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Multi-objective dynamic optimal power flow using improved artificial bee colony algorithm based on Pareto optimization

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2016年第4期26卷 692-712页

作者： Liang, Ruey-Hsun Wu, Chang-Yo Chen, Yie-Tone Tseng, Wan-Tsun Natl Yunlin Univ Sci & Technol Dept Elect Engn Touliu 640 Yunlin Taiwan

This paper presents an improved artificial bee colony algorithm based on Pareto optimization for solving the multi-objective dynamic optimal power flow (DOPF) problem. To simulate the dynamic operational situations of a power system in 24h, various load demands related to each hour were considered in an OPF problem. The multi-objective DOPF problem was solved by minimizing total generation cost, total emission, and total real power loss while meeting some constraints. When considering a multi-objective optimization problem, one of the objective functions is improved, and the other objective functions deteriorate. These functions cannot be improved simultaneously because they contradict each other. The global Pareto optimal front, which is composed of a set of nondominated solutions, was identified in the multi-objective optimization problem. Power system operators can appropriately select one of the nondominated solutions according to various situations. An improved artificial bee colony algorithm was used as optimal tool on the optimization update procedure. To avoid yielding local optimal solutions to the problem, a skill called chaos queues was added. To demonstrate the effectiveness of the proposed method, the multi-objective DOPF was performed on the IEEE 30-bus and 118-bus systems. The results of using the proposed method were compared with the results of using other algorithms, revealing the effectiveness of the proposed method in the multi-objective DOPF problem. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： artificial bee colony algorithm dynamic optimal power flow Pareto optimization power system operation

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An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training

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APPLIED SOFT COMPUTING 2016年 49卷 423-436页

作者： Karaboga, Dervis Kaya, Ebubekir Erciyes Univ Dept Comp Engn Fac Engn TR-38039 Kayseri Turkey Nevsehir Haci Bektas Veli Univ Nevsehir Vocat Coll Dept Comp Technol TR-50300 Nevsehir Turkey

In this study, we propose an Adaptive and Hybrid artificial bee colony (aABC) algorithm to train ANFIS. Unlike the standard ABC algorithm, two new parameters are utilized in the solution search equation. These are arithmetic crossover rate and adaptivity coefficient. aABC algorithm gains the rapid convergence feature with the usage of arithmetic crossover and it is applied on two different problem groups and its performance is measured. Firstly, it is performed over 10 numerical 'benchmark functions'. The results show that aABC algorithm is more efficient than standard ABC algorithm. Secondly, ANFIS is trained by using aABC algorithm to identify the nonlinear dynamic systems. Each application begins with the randomly selected initial population and then average RMSE is obtained. For four examples considered in ANFIS training, train error values are respectively computed as 0.0344, 0.0232, 0.0152 and 0.0205. Also, test error values for these examples are respectively found as 0.0255, 0.0202, 0.0146 and 0.0295. Although it varies according to the examples, performance increase between 4.51% and 33.33% occurs. Additionally, it is seen that aABC algorithm converges bettter than ABC algorithm in the all examples. The obtained results are compared with the neuro-fuzzy based approaches which are commonly used in the literature and it is seen that the proposed ABC variant can be efficiently used for ANFIS training. (C) 2016 Elsevier B.V. All rights reserved.

关键词： ANFIS Neuro-fuzzy artificial bee colony algorithm Swarm intelligence Nonlinear system identification

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A hybrid approach to artificial bee colony algorithm

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NEURAL COMPUTING & APPLICATIONS 2016年第2期27卷 387-409页

作者： Ma, Lianbo Zhu, Yunlong Zhang, Dingyi Niu, Ben Chinese Acad Sci Lab Informat Serv & Intelligent Control Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China

In this paper, we put forward a hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation. The bee life-cycle model is firstly constructed, which means that each individual can reproduce or die dynamically throughout the searching process and population size can dynamically vary during execution. With the comprehensive learning, the bees incorporate the information of global best solution into the search equation for exploration, while the Powell's search enables the bees deeply to exploit around the promising area. Finally, we instantiate a hybrid artificial bee colony (HABC) optimizer based on the proposed model, namely HABC. Comprehensive test experiments based on the well-known CEC 2014 benchmarks have been carried out to compare the performance of HABC against other bio-mimetic algorithms. Our numerical results prove the effectiveness of the proposed hybridization scheme and demonstrate the performance superiority of the proposed algorithm.

关键词： artificial bee colony algorithm Varying population Life cycle Comprehensive learning

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Service Composition Instantiation Based on Cross-Modified artificial bee colony algorithm

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China Communications 2016年第10期13卷 233-244页

作者： Lei Huo Zhiliang Wang School of Automation University of Science and Technology Beijing School of Computer and Science University of Science and Technology Beijing

Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified artificial bee colony algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.

关键词： optimization of service composition optimal service instantiation artificial bee colony algorithm swarm algorithm cross strategy

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An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

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KNOWLEDGE-BASED SYSTEMS 2016年第Jul.期104卷 14-23页

作者： Nseef, Shams K. Abdullah, Salwani Turky, Ayad Kendall, Graham Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp Bangi 43600 Selangor Malaysia RMIT Univ Sch Comp Sci & Informat Technol Adelaide SA Australia Univ Nottingham Malaysia Campus Semenyih Malaysia Univ Nottingham ASAP Res Grp Nottingham NG7 2RD England

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results. (C) 2016 Published by Elsevier B.V.

关键词： Dynamic optimisation artificial bee colony algorithm Adaptive multi-population method Meta-heuristics

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A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 102卷 359-367页

作者： Asadzadeh, Leila Payame Noor Univ Dept Comp Engn & Informat Technol Tehran Iran

The job shop scheduling problem (ISSP) has attracted much attention in the field of both information sciences and operations research. Many heuristic algorithms such as genetic algorithms, particle swarm optimization, artificial bee colony algorithm, and ant colony optimization have been presented to solve it. Parallelization is one of the best approaches that can be used to enhance the performance of these heuristic algorithms. In this paper, we propose a parallel artificial bee colony algorithm to solve the job shop scheduling problem. In this method, artificial bee colony algorithm consists of several colonies that locate on different hosts of the network and algorithm is carried out in various colonies in parallel manner. The communication between colonies is carried out by exchanging migrants. A dynamic migration strategy is used to determine when a colony must communicate by its neighbors. Benchmark instances are used to investigate the performance of the proposed approach. The results show that the proposed parallel artificial bee colony algorithm improves the efficiency. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Job shop scheduling problem Heuristic algorithms artificial bee colony algorithm Local search procedure Parallel artificial bee colony algorithm

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An improved artificial bee colony algorithm based on the strategy of global reconnaissance

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SOFT COMPUTING 2016年第12期20卷 4825-4857页

作者： Ma, Wei Sun, Zhengxing Li, Junlou Song, Mofei Lang, Xufeng Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Nanjing Inst Tourism & Hospitality Nanjing Jiangsu Peoples R China

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence and low solution precision. Moreover, it can easily become stuck at local optima. The scout bees start to search for food sources randomly and then they share nectar information with other bees. Thus, this paper proposes a global reconnaissance foraging swarm optimization algorithm that mimics the intelligent foraging behavior of scouts in nature. First, under the new scouting search strategies, the scouts conduct global reconnaissance around the assigned subspace, which is effective to avoid premature convergence and local optima. Second, the scouts guide other bees to search in the neighborhood by applying heuristic information about global reconnaissance. The cooperation between the honeybees will contribute to the improvement of optimization performance and solution precision. Finally, the prediction and selection mechanism is adopted to further modify the search strategies of the employed bees and onlookers. Therefore, the search performance in the neighborhood of the local optimal solution is enhanced. The experimental results conducted on 52 typical test functions show that the proposed algorithm is more effective in avoiding premature convergence and improving solution precision compared with some other ABCs and several state-of-the-art algorithms. Moreover, this algorithm is suitable for optimizing high-dimensional space optimization problems, with very satisfactory outcomes.

关键词： Scout bees Global reconnaissance artificial bee colony algorithm Function optimization Chaotic sequence

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Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution

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ADVANCES IN ENGINEERING SOFTWARE 2016年 92卷 1-14页

作者： Aydogdu, I. Akin, A. Saka, M. P. Akdeniz Univ Dept Civil Engn TR-07058 Antalya Turkey Trinity Meyer Util Struct Memphis TN USA Univ Bahrain Dept Civil Engn Isa Town Bahrain

Optimum design of real world steel space frames under design code provisions is a complicated optimization problem due to the presence of large numbers of highly nonlinear constraints and discrete design variables. The use of gradient based optimization techniques in finding the optimum solution of such large design problems is cumbersome due to the selection of initial design points and convergence difficulties while meta heuristic algorithms do not suffer such problems. artificial bee colony (ABC) algorithm is one of the recent additions to the swarm intelligence based meta-heuristic search techniques that mimic natural foraging behavior of honey bees. In this study optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC and its solution is obtained by using enhanced artificial bee colony algorithm. The performance of artificial bee colony algorithm is improved by adding Levy flight distribution in the search of scout bees. Real world steel space frames are designed with the new algorithm developed in this study to demonstrate its robustness and efficiency. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Steel space frames Real world structures Optimum structural design Load and resistance factor design Meta-heuristic search techniques artificial bee colony algorithm Swarm intelligence Levy flight distribution

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A new improved artificial bee colony algorithm for ship hull form optimization

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ENGINEERING OPTIMIZATION 2016年第4期48卷 672-686页

作者： Huang, Fuxin Wang, Lijue Yang, Chi George Mason Univ Sch Phys Astron & Computat Sci Fairfax VA 22030 USA

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

关键词： artificial bee colony algorithm resistance ship hull form optimization

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Discrete artificial bee colony algorithm based Optical Character Recognition

Discrete Artificial Bee Colony Algorithm based Optical Chara...

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IEEE India Council International Conference

作者： Nishal Ancelette Pereira Prajwal Rao Akshay K. Kallianpur K. G. Srinivasa Dept. of Electronics and Communication Engg. Ramaiah Inst. of Tech. Department of Information Technology Ch Brahm Prakash Government Engineering College

ISBN: (纸本)9781538643198

Recognizing and processing documents, especially historical manuscripts, has been a challenge owing to the difficulty in identifying cursive handwritten characters. This paper presents a simple technique for Optical Character Recognition based on Swarm intelligence to extract textual information from printed or handwritten documents. Primarily, combinations of different filters to remove noise and background stutter is used. After employing frequency domain transforms for feature extraction on the image containing the textual data, the Discrete artificial bee colony algorithm (DABC) is used for selecting useful features. Experimental results show that DABC based feature selection method provides good recognition by removing noise and redundant features. The proposed algorithm works well not only with machine-generated characters, but also with handwritten characters of multiple languages including English, Hindi and Kannada. The technique is comparatively more complete as it works for both machine generated and Handwritten databases, and also aims at achieving language independence.

关键词： optical character recognition Character recognition language-independent Feature extraction artificial bee colony algorithm HANDWRITTEN CHARACTERS Particle swarm optimization Noise Redundant

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