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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： ZHAO Ming SONG Xiao-Yu Gao Yi-Chen School of Information & Control Engineering Shenyang Jianzhu University Information Technology Management Office Shenyang Gas Group Co.Ltd

When large scale disaster occurs suddenly, allocating scheme of the first batch of "life" emergency supplies directly determines rescue effect. To make lack loss evaluation of the supplies accordant with actual cases, this paper proposed nonlinear loss evaluation function, based on which constructed multi to multi constrained scheduling model with multi objectives. To improve the performance of artificial bee colony algorithm, this paper proposed following schemes: food sources initialization based on converse food source. reverse learning and comprehensive learning added into bee searching procedure. Experiments results on random data of three scales show that non dominated front solutions set solved by the improved algorithm is more diverse, wide spread and uniform.

关键词： nonlinear loss emergency scheduling model constrained multi-objective optimization artificial bee colony algorithm reverse learning comprehensive learning

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AN IMPROVED artificial bee colony algorithm WITH LOCAL SEARCH FOR TRAVELING SALESMAN PROBLEM

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CYBERNETICS AND SYSTEMS 2014年第8期45卷 635-649页

作者： Kocer, Hasan Erdinc Akca, Melike Ruhan Selcuk Univ Tech Educ Fac TR-42003 Konya Turkey Nazilli Vocat High Sch Aydin Turkey

This study aims to solve the traveling salesman problem for small, medium, and large traveling salesman problems taken from the TSPLIB with known solutions, by using an improved artificial bee colony algorithm that is a swarm intelligence-based heuristic algorithm. The improvement process is achieved by using a loyalty function that is used in bee colony optimization as a fitness function used in ABC algorithms. Obtained solutions are compared to solutions from the TSPLIB and the results of cunkas and ozsaglam's (2009) study, which includes the solutions for benchmark problems and cities and counties in Turkey according to the genetic algorithm and particle swarm optimization.

关键词： traveling salesman problem opt-2 local search artificial bee colony algorithm opt-2 local search

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A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2014年第2期57卷 493-516页

作者： Xiang, Yi Peng, Yuming Zhong, Yubin Chen, Zhenyu Lu, Xuwen Zhong, Xuejun Guangdong Baiyun Univ Dept Basic Courses Guangzhou 510450 Guangdong Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Guangdong Peoples R China

The artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behavior of honey bee colonies. In this work, a particle swarm inspired multi-elitist ABC algorithm named PS-MEABC is proposed and applied for real-parameter optimization. In this modified version, the global best solution and an elitist randomly selected from the elitist archive are used to modify parameters of each food source in either onlooker bees or employed bees phases. PS-MEABC is compared with 5 state-of-the-art swarm based algorithms on CEC05 and BBOB12 benchmark functions in terms of four metrics: the mean error, the best error, the success rate (SR) and the expected running time (ERT). Wilcoxon signed ranks test results on the mean and the best error show that the performance of PS-MEABC is significantly better than or at least similar to these algorithms, and PS-MEABC has wider application range in terms of the success rate and faster convergence speed in terms of the expected running time. Our algorithm is comparable to its competitors with a fewer control parameters to be tuned.

关键词： artificial bee colony algorithm Multi-elitist preservation strategy PSO algorithm Wilcoxon signed ranks test

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Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2014年 31卷 85-99页

作者： Zhang, Shuzhu Lee, C. K. M. Choy, K. L. Ho, William Ip, W. H. Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Univ Melbourne Dept Management & Mkt Melbourne Vic Australia

The vehicle routing problem (VRP) is a critical and vital problem in logistics for the design of an effective and efficient transportation network, within which the capacitated vehicle routing problem (CVRP) has been widely studied for several decades due to the practical relevance of logistics operation. However, CVRP with the objectives of minimizing the overall traveling distance or the traveling time cannot meet the latest requirements of green logistics, which concern more about the influence on the environment. This paper studies CVRP from an environmental perspective and introduces a new model called environmental vehicle routing problem (EVRP) with the aim of reducing the adverse effect on the environment caused by the routing of vehicles. In this research, the environmental influence is measured through the amount of the emission carbon dioxide, which is a widely acknowledged criteria and accounts for the major influence on environment. A hybrid artificial bee colony algorithm (ABC) is designed to solve the EVRP model, and the performance of the hybrid algorithm is evaluated through comparing with well-known CVRP instances. The computational results from numerical experiments suggest that the hybrid ABC algorithm outperforms the original ABC algorithm by 5% on average. The transformation from CVRP to EVRP can be recognized through the differentiation of their corresponding optimal solutions, which provides practical insights for operation management in green logistics. (C) 2014 Published by Elsevier Ltd.

关键词： Green logistics Capacitated vehicle routing problem artificial bee colony algorithm Environmental influence Environmental vehicle routing problem CO2 emission

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Training of artificial neural networks with the multi-population based artifical bee colony algorithm

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2022年第1-2期33卷 124-142页

作者： Kirankaya, Cihat Aykut, Latife Gorkemli Erciyes Univ Grad Sch Nat & Appl Sci Ind Engn Kayseri Turkey Erciyes Univ Dept Ind Engn Ind Engn Fac Engn TR-38039 Kayseri Turkey

Nowadays, artificial intelligence has gained recognition in every aspect of life. artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers' acquisition of the learning and interpretation capabilities of humans and attainment of meaningful results. Whether artificial intelligence networks can yield meaningful results is directly related to how the network is trained. The traditional algorithms, which are used to train artificial intelligence networks, do not always yield successful results in complicated problems and real-life problems. Metaheuristic algorithms are efficient techniques developed in order to figure out time-consuming and challenging problems fast and as optimally as possible. This study makes use of the artificial bee colony algorithm, which has been widely used recently in solving many kinds of problems so as to train artificial neural networks efficiently. Within this study, different strategies are used on subpopulations, so that the algorithm can search without getting tangled with the local optima. And also same and different parameter settings are considered for each population to reflect different search behaviours. The proposed approach was analysed through applied results of different data sets. The results yielded that the used strategies can be promising alternatives to the current search algorithms.

关键词： artificial neural networks metaheuristic algorithms artificial bee colony algorithm multi-population-based strategy

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artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

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APPLIED SOFT COMPUTING 2015年 32卷 38-48页

作者： Benyoucef, Abou Soufyane Chouder, Aissa Kara, Kamel Silyestre, Santiago Sahed, Oussama Ait Blida 1 Univ Dept Elect SET Lab Blida Algeria Univ Msila Fac Technol Dept Elect Engn Ichbilia Algeria Univ Politecn Cataluna Dept Elect Engn ES-08034 Barcelona Spain

artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink (TM) and Cadence/Pspice (TM), is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Maximum power point tracking Photovoltaic systems Partially shaded conditions artificial bee colony algorithm Co-simulation Simulink/Pspice DSP implementation

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An artificial bee colony algorithm approach for the team orienteering problem with time windows

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 270-290页

作者： Cura, Tunchan Istanbul Univ Fac Business Adm Istanbul Turkey

This study addresses a highly constrained NP-hard problem called the team orienteering problem with time windows (TOPTW), which belongs to a well-known class of vehicle routing problems. This study proposes a relatively new technique called artificial bee colony (ABC) approach to solve the TOPTW. Moreover, considering that the number of studies for discrete optimization with an ABC algorithm is comparatively low, this study presents a new use of the ABC algorithm for a difficult discrete optimization problem. Additionally, this study introduces a new food source acceptance criterion and a new scout bee search behavior, both of which significantly contribute to the solution quality. The results show that the proposed method is effective, efficient, and comparable to other approaches. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Heuristics Routing Team orienteering problem Time window artificial bee colony algorithm

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Cooperative artificial bee colony algorithm for multi-objective RFID network planning

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2014年 42卷 143-162页

作者： Ma, Lianbo Hu, Kunyuan Zhu, Yunlong Chen, Hanning Chinese Acad Sci Shenyang Inst Automat Lab Informat Serv & Intelligent Control Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China

Radio frequency identification (RFID) is rapidly growing into an important technology for object identification and tracking applications. This gives rise to the most challenging RFID network planning (RNP) problem in the large-scale RFID deployment environment. RNP has been proven to be an NP-hard problem that involves many objectives and constraints. The application of evolutionary and swarm intelligence algorithms for solving multi-objective RNP (MORNP) has gained significant attention in the literature, while these proposed methods always transform multi-objective RNP into single-objective problem by the weighted coefficient approach. In this work, we propose a cooperative multi-objective artificial colony algorithm called CMOABC to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP. The experiment presents an exhaustive comparison of the proposed CMOABC and two successful multi-bjective techniques, namely the recently developed multi-objective artificial bee colony algorithm (MOABC) and nondominated sorting genetic algorithm II (NSGA-II), on instances of different nature, namely the two-objective and three-objective MORNP in the large-scale RFID scenario. Simulation results show that CMOABC proves to be superior for planning RFID networks compared to NSGA-II and MOABC in terms of optimization accuracy and computation robustness. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： RFID network planning Multi-objective optimization Cooperation artificial bee colony algorithm

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An artificial bee colony algorithm for the economic lot scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第4期52卷 1150-1170页

作者： Bulut, Onder Tasgetiren, M. Fatih Yasar Univ Dept Ind Engn Izmir Turkey

In this study, we present an artificial bee colony (ABC) algorithm for the economic lot scheduling problem modelled through the extended basic period (EBP) approach. We allow both power-of-two (PoT) and non-power-of-two multipliers in the solution representation. We develop mutation strategies to generate neighbouring food sources for the ABC algorithm and these strategies are also used to develop two different variable neighbourhood search algorithms to further enhance the solution quality. Our algorithm maintains both feasible and infeasible solutions in the population through the use of some sophisticated constraint handling methods. Experimental results show that the proposed algorithm succeeds to find the all the best-known EBP solutions for the high utilisation 10-item benchmark problems and improves the best known solutions for two of the six low utilisation 10-item benchmark problems. In addition, we develop a new problem instance with 50 items and run it at different utilisation levels ranging from 50 to 99% to see the effectiveness of the proposed algorithm on large instances. We show that the proposed ABC algorithm with mixed solution representation outperforms the ABC that is restricted only to PoT multipliers at almost all utilisation levels of the large instance.

关键词： economic lot scheduling problem extended basic period power-of-two policy artificial bee colony algorithm variable neighbourhood search heuristic optimization

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Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey

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ENERGY 2014年 69卷 638-647页

作者： Uzlu, Ergun Akpinar, Adem Ozturk, Hasan Tahsin Nacar, Sinan Kankal, Murat Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey Uludag Univ Fac Engn Dept Civil Engn TR-16059 Bursa Turkey

The primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC model performance with results of classical ANN models trained with the BP (back propagation) algorithm. Mean square and relative error were applied to evaluate model accuracy. The test set errors emphasized positive differences between the ANN-ABC and classical ANN models. After determining optimal configurations, three different scenarios were developed to predict future hydropower generation values for Turkey. Results showed the ANN-ABC method predicted hydroelectric generation better than the classical ANN trained with the BP algorithm. Furthermore, results indicated future hydroelectric generation in Turkey will range from 69.1 to 76.5 TWh in 2021, and the total annual electricity demand represented by hydropower supply rates will range from 14.8% to 18.0%. However, according to Vision 2023 agenda goals, the country plans to produce 30% of its electricity demand from renewable energy sources by 2023, and use 20% less energy than in 2010. This percentage renewable energy provision cannot be accomplished unless changes in energy policy and investments are not addressed and implemented. In order to achieve this goal, the Turkish government must reconsider and raise its own investments in hydropower, wind, solar, and geothermal energy, particularly hydropower. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Hydropower generation Neural networks Turkey

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