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Modified Gbest-guided artificial bee colony algorithm with new probability model

SOFT COMPUTING 2018年第7期22卷 2217-2243页

作者： Cui, Laizhong Zhang, Kai Li, Genghui Fu, Xianghua Wen, Zhenkun Lu, Nan Lu, Jian Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China

artificial bee colony (ABC) is a very effective and efficient swarm-based intelligence optimization algorithm, which simulates the collective foraging behavior of the honey bees. However, ABC has strong exploration ability but poor exploitation ability because its solution search equation performs well in exploration but badly in exploitation. In order to enhance the exploitation ability and obtain a better balance between exploitation and exploration, in this paper, a novel search strategy which exploits the valuable information of the current best solution and a novel probability model which makes full use of the other good solutions on onlooker bee phase are proposed. To be specific, in the novel search strategy, a parameter P is used to control which search equation to be used, the original search equation of ABC or the new proposed search equation. The new proposed search equation utilizes the useful information from the current best solution. In the novel probability model, the selected probability of the good solution is absolutely significantly larger than that of the bad solution, which makes sure the good solutions can attract more onlooker bees to search. We put forward a new ABC variant, named MPGABC by combining the novel search strategy and probability model with the basic framework of ABC. Through the comparison of MPGABC and some other state-of-the-art ABC variants on 22 benchmark functions, 22 CEC2011 real-world optimization problems and 28 CEC2013 real-parameter optimization problems, the experimental results show that MPGABC is better than or at least comparable to the competitors on most of benchmark functions and real-world problems.

关键词： artificial bee colony algorithm Search strategy Probability model Global optimization

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RA-OABC: An Optimal Framework for Resource Assignment in WCDMA Networks Using Oppositional artificial bee colony algorithm with Repair Strategies

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期103卷 1535-1562页

作者： Alam, Safikul S. Chandra, Sourabh Bhattacharya, Swapan Calcutta Inst Technol Dept Comp Sci & Engn Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

In this paper, we propose a new optimal framework with repair strategy to resolve major issues in the assignment of resources to mobile terminals (plainly to users) in WCDMA networks. The main goal of this proposed approach is to tackle the issues in the assignment of resources such as (1) load factor, (2) total capacity, (3) power, (4) channelization codes and (5) user's not including any of the service. Our proposed RA-OABC approach adopts the repair strategy for the replacement of resource which violates the weight factor condition (i.e. phi 1). The resource replacement is practiced just by removing the unfeasible resource and substituting it with new feasible resource (repaired versions). The oppositional artificial bee colony (OABC) algorithm acts well on the groups (assignment of users to the base stations) in a very efficient manner. Moreover, our proposed approach also exhibits a superior performance than that of the conventional technique by means of users receiving the requested services with increased availability of resources. The experimental study for the proposed RA-OABC approach is effectively carried out on various scenarios: the different number of users, user's profiles, effects on the distributions of different number of user's and so on.

关键词： artificial bee colony algorithm WCDMA Resource assignment Oppositional ABC Mobile terminals Repair strategy

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Robust Optimization of Signal Control Parameters for Unsaturated Intersection Based on Tabu Search-artificial bee colony algorithm

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IEEE ACCESS 2018年 6卷 32015-32022页

作者： Hao, Wei Ma, Changxi Moghimi, Bahman Fan, Yuanyuan Gao, Zhibo Changsha Univ Sci & Technol Hunan Key Lab Smart Roadway & Cooperat Vehicle In Changsha 410205 Hunan Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China CUNY City Coll Dept Civil Engn New York NY 10031 USA Changsha Univ Sci & Technol Sch Traff & Transportat Engn Changsha 410205 Hunan Peoples R China

In order to overcome the drawback of the conventional signal timing optimization, a robust optimization algorithm for signal control parameters based on Tabu search-artificial bee colony algorithm is proposed under unsaturated flow condition. Based on the analysis of the characteristics of traffic signal control, a robust optimization model of signal control parameters is constructed by considering the minimum the average delay and the mean square error of average delay. As a consequence, the formation process of the initial solution to the bee colony is improved and the robust optimization model is solved by using the Tabu search-artificial bee colony algorithm. The proposed robust optimization model is validated by using an intersection in Zhangye City of China. The simulation results have shown that the robust optimization model and the algorithm are feasible and practicable. This robust model and algorithm can effectively deal with the volatility of traffic flow and reduce traffic delays.

关键词： Intelligent transportation systems traffic control artificial bee colony algorithm robust optimal solution

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A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems

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KNOWLEDGE-BASED SYSTEMS 2018年第May 15期148卷 115-130页

作者： Gong, Dunwei Han, Yuyan Sun, Jianyong China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China

A blocking lot-streaming flow shop (BLSFS) scheduling problem is to schedule a number of jobs on more than one machine, where each job is split into a number of sublots while no intermediate buffers exist between adjacent machines. The BLSFS scheduling problem roots from traditional job shop scheduling problems but with additional constraints. It is more difficult to be solved than traditional job shop scheduling problems, yet very popular in real-world applications, and research on the problem has been in its infancy to date. This paper presents a hybrid multi-objective discrete artificial bee colony (HDABC) algorithm for the BLSFS scheduling problem with two conflicting criteria: the makespan and the earliness time. The main contributions of this paper include: (1) developing an initialization approach using a prior knowledge which can produce a number of promising solutions, (2) proposing two crossover operators by taking advantage of valuable information extracted from all the non-dominated solutions in the current population, and (3) presenting an efficient Pareto local search operator based on the Pareto dominance relation. The proposed algorithm is empirically compared with four state-of-the-art multi-objective evolutionary algorithms on 18 test subsets of the BLSFS scheduling problem. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of several widely-used performance metrics. (C) 2018 Published by Elsevier B.V.

关键词： Scheduling Blocking lot-streaming flow shop Multi-objective optimization artificial bee colony algorithm Pareto local search

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Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the artificial bee colony algorithm and a Back-propagation Neural Network with Double Hidden Layers

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WATER 2019年第4期11卷 860-860页

作者： Li, Huanhuan Lu, Yudong Zheng, Ce Yang, Mi Li, Shuangli Changan Univ Sch Environm Sci & Engn Minist Educ Key Lab Subsurface Hydrol & Ecol Effects Arid Reg Xian 710054 Shaanxi Peoples R China Shaanxi Yining Construct Engn Co Ltd Xian 710065 Shaanxi Peoples R China Yanbian Univ Sch Sci Yanji 133200 Peoples R China

Groundwater is crucial for economic and agricultural development, particularly in arid areas where surface water resources are extremely scarce. The prediction of groundwater levels is essential for understanding groundwater dynamics and providing scientific guidance for the rational utilization of groundwater resources. A back propagation (BP) neural network based on the artificial bee colony (ABC) optimization algorithm was established in this study to accurately predict groundwater levels in the overexploited arid areas of Northwest China. Recharge, exploitation, rainfall, and evaporation were used as input factors, whereas groundwater level was used as the output factor. Results showed that the fitting accuracy, convergence rate, and stabilization of the ABC-BP model are better than those of the particle swarm optimization (PSO-BP), genetic algorithm (GA-BP), and BP models, thereby proving that the ABC-BP model can be a new method for predicting groundwater levels. The ABC-BP model with double hidden layers and a topology structure of 4-7-3-1, which overcame the overfitting problem, was developed to predict groundwater levels in Yaoba Oasis from 2019 to 2030. The prediction results of different mining regimes showed that the groundwater level in the study area will gradually decrease as exploitation quantity increases and then undergo a decline stage given the existing mining condition of 40 million m(3)/year. According to the simulation results under different scenarios, the most appropriate amount of groundwater exploitation should be maintained at 31 million m(3)/year to promote the sustainable development of groundwater resources in Yaoba Oasis.

关键词： artificial bee colony algorithm double hidden layers back-propagation neural network groundwater level prediction arid oasis

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Variable selection in gamma regression models via artificial bee colony algorithm

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JOURNAL OF APPLIED STATISTICS 2018年第1期45卷 8-16页

作者： Dunder, Emre Gumustekin, Serpil Cengiz, Mehmet Ali Univ Ondokuz Mayis Dept Stat Samsun Turkey

Variable selection is an important task in regression analysis. Performance of the statistical model highly depends on the determination of the subset of predictors. There are several methods to select most relevant variables to construct a good model. However in practice, the dependent variable may have positive continuous values and not normally distributed. In such situations, gamma distribution is more suitable than normal for building a regression model. This paper introduces an heuristic approach to perform variable selection using artificial bee colony optimization for gamma regression models. We evaluated the proposed method against with classical selection methods such as backward and stepwise. Both simulation studies and real data set examples proved the accuracy of our selection procedure.

关键词： Variable selection gamma regression analysis heuristic optimization artificial bee colony algorithm R-project

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Optimum design of tied-arch bridges under code requirements using enhanced artificial bee colony algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2019年 135卷 102685-000页

作者： Latif, M. A. Saka, M. P. Univ Bahrain Dept Civil Engn POB 32038 Isa Town Bahrain

In this study, optimum design algorithm is presented for tied-arch bridges under AASHTO-LRFD Bridge Design Specifications provisions. It is decided that in tied-arch bridges ties, arch ribs, and bottom and top bracings are made of built-up box sections, whereas built-up I sections are utilized for floor beams and stringers. Bars are adopted for hangers. In the formulation of the optimization problem, design variables are selected as the cross sectional dimensions of steel plates not that of I and box sections. Design pools are prepared for steel plate sections in addition to the hanger bars so that the optimization algorithm can select appropriate steel plates, construct I and box built-up sections for members of 3-D tied-arch such that the weight of the bridge is minimized. In addition to design code requirements, geometrical constraints among its elements that are required for manufacturability of the bridge are also considered. The design process of tied-arch bridges differs from that of steel framed structures. It necessitates consideration of moving vehicle loads. Design optimization algorithms require the response of bridges under several design load arrangements and in the construction of influence lines. This is achieved by using open application programming interface (OAPI) facility of SAP2000. The solution of discrete nonlinear programming problem is obtained by using the proposed Enhanced artificial bee colony algorithm (eABC). The proposed algorithm is compared with Standard artificial bee colony (ABC) and Exponential Big Bang-Big Crunch (eBB-BC) algorithms to evaluate its performance.

关键词： Tied-arch bridges Built-up sections AASHTO-LRFD provisions Optimum structural design Metaheuristic algorithms artificial bee colony algorithm Big bang-big crunch algorithm

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Post-Disaster Unmanned Aerial Vehicle Base Station Deployment Method Based on artificial bee colony algorithm

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IEEE ACCESS 2019年 7卷 168327-168336页

作者： Li, Jialiuyuan Lu, Dianjie Zhang, Guijuan Tian, Jie Pang, Yawei Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Shandong Prov Key Lab Novel Distributed Comp Soft Jinan 250358 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China

Ground-based communication facilities are at risk of being destroyed after natural disasters, such as earthquakes, floods, tsunamis, hurricanes, fires, or terrorist attacks. Unmanned aerial vehicles (UAV) can be used as air base stations to support user equipment (UE). UAV base station (UAV-BS) development plays a major part in rescue operations and post-disaster reconstruction. Improving network throughput in the UAV-BS signal coverage area and reducing deployment costs while maintaining effective communication are important issues that need to be addressed. In view of the problem, this paper demonstrates the problem of maximizing network throughput under the constraint of UAV-BS capacity by deploying UAV-BS. We proposed a UAV-artificial bee colony (U-ABC) algorithm to solve the problem of UAV-BS deployment. U-ABC algorithm can calculate the optimal flight position of each UAV-BS and maximum network throughput in the disaster area. In performance evaluation, we compared U-ABC algorithm with genetic algorithm, Greedy-ABC algorithm, PSO algorithm, DI-PSO algorithm and PSO-GWO algorithm. We analyzed the flight height altitude of UAV-BSs, the interference factor of UAV-BS, and the influence of the number of UE on network throughput. Results show that the proposed method improved the overall network throughput and achieved a high UE coverage rate under a given number of UAV-BSs.

关键词： artificial bee colony algorithm network throughput post-disaster wireless communication UAV base station

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DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles

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SAUDI JOURNAL OF BIOLOGICAL SCIENCES 2018年第5期25卷 932-946页

作者： Alshamlan, Hala M. King Saud Univ Informat Technol Dept Riyadh Saudi Arabia MIT Dept Mech Engn Cambridge MA 02139 USA

In the medical domain, it is very significant to develop a rule-based classification model. This is because it has the ability to produce a comprehensible and understandable model that accounts for the predictions. Moreover, it is desirable to know not only the classification decisions but also what leads to these decisions. In this paper, we propose a novel dynamic quantitative rule-based classification model, namely DQB, which integrates quantitative association rule mining and the artificial bee colony (ABC) algorithm to provide users with more convenience in terms of understandability and interpretability via an accurate class quantitative association rule-based classifier model. As far as we know, this is the first attempt to apply the ABC algorithm in mining for quantitative rule-based classifier models. In addition, this is the first attempt to use quantitative rule-based classification models for classifying microarray gene expression profiles. Also, in this research we developed a new dynamic local search strategy named DLS, which is improved the local search for artificial bee colony (ABC) algorithm. The performance of the proposed model has been compared with well-known quantitative-based classification methods and bioinspired meta-heuristic classification algorithms, using six gene expression profiles for binary and multi-class cancer datasets. From the results, it can be concludes that a considerable increase in classification accuracy is obtained for the DQB when compared to other available algorithms in the literature, and it is able to provide an interpretable model for biologists. This confirms the significance of the proposed algorithm in the constructing a classifier rule-based model, and accordingly proofs that these rules obtain a highly qualified and meaningful knowledge extracted from the training set, where all subset of quantitive rules report close to 100% classification accuracy with a minimum number of genes. It is remarkable that apparen

关键词： Classification rule artificial bee colony algorithm Quantitive rule-based classification model Microarray Gene expression profile ABC Cancer gene selection

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An artificial bee colony algorithm for the Distributed Hybrid Flowshop Scheduling Problem

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Procedia Manufacturing 2019年 39卷 1158-1166页

作者： Yingli Li Fan Li Quan-Ke Pan Liang Gao M. Fatih Tasgetiren State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan P.R. China School of Mechatronic Engineering and Automation Shanghai University Shanghai Wuhan P.R. China Mechanical and Industrial Engineering Department Qatar University Doha Qatar

The distributed hybrid flowshop scheduling problem (DHFSP) is derived from the hybrid flowshop scheduling problem (HFSP), which involves two scheduling assignment decisions—assignment of jobs to factories and scheduling jobs in each factory. DHFSP is encountered in many practical industry applications recently. The standard DHFSP has never been discussed in any previous literature, and it is addressed first addressed in this work. In this paper, the mixed-integer linear programming (MILP) model is proposed. An improved discrete artificial bee colony (DABC) algorithm for minimizing makespan is proposed as well. Novel encoding and decoding methods are developed for DHFSP. The constructive heuristic, an improved NEH heuristic, is employed to build high quality and diversified solutions for the initial population. Two neighborhood search methods based on swap and insertion neighborhood structures are proposed for DHFSP. Comprehensive computational comparisons and statistical analysis demonstrate the effectiveness of the proposed DABC algorithm when compared with a genetic algorithm (GA) and an iterated greedy (IG) algorithm.

关键词： flexible scheduling distributed hybrid flowshop artificial bee colony algorithm makespan heuristic optimization meta-heuristics

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