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Maximizing three-hop influence spread in social networks using discrete comprehensive learning artificial bee colony optimizer

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APPLIED SOFT COMPUTING 2019年 83卷 105606-000页

作者： Ma, Lianbo Liu, Ying Northeastern Univ Coll Software Shenyang 110819 Liaoning Peoples R China

Aiming at resolving the influence maximization (IM) problem in social networks, this paper proposes a three-layer-comprehensive-influence evaluation (TLCIE) model to measure the spread range of combinational nodes in the independent or weighted cascade models. The TLCIE is an enhanced three-hop influence spread model by integrating the intra-and inter-layer's propagation effect, which improves the accuracy of propagation simulation and the reliability of parameter estimation. Then, an adaptive discrete artificial bee colony algorithm (ADABC) is devised to resolve the TLCIE model efficiently. In ADABC, the comprehensive-learning guided (CLG) updating rules, the degree-improvement initialization method and the semi-abandonment scout bee strategy are incorporated to enhance the search ability. Finally, the proposed model and algorithm are tested on a set of real-world social network instances, and the experimental results validate their effectiveness and efficiency. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Social networks Influence maximization Three-hop discrete artificial bee colony algorithm

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discrete bee algorithms and their application in multivariable function optimization

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artificial INTELLIGENCE REVIEW 2011年第1期35卷 73-84页

作者： Salim, Mina Vakil-Baghmisheh, M. T. Univ Tabriz Fac Elect & Comp Engn ICT Res Ctr Tabriz Iran

In this paper we present four discrete versions of two different existing honey bee optimization algorithms: the discrete artificial bee colony algorithm (DABC) and three versions of the discrete fast marriage in honey bee optimization algorithm (DFMBO1, DFMBO2, and DFMBO3). In these discretized algorithms we have utilized three logical operators, i.e. OR, AND and XOR operators. Then we have compared performances of our algorithms and those of three other bee algorithms, i.e. the artificial bee colony (ABC), the queen bee (QB), and the fast marriage in honey bee optimization (FMBO) on four benchmark functions for various numbers of variables up to 100. The obtained results show that our discrete algorithms are faster than other algorithms. In general, when precision of answer and number of variables are low, the difference between our new algorithms and the other three algorithms is small in terms of speed, but by increasing precision of answer and number of variables, the needed number of function evaluations for other algorithms increases beyond manageable amounts, hence their success rates decrease. Among our proposed discrete algorithms, the DFMBO3 is always fast, and achieves a success rate of 100% on all benchmarks with an average number of function evaluations not more than 1010.

关键词： artificial bee colony algorithm discrete artificial bee colony algorithm Fast marriage in honey bee optimization discrete fast marriage in honey bee optimization

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An effective computational approach based on XFEM and a novel three-step detection algorithm for multiple complex flaw clusters

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COMPUTERS & STRUCTURES 2017年第Dec.期193卷 207-225页

作者： Ma, Chunping Yu, Tiantang Le Van Lich Tinh Quoc Bui Hohai Univ Dept Engn Mech Nanjing 211100 Jiangsu Peoples R China Kyoto Univ Dept Mech Engn & Sci Nishikyo Ku Kyoto 6158540 Japan Duy Tan Univ Inst Res & Dev Da Nang City Vietnam Tokyo Inst Technol Dept Civil & Environm Engn Meguro Ku 2-12-1-W8-22 Ookayama Tokyo 1528552 Japan

This paper presents an effective computational approach comprised of forward and inverse analyses for detection of multiple complex flaw clusters in elastic solids. A three-step detection strategy is introduced for inverse analysis, whereas extended finite element method (XFEM) is adopted for forward analysis. The use of XFEM is to avoid re-meshing during the change of flaw geometries. The three-step detection strategy involves: firstly, an optimization method that couples an improved discrete artificial bee colony algorithm and hierarchical clustering analysis (IDABC-HCA) is used to capture subdomains containing flaws with limited measure points in the global domain;secondly, additional measure points are introduced locally within each captured subdomain, where the number of flaws and the rough geometry of each flaw are quickly determined with the IDABC-HCA;finally, true geometries of flaws are obtained on the basis of the rough geometries by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. To save computational time, "Queue and Kill" method is proposed to actively identify and eliminate the improper candidate flaws and/or flaw clusters. Three numerical examples of multiple flaw detection that include simple and complex flaw geometries are analyzed. The results demonstrate that the proposed approach can effectively detect multiple complex flaw clusters without prior information of the flaw number. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Flaw clusters Inverse problem XFEM discrete artificial bee colony algorithm Hierarchical clustering analysis BFGS

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COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC

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IEEE ACCESS 2020年 8卷 65116-65126页

作者： Wan, Lujun Zhong, Yun Li, Wen Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Peoples R China CETC 28 State Key Lab Air Traff Management Syst & Techn Nanjing 210007 Peoples R China PLA Unit 94062 Korla 841000 Peoples R China Air Force Engn Univ Equipment Management & UAV Engn Coll Xian 710051 Peoples R China PLA Unit 95801 Beijing 100076 Peoples R China

Aiming at the selection problem for course of action (COA) of aviation swarm, this paper proposes an optimized selection method for COA of aviation swarm based on dynamic influence nets (DINs), and discrete artificial bee colony (DABC) algorithm. Firstly, based on the basic concept of the aviation swarm combat plan, static and dynamic modeling and analysis are performed, respectively. Then, the probability propagation mechanism of DINs, which mainly includes key parameter determination and probability propagation algorithm, is established. Subsequently, based on the analysis of the evaluation index, the model is solved by using DABC algorithm with real number coding. Finally, this paper takes the offshore island attack task as an example, and carries out multiple sets of simulation cases to compare DABC algorithm with discrete glowworm swarm optimization (DGSO) algorighm and discrete particle swarm optimization (DPSO) algorithm, through all these cases, the rationality of the model, and the effectiveness and superiority of the algorithm are verified.

关键词： Heuristic algorithms Analytical models Task analysis Cognition Mathematical model Benchmark testing Programmable logic arrays Aviation swarm course of action dynamic influence nets discrete artificial bee colony algorithm probability propagation mechanism

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Material Flow Optimization of a Machine Loading Problem 1

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4th International Conference on Advanced Engineering Optimization Through Intelligent Techniques

作者： Bhosale, K. C. Pawar, P. J. Savitribai Phule Pune Univ Sanjivani Coll Engn Dept Mech Engn Pune Maharashtra India Savitribai Phule Pune Univ KK Wagh Inst Engn Educ & Res Dept Robot & Automat Pune Maharashtra India

ISBN: (数字)9789819746545

ISBN: (纸本)9789819796618;9789819746545;9789819746538

In the machine loading problem, job selection problem and an operation allocation problem are considered. The different jobs to be manufactured are selected and their sequence of operations is carried out in this machine loading problem. Thus, machine loading problem is a crucial problem in the flexible manufacturing industry. In this paper, a mathematical model is prepared with both continuous and zero-one variables for job selection and operation allocation problems. The objective function is considered to minimize the material flow time which comprises processing time and waiting time. The capacity of the machine is considered as constraint for this problem. A case study problem of Far et al. (Sci Iranica 25 (2017), [1]) is considered in this paper. A discrete artificial bee colony algorithm is then proposed to solve the machine loading problem. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.

关键词： Machine loading problem discrete artificial bee colony algorithm Material flow Waiting time

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Feature selection of Complex Power Quality Disturbances and Parameter Optimization of Random Forest 4

Feature selection of Complex Power Quality Disturbances and ...

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4th International Conference on Intelligent Green Building and Smart Grid (IGBSG)

作者： Wang, Renming Wang, Hongyang Wang, Lingyun China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China

ISBN: (纸本)9781728121482

A hybrid method based on modified discrete artificial bee colony algorithm(MDABC) for power quality disturbance(PQD) signal feature selection and parameter optimization of random forest(RF) is proposed. Firstly, time-frequency features of the complex power quality disturbance signal are extracted using s-transform(ST) to form an original feature set;Then, by using default parameters of RF, the out-of-bag(OOB) Permutation test value of each feature in the original feature set is calculated as the feature weight, and the features of the training and verification data set are rearranged in descending order accordingly. Finally, taking the generalization error of RF as the objective function, the forest scale: nTree, the number of input features:Ni and the node feature subset size:q are optimized using MDABC to determine the optimal parameters of RF and the optimal feature set. It can be seen from the experiment that compared with the RF classifier before optimization, the accuracy of the MDABC-RF classifier in the classification of 16 and 19 complex PQD signals is better, and its operating efficiency is greatly improved.

关键词： power quality discrete artificial bee colony algorithm random forest parameter optimization feature selection

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Facial Recognition Using discrete ABC 5

Facial Recognition Using Discrete ABC

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Kallianpur, Akshay K. Kalyani, Aditya Naligay, Subramanya E. Kallimani, Jagadish S. MS Ramaiah Inst Technol Bangalore Karnataka India

ISBN: (纸本)9781509020287

Facial recognition is a topic of interest for research as it has room for improvement in the accuracy of the recognition rate. To achieve this, either the recognition algorithm is modified or more efficient pre-processing techniques are used. This paper proposes a novel and optimized artificial bee colony (ABC) algorithm, to perform facial recognition. Although the database being used here is Labeled Faces in the Wild (LFW), it is also tested on Carnigie Melon database to ensure consistent results. Applying the concept to perform facial recognition and obtaining satisfactory results is sublime. The discretization of the ABC algorithm serves many applications in the fields of pattern recognition and image analysis. This version of the ABC algorithm contains certain elements from Particle Swarm Optimization (PSO), hence yielding a hybrid algorithm that contains the best of both its contributors. This paper primarily focuses on the application of the proposed technique onto facial recognition. Standard data sets are used to test and quantify the efficacy of the algorithm. Given that the requirement is an extremely swift pattern recognition software that does not compromising the efficiency of the recognition rate, the proposed algorithm upholds both these criteria and is a robust technique.

关键词： discrete artificial bee colony algorithm Gaussian Filters Laplacian Filters Feature Extraction discrete Cosine Transform

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CA Joint Resource Allocation algorithm Based on QoE Weight

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第5期12卷 2233-2252页

作者： Liu Jun-Xia Jia Zhen-Hong Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Xinjiang Peoples R China Xinjiang Inst Engn Dept Elect & Informat Urumqi 830023 Xinjiang Peoples R China

For the problem of cross-layer joint resource allocation (JRA) in the Long-Term Evolution (LTE)-Advanced standard using carrier aggregation (CA) technology, it is difficult to obtain the optimal resource allocation scheme. This paper proposes a joint resource allocation algorithm based on the weights of user's average quality of experience (JRA-WQOE). In contrast to prevalent algorithms, the proposed method can satisfy the carrier aggregation abilities of different users and consider user fairness. An optimization model is established by considering the user quality of experience (QoE) with the aim of maximizing the total user rate. In this model, user QoE is quantified by the mean opinion score (MOS) model, where the average MOS value of users is defined as the weight factor of the optimization model. The JRA-WQOE algorithm consists of the iteration of two algorithms, a component carrier (CC) and resource block (RB) allocation algorithm called DABC-CCRBA and a subgradient power allocation algorithm called SPA. The former is used to dynamically allocate CC and RB for users with different carrier aggregation capacities, and the latter, which is based on the Lagrangian dual method, is used to optimize the power allocation process. Simulation results showed that the proposed JRA-WQOE algorithm has low computational complexity and fast convergence. Compared with existing algorithms, it affords obvious advantages such as improving the average throughput and fairness to users. With varying numbers of users and signal-to-noise ratios (SNRs), the proposed algorithm achieved higher average QoE values than prevalent algorithms.

关键词： LTE-Advanced carrier aggregation resource allocation quality of experience discrete artificial bee colony algorithm

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Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2021年 97卷 104016-104016页

作者： Huang, Jiang-Ping Pan, Quan-Ke Miao, Zhong-Hua Gao, Liang Shanghai Univ Sch Mech Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The distributed permutation flowshop scheduling problem (DPFSP) has been a hot issue in recent years. Due to the practical relevance of sequence-dependent setup time (SDST), we consider the DPFSP with SDST to minimize makespan. For the purpose, we propose three constructive heuristics and an effective discrete artificial bee colony (DABC) algorithm. All the heuristics are based on a greedy assignment rule and a local search of job blocks in each factory. In the local search, three different setup times are respectively considered for inserting a job block. In the DABC, to balance the local exploitation and the global exploration, we propose six composite neighborhood operators according to the problem characteristics. The first three are based on insertion and swap operators, and the second three have a close relationship with the critical factory. A problem-oriented local search method is developed to improve the best individual in the population. A comprehensive computational campaign against the closely related and state-of-the-art algorithms in the literature shows that both the proposed heuristics and DABC are very effective for solving the problem under consideration.

关键词： Flowshop Scheduling Heuristics discrete artificial bee colony algorithm Meta-heuristics

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Facial Recognition Using discrete ABC

Facial Recognition Using Discrete ABC

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International Conference on Advances in Computing, Communications and Informatics

作者： Akshay K. Kallianpur Aditya Kalyani Subramanya E. Naligay Jagadish S. Kallimani M.S. Ramaiah Institute of Technology Bangalore Karnataka India

ISBN: (纸本)9781509020300

关键词： discrete artificial bee colony algorithm Gaussian Filters Laplacian Filters Feature Extraction discrete Cosine Transform

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