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Estimating transportation energy demand in Turkey using the artificial bee colony algorithm

ENERGY 2017年 122卷 301-310页

作者： Sonmez, Mustafa Akgungor, Ali Payidar Bektas, Salih Aksaray Univ Fac Engn Dept Civil Engn TR-68100 Aksaray Turkey Kirikkale Univ Fac Engn Dept Civil Engn TR-71451 Kirikkale Turkey

In this study, three different mathematical models were proposed to estimate transportation energy demand of Turkey using the artificial bee colony algorithm. In the development of the models, gross domestic product, population and total annual vehicle-km were taken as parameters. For transportation energy demand estimations, linear, exponential and quadratic forms of mathematical expressions were used. A 44-year-old historical data from 1970 to 2013 were utilized for the training and testing stages of the models. The performances of the models were then evaluated by six different global error measurement approaches. The models that were developed were used in two possible scenarios to forecast transportation energy demand of Turkey for a 21-year period from 2014 to 2034. artificial bee colony algorithm revealed the suitability of the optimization method for transportation energy planning and policy developments in Turkey. Furthermore, the results obtained from scenarios indicated that the energy demand of Turkey will be double that of 2013 by 2034. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Transportation energy demand artificial bee colony algorithm Transportation energy demand modeling Turkey

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An effective artificial bee colony algorithm for the flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第1-4期60卷 303-315页

作者： Wang, Ling Zhou, Gang Xu, Ye Wang, Shengyao Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

An effective artificial bee colony (ABC) algorithm is proposed in this paper for solving the flexible job-shop scheduling problem with the criterion to minimize the maximum completion time (makespan). The ABC algorithm stresses the balance between global exploration and local exploitation. First, multiple strategies are utilized in a combination to generate the initial solutions with certain quality and diversity as the food sources. Second, crossover and mutation operators are well designed for machine assignment and operation sequence to generate the new neighbor food sources for the employed bees. Third, a local search strategy based on critical path is proposed and embedded in the searching framework to enhance the local intensification capability for the onlooker bees. Meanwhile, an updating mechanism of population by generating the scout bees with the initialing strategy is proposed to enrich the searching behavior and avoid the premature convergence of the algorithm. In addition, a well-designed left-shift decoding scheme is employed to transform a solution to an active schedule. Numerical simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed ABC algorithm.

关键词： Flexible job-shop scheduling problem Makespan artificial bee colony algorithm Machine assignment Operation sequence Critical path

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Cluster based wireless sensor network routing using artificial bee colony algorithm

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WIRELESS NETWORKS 2012年第7期18卷 847-860页

作者： Karaboga, Dervis Okdem, Selcuk Ozturk, Celal Erciyes Univ Fac Engn Dept Comp Engn TR-38039 Kayseri Turkey

Due to recent advances in wireless communication technologies, there has been a rapid growth in wireless sensor networks research during the past few decades. Many novel architectures, protocols, algorithms, and applications have been proposed and implemented. The efficiency of these networks is highly dependent on routing protocols directly affecting the network life-time. Clustering is one of the most popular techniques preferred in routing operations. In this paper, a novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time. artificial bee colony algorithm, simulating the intelligent foraging behavior of honey bee swarms, has been successfully used in clustering techniques. The performance of the proposed approach is compared with protocols based on LEACH and particle swarm optimization, which are studied in several routing applications. The results of the experiments show that the artificial bee colony algorithm based clustering can successfully be applied to WSN routing protocols.

关键词： Wireless sensor networks Cluster based routing artificial bee colony algorithm

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A grey artificial bee colony algorithm

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APPLIED SOFT COMPUTING 2017年 60卷 1-17页

作者： Xiang, Wan-li Li, Yin-zhen Meng, Xue-lei Zhang, Chun-min An, Mei-qing Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China

artificial bee colony (ABC) algorithm is a very popular population-based algorithm. Unfortunately, there exists a shortcoming of slow convergence rate, which partly results from random choices of neighbor individuals regarding its solution search equation. A novel scheme for the choice of neighbors is introduced based on grey relational degrees between a current individual and its neighbors to overcome the insufficiency. Then, the chosen neighbor is used to guide the search process. Additionally, inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed. What is more, solution search equations using information of the best individual, an opposition-based learning method and a chaotic initialization technique are also integrated into the proposed algorithm called grey artificial bee colony algorithm (GABC for short). Subsequently, the effectiveness and efficiency of GABC are validated on a test suite composed of fifty-seven benchmark functions. Furthermore, it is also compared with a few state-of-the-artalgorithms. The related experimental results show the effectiveness and superiority of GABC. (C) 2017 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Grey relational analysis Opposition based learning Chaotic initialization Numerical optimization

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Multi objective lotsizing and scheduling with material constraints in flexible parallel lines using a Pareto based guided artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 659-680页

作者： Yue, Lei Guan, Zailin Zhang, Li Ullah, Saif Cui, Yanyan Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Univ Engn & Technol Dept Ind Engn Taxila Pakistan

Flexible production lines are significantly used in recent years for production of different variety of products in large quantity. These production lines usually produce mixed model products in lots which can improve the production efficiency and satisfy the different customer's demand. However, it is very challenging for factory planners to make decision on the optimal size of lots for each product model in production lines. Therefore, current research investigates lotsizing and mixed model scheduling problem in flexible parallel production lines considering sequence dependent setup times between mixed model products with aims to minimize makespan of production lines, balance the workload among lines and maximize the net profit, simultaneously. Additionally, a new constraint of material availability is introduced to the problem. A novel Pareto based guided artificial bee colony algorithm (PGABC) with three different guided mechanisms is designed for the current problem to obtain near optimal Pareto solutions. Taguchi method is adopted to tune the effective parameters of the proposed PGABC algorithm. Furthermore, nine different sets of instances including 90 problems are generated and tested using PGABC. The performance of PGABC is compared with other three famous methods used for multi objective optimization in literature including, multi objective artificial bee colony algorithm, non-dominated sorting genetic algorithm III (NSGAIII) and improved strength Pareto evolutionary algorithm (SPEA2). Computational results indicate that proposed PGABC outperforms the other considered algorithms both in terms of solution diversity and quality based on the considered test problem instances. Finally, an industrial case problem from a bus and coach company is solved by the proposed approach for mixed-model lotsizing and scheduling in parallel production lines.

关键词： Lotsizing Mixed model Multi-objectives artificial bee colony algorithm Sequence dependent setup Taguchi method

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A hierarchical learning based artificial bee colony algorithm for numerical global optimization and its applications

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APPLIED INTELLIGENCE 2024年第1期54卷 169-200页

作者： Zhang, Qingke Bu, Xianglong Gao, Hao Li, Tianqi Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China

The artificial bee colony algorithm (ABC) is a swarm intelligence algorithm inspired by honey bee harvesting behavior. It boasts the benefits of minimal parameters and strong exploration capabilities. However, the ABC algorithm is still susceptible to local optima entrapment and lacks consideration of selection probability in the onlooker bee phase, leading to reduced convergence accuracy in later search stages. To address these issues, this paper introduces an enhanced ABC algorithm called Hierarchical Learning-based artificial bee colony (HLABC). Initially, a hierarchical learning approach is devised, dividing the entire population into distinct layers based on solution quality. In this hierarchical approach, bees at lower layers can access much better advantageous information from higher layers. Secondly, the exploitation ability of onlooker bees is enhanced through novel strategies designed based on hierarchical learning. Thirdly, the exploration ability of scout bees is strengthened by implementing an opposition-based learning method. To evaluate the performance of the proposed algorithm, 69 benchmark functions from four benchmark suites (CEC2005, CEC2010, CEC2013 and CEC2022) are used to test the performance of HLABC, along with five variants of the ABC algorithm, The experimental statistical results show that the HLABC algorithm outperforms the ABC algorithm on all test problems with an average winning rate of 89%. Furthermore, to validate the performance of the HLABC algorithm in real-world optimization problems, this paper applies the HLABC algorithm to two practical applications: the deployment of wireless sensor networks (WSNs), the power scheduling problem in a smart home (PSPSH) and the multi-thresholding image segmentation (MIS). The experimental and statistical results demonstrate that HLABC is an efficient and stable optimizer. It shows better or comparable performance compared to other ABC variants when considering the quality of solutions for a sui

关键词： artificial bee colony algorithm Hierarchical learning Numerical global optimization Wireless sensor networks deployment Power scheduling problem Multi-thresholding image segmentation

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Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2013年第2期64卷 293-311页

作者： Lin, S-W Ying, K-C Chang Gung Univ Dept Informat Management Tao Yuan Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 10608 Taiwan

The order acceptance and scheduling (OAS) problem is an important topic for make-to-order production systems with limited production capacity and tight delivery requirements. This paper proposes a new algorithm based on artificial bee colony (ABC) for solving the single machine OAS problem with release dates and sequence-dependent setup times. The performance of the proposed ABC-based algorithm was validated by a benchmark problem set of test instances with up to 100 orders. Experimental results showed that the proposed ABC-based algorithm outperformed three state-of-art metaheuristic-based algorithms from the literature. It is believed that this study successfully demonstrates a high-performance algorithm that can serve as a new benchmark approach for future research on the OAS problem addressed in this study. Journal of the Operational Research Society (2013) 64, 293-311. doi:10.1057/jors.2012.47;published online 9 May 2012

关键词： order acceptance and scheduling problem single machine artificial bee colony algorithm sequence-dependent setup times

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Gbest-guided artificial bee colony algorithm for numerical function optimization

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APPLIED MATHEMATICS AND COMPUTATION 2010年第7期217卷 3166-3173页

作者： Zhu, Guopu Kwong, Sam City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments. (C) 2010 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Genetic algorithm Particle swarm optimization Differential evolution Biological-inspired optimization algorithm Numerical function optimization

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A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第8期30卷 2913-2925页

作者： Kucukkoc, Ibrahim Buyukozkan, Kadir Satoglu, Sule Itir Zhang, David Z. Univ Exeter Coll Engn Math & Phys Sci North Pk Rd Exeter EX4 4QF Devon England Balikesir Univ Fac Engn & Architecture Dept Ind Engn Cagis Campus Balikesir Turkey Istanbul Tech Univ Dept Ind Engn Istanbul Turkey Karadeniz Tech Univ Fac Engn Dept Ind Engn Kanuni Campus Trabzon Turkey

Typically, the total number of required workstations are minimised for a given cycle time (this problem is referred to as type-1), or cycle time is minimised for a given number of workstations (this problem is referred to as type-2) in traditional balancing of assembly lines. However, variation in workload distributions of workstations is an important indicator of the quality of the obtained line balance. This needs to be taken into account to improve the reliability of an assembly line against unforeseeable circumstances, such as breakdowns or other failures. For this aim, a new problem, called lexicographic bottleneck mixed-model assembly line balancing problem (LB-MALBP), is presented and formalised. The lexicographic bottleneck objective, which was recently proposed for the simple single-model assembly line system in the literature, is considered for a mixed-model assembly line system. The mathematical model of the LB-MALBP is developed for the first time in the literature and coded in GAMS solver, and optimal solutions are presented for some small scale test problems available in the literature. As it is not possible to get optimal solutions for the large-scale instances, an artificial bee colony algorithm is also implemented for the solution of the LB-MALBP. The solution procedures of the algorithm are explored illustratively. The performance of the algorithm is also assessed using derived well-known test problems in this domain and promising results are observed in reasonable CPU times.

关键词： Lexicographic bottleneck Assembly line balancing Mixed-model lines Mathematical model artificial bee colony algorithm

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Optimal Capacitor Location and Sizing for Reducing the Power Loss on the Power Distribution Systems due to the Dynamic Load of the Electric Buses Charging System using the artificial bee colony algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第4期16卷 1821-1831页

作者： Boonraksa, Terapong Boonraksa, Promphak Marungsri, Boonruang Rajamangala Univ Technol Rattanakosin Sch Elect Engn Salaya Nakhon Pathom Thailand Bangkok Thonburi Univ Sch Elect Engn Bangkok Thailand Suranaree Univ Technol Sch Elect Engn Nakhon Ratchasima Thailand

The modern power distribution system is connected to many loads, affecting the power system reliability and causing more power loss. One of the new loads is the battery charging station for electric vehicles or electric buses. The charging load will have a charge that varies with the operating time of each vehicle. Therefore, this article focuses on optimal capacitor location and sizing for reducing the power loss on the power distribution systems due to the dynamic load of the electric bus system using the artificial bee colony algorithm. The dynamic charging load of 3 types of electric bus systems was applied: Plug-in charging, Pantograph charging, and Battery swapping charging. The simulation results show that the installation of 0.3167 MVar capacitors for Plug-in electric bus systems reduced the power loss by 0.5465 MWh. In comparison, the installation of 0.2893 MVar capacitors for the Pantograph electric bus systems reduces the power loss by 0.5459 MWh. The installation of 0.2922 MVar capacitors for the Battery swapping systems reduces the power loss of 0.5517 MWh. The installation of shunt capacitors can reduce the power distribution system's power loss and the benefit of capacitors installing 1,501.27, 1,443.76, and 1,474.02 USD/year, respectively.

关键词： Shunt capacitors Power distribution systems Dynamic load flow Electric buses artificial bee colony algorithm

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