Swarm intelligence is a research field that models the collective intelligence in swarms of insects or animals. Many algorithms that simulates these models have been proposed in order to solve a wide range of problems...
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Swarm intelligence is a research field that models the collective intelligence in swarms of insects or animals. Many algorithms that simulates these models have been proposed in order to solve a wide range of problems. The artificial bee colony algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behaviour of honey bee colonies. In this work, modified versions of the artificial bee colony algorithm are introduced and applied for efficiently solving real-parameter optimization problems. (C) 2010 Elsevier Inc. All rights reserved.
Dynamic economic dispatch (DED) is an important dynamic problem in power system operation and control. The objective of the problem is to schedule power generation for the online units over a time horizon, satisfying ...
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Dynamic economic dispatch (DED) is an important dynamic problem in power system operation and control. The objective of the problem is to schedule power generation for the online units over a time horizon, satisfying the unit and ramp rate constraints. In this paper, artificialbeecolony (ABC) algorithm is used to solve the dynamic economic dispatch problem for generating units with valve-point effect. The feasibility of the proposed method is validated with ten- and five-unit-test systems for a period of 24 hours. In addition, the effects of control parameters on the performance of ABC algorithm for DED problem are studied. Results obtained with the proposed approach are compared with other techniques in the literature. The results obtained substantiate the applicability of the proposed method for solving DED problems with non-smooth cost functions in terms of solution quality and computation efficiency. Copyright (C) 2010 John Wiley & Sons, Ltd.
Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and...
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Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and imperfect secondary path modeling require model-free ANC algorithms for practical applications. In this article, the artificialbeecolony (ABC) algorithm is improved to suitably develop a novel ANC algorithm without secondary path modeling. In addition, the FxLMS algorithm may fall into local minima, while our proposed algorithm features its global optimization ability. In order to have anti-interference ability, in our algorithm, a forgetting factor is introduced into the fitness function. Moreover, the least mean square (LMS) algorithm is integrated into the ABC algorithm to enhance its exploitation ability, further accelerate the convergence rate, and improve the noise reduction performance. Compared with some closely related population-based model-free ANC algorithms, our proposed algorithm has anti-interference ability, faster convergence rate, and better noise reduction performance. Simulations and experiments are conducted to illustrate the effectiveness of the proposed algorithm.
artificialbeecolony (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 i...
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artificialbeecolony (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.
artificialbeecolony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a lar...
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artificialbeecolony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. (C) 2009 Elsevier Inc. All rights reserved.
In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists ...
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In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificialbeealgorithm is used as the global optimization method to solve the dual- objective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.
Software aging is a common phenomenon that exists in systems that require long periods of operation, especially in Internet-of-Things environments. The back propagation (BP) neural network has been adopted widely to p...
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Software aging is a common phenomenon that exists in systems that require long periods of operation, especially in Internet-of-Things environments. The back propagation (BP) neural network has been adopted widely to predict the trend of software aging. However, the weight and threshold of the BP neural network are randomly initialized, so it is easy to get the unsatisfactory local optimal solutions and the convergence speed of computing is slow. In this paper, we propose a novel software aging prediction method using the artificial bee colony algorithm to optimize the BP neural network model for achieving better software aging prediction accuracy. The experiment results show that our method fits the prediction trend of software aging more accurately than the traditional BP neural network, and our method also has faster convergence speed and more stable prediction results.
Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificialbeecolony (HDABC) algori...
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Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificialbeecolony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificialbeecolony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.
In recent years, mixed model assembly lines are gaining popularity to produce a variety of models on the single-model assembly lines. Mixed model assembly lines have two types of problems which include sequencing of d...
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In recent years, mixed model assembly lines are gaining popularity to produce a variety of models on the single-model assembly lines. Mixed model assembly lines have two types of problems which include sequencing of different models on the line and balancing of assembly line. These two problems collectively affect the performance of assembly lines, and therefore, current research is aimed to balance the workload of different models on each station, to reduce the deviation of workload of a station from the average workload of all the stations and to minimize the total flow time of models on different stations simultaneously. A multi-objective artificialbeecolony (multi-ABC) algorithm for simultaneous sequencing and balancing problem with Pareto concepts and local search mechanism is presented. Two kinds of mixed model assembly line problems are analysed. For the first and second problems, each model task time data and precedence relation data are taken from standard assembly line problems, from operation research library (ORL) and from a truck manufacturing company in China, respectively. Both problems are solved using the proposed multi-ABC algorithm on two different demand scenarios of models, and the results are compared against the results obtained from a famous algorithm in the literature, i.e. non-dominated sorting genetic algorithm (NSGA) II. Computational results of the selected problems indicate that the proposed multi-ABC algorithm outperforms NSGA II and gives better Pareto solutions for the selected problems on different demand scenarios of models.
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 artificialbeecolony al...
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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 artificialbeecolony 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.
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