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An artificial bee colony algorithm for the maximally diverse grouping problem

INFORMATION SCIENCES 2013年 230卷 183-196页

作者： Rodriguez, Francisco J. Lozano, M. Garcia-Martinez, C. Gonzalez-Barrera, Jonathan D. Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Cordoba Dept Comp & Numer Anal Cordoba Spain Univ La Laguna Dept Stat Operat Res & Comp Sci San Cristobal Laguna Spain

In this paper, an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem. This complex optimisation problem consists of forming maximally diverse groups with restricted sizes from a given set of elements. The artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behaviour of honeybees. The behaviour of this algorithm is determined by two search strategies: an initialisation scheme employed to construct initial solutions and a method for generating neighbouring solutions. More specifically, the proposed approach employs a greedy constructive method to accomplish the initialisation task and also employs different neighbourhood operators inspired by the iterated greedy algorithm. In addition, it incorporates an improvement procedure to enhance the intensification capability. Through an analysis of the experimental results, the highly effective performance of the proposed algorithm is shown in comparison to the current state-of-the-art algorithms which address the problem. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Maximally diverse grouping problem artificial bee colony algorithm Iterated greedy

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Parameter identification for Van Der Pol-Duffing oscillator by a novel artificial bee colony algorithm with differential evolution operators

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APPLIED MATHEMATICS AND COMPUTATION 2013年 222卷 132-144页

作者： Gao, Fei Lee, Xue-jing Fei, Feng-xia Tong, Heng-qing Qi, Yi-bo Deng, Yan-fang Balasingham, Ilangko Zhao, Hua-ling Wuhan Univ Technol Dept Math Sch Sci Wuhan 430070 Hubei Peoples R China Norwegian Univ Sci & Technol Signal Proc Grp Dept Elect & Telecommun N-7491 Trondheim Norway Wuhan Univ Technol Sch Sci Dept Stat Wuhan 430070 Hubei Peoples R China

This paper is concerned with the uncertain parameters' identification of Van Der Pol-Duffing oscillators (VDPD) through a non-Lyapunov way with ABC and ABCDEO respectively by converting the problem into a multiple modal non-negative functions' minimization, which is of vital significance and attracts increasing interests in various research fields. Firstly, a novel artificial bee colony (ABC) algorithm with differential evolution operators (ABCDEO) is introduced to accelerate ABC due to the slow nature of the collective intelligence of honey bee swarms, by doing differential evolution operators to the employed bees colony in a certain probability, a special region contraction rules, and swarm re-initialization strategy based on number-theory nets. Secondly, the unknown parameters of VDPD for three main physical states, the cases with noise, and a comprehensive set of twelve complex benchmark functions including a wide range of dimensions, are employed for experimental verification. Experimental results confirm that the ABCDEO is a successful method in VDPD's parameters' identification. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Parameters identification artificial bee colony algorithm Differential evolution operators Swarm re-initialization strategy Van Der Pol-Duffing oscillators Nonlinear optimization

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A hybrid artificial bee colony algorithm for the job shop scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2013年第1期141卷 167-178页

作者： Zhang, Rui Song, Shiji Wu, Cheng Nanchang Univ Sch Econ & Management Nanchang 330031 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. In terms of the objective function, most existing research has been focused on the makespan criterion (i.e., minimizing the overall completion time). However, for contemporary manufacturing firms, the due date related performance is usually more important because it is crucial for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a novel artificial bee colony (ABC) algorithm is proposed for solving the problem. A neighborhood property of the problem is discovered, and then a tree search algorithm is devised to enhance the exploitation capability of ABC. According to extensive computational tests, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness criterion. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Job shop scheduling problem artificial bee colony algorithm Tree search Neighborhood property

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Optimum cost design of RC columns using artificial bee colony algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2013年第5期45卷 643-654页

作者： Ozturk, Hasan Tahsin Durmusa, Ahmet Karadeniz Tech Univ Fac Engn Civil Trabzon Turkey

Optimum cost design of columns subjected to axial force and uniaxial bending moment is presented in this paper. In the formulation of the optimum design problem, the height and width of the column, diameter and number of reinforcement bars are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the column consisting the cost of concrete, steel, and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The artificial bee colony algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

关键词： optimization metaheuristic artificial bee colony algorithm reinforced concrete columns

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A novel artificial bee colony algorithm for HVAC optimization problems

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HVAC&R RESEARCH 2013年第6期19卷 715-731页

作者： Zhang, Xin Fong, Kwong Fai Yuen, Shiu Yin City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Div Bldg Sci & Technol Kowloon Hong Kong Peoples R China

The strategy of simulation-optimization is popular in handling the design and energy management of centralized HVAC systems. An effective optimization algorithm is paramount to obtain the optimal design and operation of HVAC systems. Due to the multi-dimensional and highly constrained nature commonly found in HVAC problems, traditional optimization methods are generally not applicable. Instead, a metaheuristc algorithm, e.g., the genetic algorithm, is a good choice to achieve these purposes. In recent years, the paradigm of the artificial bee colony is emerging in the field of evolutionary computation. The artificial bee colony optimizes the problem through the foraging behavior of honey bees. This article presents a novel approach developed from this paradigm, called the one-position inheritance artificial bee colony algorithm, for handling HVAC system optimization. Through two HVAC example problems, it is found that the one-position inheritance artificial bee colony can more effectively and efficiently search the optimal solutions against the genetic algorithm and original artificial bee colony. This suggests that the one-position inheritance artificial bee colony is a good optimization algorithm to solve other HVAC engineering problems with a multi-dimensional, mixed discrete-continuous, and highly constrained search landscape.

关键词： bee colonies High Voltage Alternating Current Systems artificial bee colony algorithm heating ventilation and air conditioning Optimization algorithms bee Dosage Form Not Applicable Genetic algorithm (GA) Inheritance

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Swarm Intelligence Topology Optimization Based on artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2013年第1期14卷 115-121页

作者： Park, Ji-Yong Han, Seog-Young Hanyang Univ Sch Mech Engn Seoul 133791 South Korea Hanyang Univ Grad Sch Dept Mech Engn Seoul 133791 South Korea

artificial bee colony algorithm (ABCA) as one of swarm intelligence methods and finite element analysis are first adopted for structural topology optimization. The objective of this paper is to suggest how to apply ABCA and examine the effectiveness and applicability of the suggested ABCA in structural topology optimization. Since ABCA was originally developed for continuous function optimization problems, this paper describes considerable modifications made to ABCA in order to obtain an optimal topology for a structure. The waggle index update rule and fitness update scheme are developed and applied for obtaining stable and robust optimal topology and accelerating convergence rate based on the suggested ABCA. Typical examples of 2D structural models and a 3D structural problem are provided for comparing to the solid isotropic microstructure with penalization (SIMP). The desirable conclusions are obtained through the results of the examples based on the suggested ABCA.

关键词： artificial bee colony algorithm Topology optimization Roulette wheel method Finite element method

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Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第12期40卷 4785-4790页

作者： Alvarado-Iniesta, Alejandro Garcia-Alcaraz, Jorge L. Ivan Rodriguez-Borbon, Manuel Maldonado, Aide Autonomous Univ Ciudad Juarez Dept Ind & Mfg Engn Ciudad Juarez 32315 Chihuahua Mexico

To survive in today's competitive global market, companies must perform strategic changes in order to increase productivity, eliminating wasted materials, time, and effort. This study will examine how to optimize the time and effort required to supply raw material to different production lines in a manufacturing plant in Juarez, Mexico by minimizing the distance an operator must travel to distribute material from a warehouse to a set of different production lines with corresponding demand. The core focus of this study is similar to that of the Vehicle Routing Problem in that it is treated as a combinatorial optimization problem. The artificial bee colony algorithm is applied in order to find the optimal distribution of material with the aim of establishing a standard time for this duty by examining how this is applied in a local manufacturing plant. Results show that using this approach may be convenient to set standard times in the selected company. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Material flow Continuous improvement Standard time Vehicle routing problem artificial bee colony algorithm

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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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MMSE design of nonlinear Volterra equalizers using artificial bee colony algorithm

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MEASUREMENT 2013年第1期46卷 210-219页

作者： Singh, Th Suka Deba Chatterjee, Amitava Jadavpur Univ Elect Engg Dept Kolkata 700032 India

In this paper a novel approach for channel equalization is presented, where a framework for Volterra system is used to model both the channel and the equalizer. We propose development of first-order and second-order Volterra equalizers using minimum mean square error (MMSE) approach and design these equalizers using swarm intelligence based stochastic optimization algorithm which is applied to adapt the equalizer coefficients to the time varying channel. This work proposes to use the artificial bee colony (ABC) algorithm, recently introduced for global optimization, simulating the intelligent foraging behavior of honey bee swarm in a simple, robust, and flexible manner. For comparative analysis, adaptive equalizers like least mean squares (LMSs) equalizer, recursive least squares (RLSs) equalizer and least mean p-Norm (LMP) equalizer and population based optimum equalizers employing PSO are also applied for identical problems and the superiority of the newly proposed algorithm is aptly demonstrated. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Channel equalization Volterra system artificial bee colony algorithm Minimum mean square error

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Parameter Estimation for Chaotic Systems by Improved artificial bee colony algorithm

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第3期10卷 756-762页

作者： Li, Xiangtao Yin, Minghao NE Normal Univ Coll Comp Sci Changchun 130117 Peoples R China

This paper is concerned with the parameter estimation of nonlinear chaotic system, which could be essentially formulated as a multi-dimension optimization problem. In this article, an improved artificial bee colony algorithm is implemented to solve parameter estimation for chaotic systems. This algorithm can combine the stochastic exploration of the artificial bee colony and the exploitation capability of new search strategies. Experiments have been conducted on Lorenz system and Chen system. The proposed algorithm is applied to estimate the parameters of these two systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to drift particle swarm optimization, particle swarm optimization and genetic algorithm from literature when considering the quality of the solutions obtained.

关键词： artificial bee colony algorithm Exploration Exploitation Chaotic System Parameter Estimation Improved algorithm

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