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Simultaneous optimization of elastic constants of laminated composites using artificial bee colony algorithm

ADVANCED COMPOSITES AND HYBRID MATERIALS 2019年第3期2卷 431-443页

作者： Esmaeeli, Meysam Kazemianfar, Behzad Nami, Mohammad Rahim Shiraz Univ Sch Mech Engn Shiraz *** Iran

Multifunctional application of laminated composites requires multi-objective optimization of their characteristics. In this paper, the ply angles of laminated composites are determined in order to maximize the effective in-plane elastic constants simultaneously. These constants are determined by considering a representative small element of the laminated composite and imposing the conditions of uniformity of out-of-plane stresses and in-plane strains at orthotropic layer interfaces. Then, by combining the artificial bee colony algorithm and various multi-objective optimization methods, the optimal ply angles and the corresponding co-optimized constants are determined. The correctness and accuracy of the method is verified not only by providing a comparison with the existing results but also by solving a known problem. The results are presented and discussed, considering different multi-objective optimization problems. The results show that the increasing number of layers in the problem of simultaneous optimization of Young's moduli not only does result in finding more Pareto optimal solutions in the feasible objective region but also shifts the Pareto frontier toward the utopia point. However, when the shear modulus optimization is engaged in the problem, using more than two layers only leads to obtaining more Pareto optimal answers.

关键词： Laminated composites Ply angles Effective elastic constants Multi-objective optimization artificial bee colony algorithm

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A modified artificial bee colony algorithm with its applications in signal processing

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第2-3期47卷 297-303页

作者： Yu, Xiangyu Zhu, Zucong South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Guangdong Peoples R China Guangdong Co Ltd China Mobile Grp Corp Maoming Branch Maoming Guangdong Peoples R China

artificial bee colony (ABC) algorithm is a biological-inspired optimisation algorithm proposed in recent years. It has been shown to have some advantages than most of conventional biological-inspired algorithms and has been widely used in many applications. However, the ABC algorithm does not consider the balance between global best and local best, and make ABC algorithm insufficiency. In this paper, a modified ABC algorithm is proposed, global best is introduced into the original ABC algorithm to modify the update equation of employed and onlooker bees while the equation for scouts remain unchanged. The effectiveness of the proposed approach is verified on the problem of peak-to-average power ratio reduction in orthogonal frequency division multiplexing signals and multi-level image segmentation. Simulation results showed that the proposed approach has better performance than traditional ABC algorithm with the same computational complexity.

关键词： artificial bee colony algorithm biological-inspired optimisation algorithm peak-to-average power ratio reduction partial transmit sequence image segmentation

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Optimal power flow resolution using artificial bee colony algorithm based grenade explosion method

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JOURNAL OF ELECTRICAL SYSTEMS 2016年第4期12卷 734-756页

作者： Salhi, Ahmed Naimi, Djemai Bouktir, Tarek Univ Mohamed Khider Dept Elect Engn BP145 Biskra 07000 Algeria Univ Mohamed Khider Lab Elect Engn Biskra Algeria Univ Ferhat Abbas Setif 1 Fac Technol Dept Elect Engn Setif Algeria

This paper exposes an Optimal Power Flow (OPF) problem resolution using a recent developed meta-heuristic algorithm called artificial bee colony (ABC) algorithm based Grenade Explosion Method (GEM). From many previous researches, the ABC algorithm has proved its goodness at exploration process in the search space for better solutions, but it is weak at exploitation process. The proposed GEM associated with the ABC algorithm gives more ability to enhance the exploitation process by proposing two modified versions of basic ABC algorithm, which are GABC1 by embedding GEM in the employed bee phase and GABC2 by introducing GEM in the onlooker bee phase. The effectiveness of the two proposed algorithms in the present work is investigated by solving the optimization problems of objective functions for smooth, non-smooth and piecewise quadratic curves of fuel cost function, along with the minimization of voltage magnitude deviation and voltage stability index. The simulation results on IEEE 30 bus and IEEE 57 bus test systems are compared to those of other optimization techniques in literature, showing that the two proposed algorithms are capable to give higher quality solutions efficiently for a complex OPF problem.

关键词： artificial bee colony algorithm Fuel cost Grenade explosion method Optimal power flow Voltage magnitude deviation Voltage stability index

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Modelling of optimal transportation route selection based on artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2024年第1-2期46卷 90-111页

作者： Liu, Honggang Zhang, Peilin Wu, Hua Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan Hubei Peoples R China Air Force Logist Univ Dept Air Transport & Delivery Guarantee Xuzhou Jiangsu Peoples R China

Optimising enterprise management and reducing logistics costs have become the common focus of Chinese logistics companies. This study mainly discusses the modelling of optimal transportation route selection based on artificial bee colony algorithm. In this paper, the designed bee colony algorithm is used to solve the cross-dock vehicle scheduling part in the mathematical model, and the cross-dock gate allocation and vehicle parking sequence scheduling schemes are obtained. Then, the scheduling scheme is used as the known condition of the path optimisation part, and the vehicle transportation path is optimised by using the bee colony algorithm, and the total cost is minimised in the process of mutual iteration. The research results show that the proportion of leading bees is 70% when the best calculated average value is obtained, and 90% when the variance of the calculated results is the smallest.

关键词： artificial bee colony algorithm transportation mode optimal path selection modelling

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PID controller enhanced with artificial bee colony algorithm for active magnetic bearing

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2022年第1期10卷 686-697页

作者： Du, Hualong Cui, Qiuyu Liu, Pengfei Ma, Xin Wang, He Univ Sci & Technol Liaoning Sch Mech Engn & Automat Anshan Peoples R China

To reduce the effect of non-linearity in air gap control in active magnetic bearings (AMB). The PID controller for the AMB is proposed in this study, which is optimized with a reformative artificial bee colony (RABC) algorithm. The RABC algorithm balances the exploitation and exploration capabilities of the ABC algorithm by introducing globally optimal solutions and improved food source probabilities. Simulation with six benchmark functions validates the proposed algorithm, and the results reveal that the RABC algorithm has higher search accuracy and faster search speed than previous ABC algorithm versions. The experimental results show that RABC-PID outperforms the other four approaches and has greater robustness when compared to traditional PID, PSO-PID, DE-PID, and GA-PID. Meanwhile, the RABC-PID controller makes the AMB system more stable.

关键词： artificial bee colony algorithm active magnetic bearing PID controller RABC algorithm parameter optimization

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A Hybrid Nature-Inspired artificial bee colony algorithm for Uncapacitated Examination Timetabling Problems

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JOURNAL OF INTELLIGENT SYSTEMS 2015年第1期24卷 37-54页

作者： Bolaji, Asaju Khader, Ahamad Al-Betar, Mohammed Awadallah, Mohammed Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Univ Ilorin Dept Comp Sci Ilorin Nigeria Univ Sains Malaysia Sch Comp Sci George Town Malaysia Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan

This article presents a Hybrid artificial bee colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

关键词： artificial bee colony algorithm Swarm Intelligence metaheuristics timetabling problem examination timetabling problem

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Multi-Objective artificial bee colony algorithm for Parameter-Free Neighborhood-Based Clustering

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2021年第4期12卷 186-204页

作者： Boudane, Fatima Berrichi, Ali Univ MHamed Bougara Boumerdes Dept Comp Sci LIMOSE Lab Boumerdes Algeria

Although various clustering algorithms have been proposed, most of them cannot handle arbitrarily shaped clusters with varying density and depend on the user-defined parameters which are hard to set. In this paper, to address these issues, the authors propose an automatic neighborhood-based clustering approach using an extended multi-objective artificial bee colony (NBC-MOABC) algorithm. In this approach, the ABC algorithm is used as a parameter tuning tool for the NBC algorithm. NBC-MOABC is parameter-free and uses a density-based solution encoding scheme. Furthermore, solution search equations of the standard ABC are modified in NBC-MOABC, and a mutation operator is used to better explore the search space. For evaluation, two objectives, based on density concepts, have been defined to replace the conventional validity indices, which may fail in the case of arbitrarily shaped clusters. Experimental results demonstrate the superiority of the proposed approach over seven clustering methods.

关键词： Arbitrary Shaped Clusters artificial bee colony algorithm Density-Based Clustering Multi-Objective Clustering Neighborhood

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Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm

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INFORMATION SCIENCES 2019年 484卷 350-366页

作者： Zeng, Shouzhen Chen, Shyi-Ming Teng, Mario Orlando Ningbo Univ Sch Business Ningbo 315211 Zhejiang Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

In this paper, we propose a new clustering-based fuzzy time series (FTS) forecasting method based on linear combinations of independent variables, the subtractive clustering algorithm and the artificial bee colony (ABC) algorithm. The subtractive clustering algorithm is used to automatically generate clusters of historical training data and get the cluster center of each cluster. The ABC algorithm is applied to obtain the optimal neighborhood radiuses of the subtractive clustering algorithm to get the optimal cluster center of each cluster of the historical training data. Based on the obtained cluster center of each cluster, the weighted contribution of each cluster with respect to each historical training datum is calculated. Finally, the proposed method constructs the linear combinations of independent variables of the historical training data using this weighted contribution to forecast the historical testing data. The proposed method gets higher forecasting accuracy rates than the existing methods for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the enrollments of the University of Alabama and the daily percentage of CO2. (C) 2019 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Fuzzy time series Subtractive clustering algorithm TAIEX

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Optimal Design of Water Distribution Network Using Improved artificial bee colony algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第5期47卷 3123-3136页

作者： Najarzadegan, Mohammad Reza Moeini, Ramtin Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

Urban water distribution networks are the most essential and costly network in each city. Major part of the urban water distribution network costs are related to the purchase of water distribution network accessories. Therefore, the cost of the water distribution network can be reduced by reducing this part. For this purpose, the design of water distribution network should be defined as an optimization model and solved it using an efficient method. Nowadays, meta-heuristics algorithms are the most efficient methods for solving optimization models. In this research, three benchmark problems, mean two-loop, New York, and Go Yang networks, are modeled in EPANET software and the optimization model is solved using an improved version of the artificial bee colony algorithm that is called in MATLAB software. To evaluate the efficiency of the proposed algorithm, the results are presented and compared with the standard version of the artificial bee colony algorithm and other available results. The results show that by using an improved artificial bee colony algorithm for two-loop network, New York, and Go Yang network, the objective function values (construction costs) and computational costs are 419,000 unit, 38.13 M$, and 175.78 MWon and 2500, 3600, and 2600, respectively. In addition, comparison of the results shows that the construction and computational costs are reduced compared with the result of the standard version.

关键词： artificial bee colony algorithm Water distribution network EPANET Optimal design Pipe diameter

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Limacon inspired artificial bee colony algorithm for numerical optimization

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EVOLUTIONARY INTELLIGENCE 2021年第3期14卷 1345-1353页

作者： Sharma, Kavita Gupta, P. C. Sharma, Nirmala Univ Kota Kota India Rajasthan Tech Univ Kota India

The artificial bee colony algorithm (ABCA) has established itself as a signature algorithm in the area of swarm intelligence based algorithms. The hybridization of the local search technique enhances the exploitation capability in the search process of the global optimization strategies. In this article, an effective local search technique that is designed by taking inspiration by Limacon curve, is incorporated in ABCA and the designed strategy is named Limacon inspired ABC (LABC) algorithm. The exploitation capability of the LABC strategy is tested over 18 complex benchmark optimization problems. The test results are compared with similar state-of-art algorithms and statistical analysis shows the LABC can be considered an effective variant of the ABC algorithms to solve the complex optimization problems.

关键词： artificial bee colony algorithm Swarm intelligence Limacon curve Local search Optimization

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