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An approximation algorithm for fuzzy polynomial interpolation with artificial bee colony algorithm

APPLIED SOFT COMPUTING 2013年第4期13卷 1997-2002页

作者： Mansouri, P. Asady, B. Gupta, N. Islamic Azad Univ Arak Branch Dept Math Arak Iran Univ Delhi Dept Comp Sci Delhi 110007 India

In this paper, a novel approximation algorithm for fuzzy polynomial interpolation using artificial bee colony algorithm to interpolate fuzzy data is discussed. However, we use our modified ABC (MABC;Mansouri et al. [13]) to perform the required task. Some examples (including the benchmark functions Griewank and Rastrigin) illustrate the rationality of the method and the validity of the solution. We compare our results with other methods including Genetic algorithm (GA), Particle Swarm algorithm (PSO). The results show that proposed method outperforms the other algorithms. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Fuzzy polynomial interpolation artificial bee colony algorithm

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An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32169-32194页

作者： Rahnema, Nouria Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms.

关键词： artificial bee colony algorithm Whale optimization algorithm Data clustering Optimization

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Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing

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APPLIED SOFT COMPUTING 2017年 56卷 379-397页

作者： Zhou, Jiajun Yao, Xifan South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China

As a new service-oriented smart manufacturing paradigm, cloud manufacturing (CMfg) aims at fully sharing and circulation of manufacturing capabilities towards socialization, in which composite CMfg service optimal selection (CCSOS) involves selecting appropriate services to be combined as a composite complex service to fulfill a customer need or a business requirement. Such composition is one of the most difficult combination optimization problems with NP-hard complexity. For such an NP-hard CCSOS problem, this study proposes a new approach, called multi-population parallel self-adaptive differential artificial bee colony (MPsaDABC) algorithm. The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution (DE) to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently. Moreover, the size of each subpopulation is dynamically adjusted based on the information derived from the search process. Different scales of the CCSOS problems are conducted to validate the effectiveness of the proposed algorithm, and the experimental results show that the proposed algorithm has superior performance over other hybrid and single population algorithms, especially for complex CCSOS problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cloud manufacturing Service composition Multi-population Differential evolution artificial bee colony algorithm

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Noncooperative Target Detection of Spacecraft Objects Based on artificial bee colony algorithm

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IEEE INTELLIGENT SYSTEMS 2019年第4期34卷 3-15页

作者： Liu, Xinyu Li, Donghui Doug, Na Ip, Wai Hung Yung, Kai Leung Tianjin Univ Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

Although heuristic algorithms have achieved the state-of-the-art performance for object detection, they have not been demonstrated to be sufficiently accurate and robust for multiobject detection. To address this problem, this article incorporates the concept of species into the artificial bee colony algorithm and proposes a multipeak optimization algorithm named species-based artificial bee colony (SABC). Then, we apply SABC to detect the noncooperative target (NCT) from two aspects: Multicircle detection and multitemplate matching. Experiments are conducted using real cases of "ShenZhou8" and "Apollo 9" space missions as well as the "Chang'e" camera point system developed by the Hong Kong Polytechnic University. Experimental results show that the proposed method is robust to detect NCT under various kinds of noise, weak light, and in-orbit and leads to accurate detection results with less time than other methods.

关键词： Optimization artificial bee colony algorithm Intelligent systems Heuristic algorithms Object detection Satellites Mathematical model

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Mobile Sink-Based Path Optimization Strategy in Wireless Sensor Networks Using artificial bee colony algorithm

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IEEE ACCESS 2019年 7卷 11668-11678页

作者： Lu, Yong Sun, Na Pan, Xiuqin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

In traditional static wireless sensor networks (WSNs), the unbalanced communication overhead in different regions will result in premature death of some monitoring nodes. The introduction of mobile sink in WSNs can not only balance the node traffic load, but also obtain even energy consumption of nodes, thus effectively avoiding the "hot spot" problem and prolonging the network lifetime. However, the mobility of the sink will lead to frequent changes in the aspect of network topology, which can aggravate the overhead of the node's reorganization in hierarchical WSNs. Therefore, it is essential to obtain the optimal trajectory design of the mobile sink so as to improve the ability of data gathering. In this paper, a mobile sink-based path optimization strategy in WSNs using artificial bee colony algorithm is proposed. First, the problem of overall energy consumption in the network can be transformed into the minimization of the total hops between all subnodes and the rendezvous points of the mobile sink. The objective function and the constraint criterion should be established. Second, an improved artificial bee colony algorithm is proposed to solve the problem. On the one hand, the cumulative factor is introduced to the position update of the employed bee stage to speed up the convergence of the algorithm. On the other hand, the Cauchy mutation operator is presented to increase the diversity of the feasible solution and enhance the global search ability of the algorithm. The simulation results show that the proposed algorithm is better than the traditional methods in the aspects of energy efficiency and the real-time performance of data collection.

关键词： Wireless sensor networks mobile sink path optimization artificial bee colony algorithm

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A multi-objective placement of phasor measurement units using fuzzified artificial bee colony algorithm, considering system observability and voltage stability

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JOURNAL OF EXPERIMENTAL & THEORETICAL artificial INTELLIGENCE 2016年第1-2期28卷 113-136页

作者： Jeyaraj, K. Arul Rajasekaran, V. Kumar, S. K. Nandha Chandrasekaran, K. PSNA Coll Engn & Technol Dept Elect Engn Dindigul Tamil Nadu India Natl Inst Technol Puducherry Dept Elect Engn Karaikal India

This paper presents Multi-objective Optimal Placement of Phasor measurement units (MOPP) to improve the performance of power system monitoring and control. It is proposed for achieving complete observability with a minimal number of phasor measurement units (PMUs) and maximising the voltage stability level of the system, simultaneously. As the above mentioned objectives are conflicting in nature, a fuzzified binary artificial bee colony algorithm is considered to solve the MOPP problem to offer a good tradeoff solution between the competing objectives. Here, fuzzy membership for each objective function is designed and proposed to decide the best solution of MOPP problem. Initially, weak buses are identified using the fast voltage stability index calculation for their close and reliable monitoring. This is done in order to prevent the outage of these buses. The conventional rule is used to minimise the number of PMUs and a new rule is proposed to maximise both the observability and voltage stability levels of the system. The efficiency of the artificial bee colony method is validated on IEEE 14, 30 and 57 bus test systems and it is verified by comparing the performance of proposed method with earlier works.

关键词： optimal placement fuzzy set theory artificial bee colony algorithm voltage stability evaluation phasor measurement unit

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Topological shape optimization scheme based on the artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2017年第10期18卷 1393-1401页

作者： Kim, Yong-Ho Han, Seog-Young Hanyang Univ Sch Mech Engn 222 Wangsimni Ro Seoul 04763 South Korea

We propose a new topological shape optimization scheme based on the artificial bee colony algorithm (ABCA). Since the level set method (LSM) and phase field method (PFM) in topological shape optimization have been developed, one of any algorithms in this field has not yet been proposed. To perform the topological shape optimization based on the ABCA, a variable called the "Boundary Element Indicator (BEI)," is introduced, which serves to define the boundary elements whenever a temporary candidate solution is found in the employed and onlooker bee phases. Numerical examples are provided to verify the performance of the suggested ABCA compared with the discrete LSM and the ABCA for topology optimization. The numerical examples showed that holes in the structure are naturally created in the ABCA for topological shape optimization. Moreover, the objective function of the suggested ABCA is lower than that of the ABCA for topology optimization, and is similar to that of the discrete LSM. The convergence rate of the suggested ABCA is the fastest among the comparison methods. Therefore, it can be verified that the suggested topological shape optimization scheme, based on the ABCA, is the most effective among the comparison methods.

关键词： artificial bee colony algorithm Topology optimization Shape optimization Finite element method Stochastic search method

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A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

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APPLIED SOFT COMPUTING 2015年 35卷 766-785页

作者： Xiang, Yi Zhou, Yuren Sun Yat Sen Univ Sch Data Sci & Comp Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510275 Guangdong Peoples R China

This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi objective optimization Multi colony model artificial bee colony algorithm Migration strategy Friedman test

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An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 34-42页

作者： Liao, Xiang Zhou, Jianzhong Ouyang, Shuo Zhang, Rui Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Hubei Peoples R China

Short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a set of constraints, which plays an important role in power system operations. In this paper, we propose to use an adaptive chaotic artificial bee colony (ACABC) algorithm to solve the SHS problem. In the proposed method, chaotic search is applied to help the artificial bee colony (ABC) algorithm to escape from a local optimum effectively. Furthermore, an adaptive coordinating mechanism of modification rate in employed bee phase is introduced to increase the ability of the algorithm to avoid premature convergence. Moreover, a new constraint handling method is combined with the ABC algorithm in order to solve the equality coupling constraints. We used a hydrothermal test system to demonstrate the effectiveness of the proposed method. The numerical results obtained by ACABC are compared with those obtained by the adaptive ABC algorithm (AABC), the chaotic ABC algorithm (CABC) and other methods mentioned in literature. The simulation results indicate that the proposed method outperforms those established optimization algorithms. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal scheduling artificial bee colony algorithm Adaptive Constrained optimization Swarm intelligence

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Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第Sup1期21卷 1995-2014页

作者： Koodalsamy, Chandrasekaran Simon, Sishaj Pulikottil Natl Inst Technol Dept Elect & Elect Engn Pondicherry India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

The economic dispatch (ED) problem is one of the important optimization problems in power system operation. Recently the power system has stressed the need for reliable, nonpolluting, and economic operation. Hence, 3 conflicting functions of reliability, emission, and fuel cost are considered in the objective function of the proposed ED problem. The problem is formulated as a nonsmooth and nonconvex problem when the valve-point effects of thermal units are considered in the proposed reliable emission and economic dispatch (REED) problem. This paper presents a multiobjective optimization methodology for solving the newly developed REED problem using a fuzzified artificial bee colony algorithm. The artificial bee colony algorithm is used to schedule the optimal dispatch and fuzzy membership approach is used to find the best compromise solution from the Pareto optimal set. The methodology is validated on an IEEE 30-bus system and 3-, 6-, 10-, 26-, and 40-unit systems and the results are compared with the existing literature. The results clearly show that the proposed method is able to produce well-distributed Pareto optimal solutions when compared with other methods reported in the literature.

关键词： artificial bee colony algorithm reliable emission and economic dispatch problem fuzzy set theory

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