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Optimal filter design using an improved artificial bee colony algorithm

INFORMATION SCIENCES 2014年 281卷 443-461页

作者： Bose, Digbalay Biswas, Subhodip Vasilakos, Athanasios V. Laha, Sougata Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 32 W Bengal India Kuwait Univ Dept Comp Sci Safat 3060 Kuwait

The domain of analog filter design revolves around the selection of proper values of the circuit components from a possible set of values manufactured keeping in mind the associated cost overhead. Normal design procedures result in a set of values for the discrete components that do not match with the preferred set of values. This results in the selection of approximated values that cause error in the associated design process. An optimal solution to the design problem would include selection of the best possible set of components from the numerous possible combinations. The search procedure for such an optimal solution necessitates the usage of Evolutionary Computation (EC) as a potential tool for determining the best possible set of circuit components. Recently algorithms based on Swarm Intelligence (SI) have gained prominence due to the underlying focus on collective intelligent behavior. In this paper a novel hybrid variant of a swarm-based metaheuristics called artificial bee colony (ABC) algorithm is proposed and shall be referred to as CRbABC_Dt (Collective Resource-based ABC with Decentralized tasking) and it incorporates the idea of decentralization of attraction from super-fit members along with neighborhood information and wider exploration of search space. Two separate filter design instances have been tested using CRbABC_Dt algorithm and the results obtained are compared with several competitive state-of-the-art optimizing algorithms. All the components considered in the design are selected from standard series and the resulting deviation from the idealized design procedure has been investigated. Additional empirical experimentation has also been included based on the benchmarking problems proposed for the CEC 2013 Special Session & Competition on Real-Parameter Single Objective Optimization. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Analog filter design artificial bee colony algorithm Global optimization Information sharing Passive circuit component Swarm intelligence

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A Hybrid Differential artificial bee colony algorithm based tuning of fractional order controller for Permanent Magnet Synchronous Motor drive

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2014年第3期5卷 327-337页

作者： Rajasekhar, Anguluri Abraham, Ajith Pant, Millie Natl Inst Technol Dept Elect & Elect Engn Warangal 506004 Andhra Pradesh India Tech Univ Ostrava VSB Fac Comp Sci & Elect Engn Ostrava Czech Republic Machine Intelligence Res Labs MIR Labs Auburn WA USA Indian Inst Technol Dept Paper & Pulp Technol Roorkee 247667 Uttar Pradesh India

In this paper a novel Hybrid Differential artificial bee colony algorithm (HDABCA) has been proposed for designing a fractional order proportional-integral (FO-PI) speed controller in a Permanent Magnet Synchronous Motor (PMSM) drive. FO-PI controllers' parameters involve proportionality constant, integral constant and integral order, and hence its design is more complex than that of the usual Integral-order proportional-integral controller. To overcome this complexity in designing, we had used the proposed hybrid algorithm, such that all the design specifications of the motor are satisfied. In order to digitally realize the FO-PI controller, an Oustaloup approximation method has been used. Simulations and comparisons of proposed HDABCA with conventional methods and also other state-of-art methods demonstrate the competence of the proposed approach, especially for actuating fractional order controller for integer order plants.

关键词： PMSM FO-PI speed controller Differential evolution artificial bee colony algorithm

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Applicability of artificial bee colony algorithm for nurse scheduling problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2014年第Supp 1期7卷 121-136页

作者： Buyukozkan, Kadir Sarucan, Ahmet Karadeniz Tech Univ Dept Ind Engn TR-61080 Trabzon Turkey Selcuk Univ Dept Ind Engn TR-42000 Selcuklu Konya Turkey

This paper describes the first artificial bee colony (ABC) algorithm approach applied to nurse scheduling evaluated under different working environments. For this purpose, the model has been applied on a real hospital where data taken from different departments of the hospital were used and the schedules from the model were compared with the existing schedules. The results obtained indicated that the proposed model exhibits success in solving the nurse scheduling problems in hospitals.

关键词： artificial bee colony algorithm nurse listing nurse scheduling task assignment

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A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities

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APPLIED MATHEMATICAL MODELLING 2014年第3期38卷 1111-1132页

作者： Li, Jun-Qing Pan, Quan-Ke Tasgetiren, M. Fatih Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Yasar Univ Dept Ind Engn Izmir Turkey

This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Flexible job-shop scheduling problem with maintenance activities Multi-objective optimization artificial bee colony algorithm Tabu search

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Optimal reactive power flow solution in multiterminal AC-DC systems based on artificial bee colony algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第5期22卷 1159-1176页

作者： Yalcin, Faruk Arifoglu, Ugur Sakarya Univ Dept Mechatron Engn Sakarya Turkey Sakarya Univ Dept Elect Elect Engn Sakarya Turkey

Active power loss optimization is one of the important goals in electrical power systems and it is provided by optimal reactive power flow (ORPF). In this study, a new approach for the solution of the ORPF in multiterminal AC-DC systems is proposed. This approach provides 3 main contributions. First, the convergence problem in the AC-DC power flow is solved. Second, the problem of getting stuck in local minima during the optimization process is overcome. Third, a better global optimum point is obtained for the ORPF. Active power loss optimization is implemented through the artificial bee colony (ABC) algorithm, which is a heuristic optimization method, by considering the system constraints. This study is the first to use the ABC algorithm for the solution of the ORPF in multiterminal AC-DC systems. The proposed approach is tested on the modified IEEE 14-bus AC-DC test system and the obtained results from this and other studies are given. Comparative results prove that this approach is more efficient and reliable in reaching a global optimum while satisfying all of the system constraints.

关键词： Optimal reactive power flow AC-DC artificial bee colony algorithm genetic algorithm

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Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights

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AUTOMATION IN CONSTRUCTION 2014年第Mar.期38卷 14-29页

作者： Yahya, M. Saka, M. P. Univ Bahrain Sakhir Bahrain Minist Municipal Affairs & Urban Planning Manama Bahrain Univ Bahrain Dept Civil Engn Sakhir Bahrain

Construction site layout planning has been recognized as a critical step in construction planning. The basic function of this process is to find the best arrangement of the temporary facilities according to multiple objectives that may conflict with each other and subjected to logical and resource constraints. The formulation of the construction site layout planning problem as an optimization problem turns out to be a nonlinear programming problem where there are conflicting multi-objectives to be achieved. It is shown that the swarm intelligence based meta-heuristic algorithms are quite powerful in obtaining the solution of such hard to solve type of optimization problems. In this study a multi objective artificial bee colony (MOABC) via Levy flights algorithm is proposed to determine the optimum construction site layout. The model is intended to optimize the dynamic layout of unequal-area under two objective functions. The performance of MOABC with Levy flights is demonstrated on a real benchmark construction engineering of construction site layout planning problem and the optimum solution obtained is compared with the one determined by the ant colony algorithm. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Construction site layout planning Swarm intelligence Levy flights artificial bee colony algorithm

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Improved sheep flock heredity algorithm and artificial bee colony algorithm for scheduling m-machine flow shops lot streaming with equal size sub-lot problems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第5期52卷 1509-1527页

作者： Chakaravarthy, G. Vijay Marimuthu, S. Ponnambalam, S. G. Kanagaraj, G. Fatima Michael Coll Engn & Technol Dept Mech Engn Madurai Tamil Nadu India Latha Mathavan Engn Coll Dept Mech Engn Madurai Tamil Nadu India Monash Univ Sch Engn Selangor Malaysia Thiagarajar Coll Engn Dept Mech Engn Madurai Tamil Nadu India

The objective of this paper is to develop intelligent search heuristics to solve n-jobs, m-machines lot streaming problem in a flow shop with equal size sub-lots where the objective is to minimise makespan and total flow time independently. Improved sheep flock heredity algorithm (ISFHA) and artificial bee colony (ABC) algorithms are applied to the problem above mentioned. The performance of these algorithms is evaluated against the algorithms reported in the literature. The computational analysis shows the better performance of ISFHA and ABC algorithms.

关键词： flow shop scheduling lot streaming equal size sub-lots improved sheep flock heredity algorithm artificial bee colony algorithm

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hABCDE: A hybrid evolutionary algorithm based on artificial bee colony algorithm and differential evolution

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APPLIED MATHEMATICS AND COMPUTATION 2014年 238卷 370-386页

作者： Xiang, Wanli Ma, Shoufeng An, Meiqing Tianjin Univ Inst Syst Engn Tianjin 300072 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China

artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms which has been proven to be competitive with other population based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which lacks the guidance of better solutions and much more exchange of information between the old solution and new solution. Inspired by gbest-guided ABC (GABC), a new solution search equation with the direction of better solutions, is introduced and combined with the original one. Moreover, many more dimensions of an old solution are perturbed to enhance the level of information exchange between the two solutions (social learning). And then a modified differential evolution (DE) is also incorporated into the modified ABC in view of the fast convergence speed of DE. Subsequently, a new population catastrophe scheme is introduced in order to further achieve better compromise between the exploration and the exploitation. Based on the above explanation, this paper presents a novel hybrid evolutionary algorithm named hABCDE, which integrates a modified ABC and a modified DE to solve numerical optimization problems. Finally, the experimental results tested on a set of 20 benchmark functions show that the hABCDE algorithm can outperform ABC, DE and a few other state-of-the-art DE variants in most cases. (C) 2014 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Differential evolution Chaotic systems Catastrophe Numerical optimization

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Buckling load optimization of laminated plates via artificial bee colony algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2014年第4期52卷 755-765页

作者： Topal, Umut Ozturk, Hasan Tahsin Karadeniz Tech Univ Fac Technol Dept Civil Engn TR-61830 Trabzon Turkey

In this present work, artificial bee colony algorithm (ABCA) is used to optimize the stacking sequences of simply supported antisymmetric laminated composite plates with criticial buckling load as the objective functions. The fibre orientations of the layers are selected as the optimization design variables with the aim to fmd the optimal laminated plates. In order to perform the optimization based on the ABCA, a special code is written in MATLAB software environment. Several numerical examples are presented to illustrate this optimization algorithm for different plate aspect ratios, number of layers and load ratios.

关键词： laminated plates artificial bee colony algorithm buckling optimization

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Enhancing artificial bee colony algorithm using more information-based search equations

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INFORMATION SCIENCES 2014年 270卷 112-133页

作者： Gao, Wei-feng Liu, San-yang Huang, Ling-ling China Univ Petr Sch Sci Qingdao 266580 Peoples R China Xidian Univ Sch Sci Xian 710071 Shannxi Peoples R China

artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, in this paper, we propose a novel ABC method called as EABC to improve the performance of ABC. In our method, in order to balance the exploration and the exploitation, two new search equations are presented to generate candidate solutions in the employed bee phase and the onlookers phase, respectively. Additionally, we use a more robust calculation to determine and compare the quality of alternative solutions. Experiments are conducted on a set of 48 benchmark functions and also two engineering optimization problems. The results show that EABC significantly improves the performance of ABC, offering faster global convergence, higher solution quality, and stronger robustness when compared with the other algorithms. (c) 2014 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Evolutionary algorithms Search equation Differential evolution

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