiaozuo Peoples R China Univ Wyoming Dept Chem & Petr Engn Laramie WY 82071 USA

Geomechanical parameters play a very important role in geotechnical engineering design and construction. Inverse analysis provides a powerful tool to characterize geomechanical parameters based on the behaviour of rock under certain boundary conditions. In this paper, a new method for inverse analysis that takes advantage of multi-output support vector machine (MSVM) and artificial bee colony (ABC) is proposed and applied to solving a real field problem. MSVM is used to map the relationship between geomechanical parameters and displacements. ABC is adopted in inverse analysis to find the optimal geomechanical parameters. The proposed method is used to recognize the parameters of the permanent shiplock slope of the Three Gorges in China. Results show that the proposed method can effectively determine geomechanical parameters.

关键词： Inverse analysis parameters identification geotechnical engineering multi-output support vector machine artificial bee colony algorithm

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A discrete artificial bee colony algorithm for single machine scheduling problems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第22期54卷 6860-6878页

作者： Yurtkuran, Alkin Emel, Erdal Uludag Univ Dept Ind Engn Bursa Turkey

This paper presents a discrete artificial bee colony algorithm for a single machine earliness-tardiness scheduling problem. The objective of single machine earliness-tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness-tardiness penalties. artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.

关键词： scheduling single machine artificial bee colony algorithm meta-heuristics combinatorial optimisation

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A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation

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INFORMATION SCIENCES 2016年 367卷 1012-1044页

作者： Cui, Laizhong Li, Genghui Lin, Qiuzhen Du, Zhihua Gao, Weifeng Chen, Jianyong Lu, Nan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Xidian Univ Sch Math & Stat Xian Peoples R China

Inspired by the intelligent foraging behavior of honey bees, the artificial bee colony algorithm (ABC), a swarm-based stochastic optimization method, has shown to be very effective and efficient for solving optimization problems. However, since its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. To better balance the tradeoff between exploration and exploitation, in this paper, we propose a depth-first search (DFS) framework. The key feature of the DFS framework is to allocate more computing resources to the food sources with better quality and easier to be improved for evolution. We apply the DFS framework to ABC, GABC and CABC, yielding DFSABC, DFSGABC and DFSCABC respectively. The experimental results on 22 benchmark functions show that the DFS framework can speed up convergence rate in most cases. To further improve the performance, we introduce two novel solution search equations: the first equation incorporates the information of elite solutions and can be applied to the employed bee phase, while the second equation not only exploits the information of the elite solutions but also employs the current best solution in the onlooker bee phase. Finally, two novel proposed search equations are combined with DFSABC to form a new variant of ABC, named DFSABC_elite. Through the comparison of DFSABC_elite with other variants of ABC and some non-ABC methods, the experimental results demonstrate that DFSABC_elite is significantly better than the compared algorithms on most of the test functions in terms of solution quality, robustness, and convergence speed. (C) 2016 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Depth-first search framework Computing resources allocation Novel search equations

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