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A two-stage approach for multicast-oriented virtual network function placement

APPLIED SOFT COMPUTING 2021年 112卷 107798-107798页

作者： Wang, Xinhan Xing, Huanlai Zhan, Dawei Luo, Shouxi Dai, Penglin Iqbal, Muhammad Azhar Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Peoples R China

Network function virtualization (NFV) is an emerging network paradigm that decouples softwarized network functions from proprietary hardware. Nowadays, resource allocation has become one of the hot topics in the NFV domain. In this paper, we formulate a service function chain (SFC) mapping problem in the context of multicast, which is also referred to as the multicast-oriented virtual network function placement (MVNFP) problem. The objective function considers end-to-end delay as well as compute resource consumption, with bandwidth requirements met. A two-stage approach is proposed to address this problem. In the first stage, Dijkstra's algorithm is adopted to construct a multicast tree. In the second stage, a novel estimation of distribution algorithm (nEDA) is developed to map a given SFC over the multicast tree. Simulation results show that the proposed two-stage approach outperforms a number of state-of-the-art evolutionary, approximation, and heuristic algorithms, in terms of the solution quality. (C) 2021 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithm Multicast Network function virtualization Virtual network function placement

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A Novel Imperialist Competitive algorithm With Multi-Elite Individuals Guidance for Multi-Object Unrelated Parallel Machine Scheduling Problem

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IEEE ACCESS 2019年 7卷 121223-121235页

作者： Wang, Mei Pan, Guohua Yantai Vocat Coll Lab Image Proc & Pattern Recognit Yantai 264670 Peoples R China Yantai Publ Secur Bur Yantai 264670 Peoples R China

In this study unrelated parallel machine scheduling problem (UPMSP) with preventive maintenance (PM) and sequence dependent setup times (SDST) is investigated. A novel imperialist competitive algorithm (NICA) with multi-elite individuals guidance is proposed to minimize makespan and total tardiness simultaneously. Initialization is done by two heuristics, each of which is built based on one objective. Multielite individuals guidance strategy is added in assimilation that colonies can move toward other imperialists, diversified strategies such as local search and estimation of distribution algorithm (EDA) are adopted based on solution quality in revolution and EDA is also used in imperialist competition. Empire aggression is added by local search of imperialist for plundering a randomly chosen colony. A number of experiments are conducted on the impact of new strategies and the comparisons among NICA and other algorithms. Computational results demonstrate the effectiveness and advantages of NICA in solving UPSMP with PM and SDST.

关键词： Preventive maintenance setup times imperialist competitive algorithm multi-elite individual guidance estimation of distribution algorithm

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Dynamic Bandwidth Scheduling of Software Defined Networked Collaborative Control System

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IEEE ACCESS 2020年 8卷 75968-75975页

作者： Yan, Xiang Sun, Yanfei Mei, Bizhou Zhejiang Wanli Univ Sch Informat & Intelligence Engn Ningbo 315000 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Zhejiang Yi Duan Machinery Co Ningbo 315000 Peoples R China

From the perspective of collaborative design of control and scheduling, a software-defined networked collaborative control system (SD-NCCS) is established. On this basis, the system performance of SD-NCCS is analyzed and the average sensitivities of reference are used in utility function to evaluate the effects of network-induced delays. Moreover, the network pricing mechanism and game theory are introduced and a dynamic bandwidth resource allocation model of the overall control system for optimal performance is obtained. Thus, the problem of the network resource allocation of the SD-NCCS has been converted to be the problem of solving the Nash equilibrium point under the non-cooperative game model. Furthermore, the Nash equilibrium solution under this frame is obtained using the estimation distributed algorithm (EDA). Finally, a simple example is included to illustrate the performance of our scheme.

关键词： estimation of distribution algorithm game theory network pricing software-defined network

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Two-level evolutionary approach to the survivable mesh-based transport network topological design

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JOURNAL OF HEURISTICS 2010年第5期16卷 723-744页

作者： Sun, J. Zhang, Q. Li, J. Univ Nottingham CPIB Sch Biosci Loughborough LE12 5RD England Univ Essex Dept Comp Sci Wivenhoe Pk CO4 3SQ Essex England Unilever Safety & Environm Assurance Ctr Sharnbrook MK44 1LQ Beds England

The complete topology design problem of survivable mesh-based transport networks is to address simultaneously design of network topology, working path routing, and spare capacity allocation based on span-restoration. Each constituent problem in the complete design problem could be formulated as an Integer Programming (IP) and is proved to be NP-hard. Due to a large amount of decision variables and constraints involved in the IP formulation, to solve the problem directly by exact algorithms (e. g. branch-and-bound) would be impractical if not impossible. In this paper, we present a two-level evolutionary approach to address the complete topology design problem. In the low-level, two parameterized greedy heuristics are developed to jointly construct feasible solutions (i.e., closed graph topologies satisfying all the mesh-based network survivable constraints) of the complete problem. Unlike existing "zoom-in"-based heuristics in which subsets of the constraints are considered, the proposed heuristics take all constraints into account. An estimation of distribution algorithm works on the top of the heuristics to tune the control parameters. As a result, optimal solution to the considered problem is more likely to be constructed from the heuristics with the optimal control parameters. The proposed algorithm is evaluated experimentally in comparison with the latest heuristics based on the IP software CPLEX, and the "zoom-in"-based approach on 28 test networks problems. The experimental results demonstrate that the proposed algorithm is more effective in finding high-quality topologies than the IP-based heuristic algorithm in 21 out of 28 test instances with much less computational costs, and performs significantly better than the "zoom-in"-based approach in 19 instances with the same computational costs.

关键词： Survivable mesh-based network Network topology design Heuristics Evolutionary algorithm estimation of distribution algorithm

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An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups

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KNOWLEDGE-BASED SYSTEMS 2019年 173卷 83-112页

作者： Li, Z. C. Qian, B. Hu, R. Chang, L. L. Yang, J. B. Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China High Tech Inst Xian Xian 710025 Shaanxi Peoples R China Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England

In this paper, an elitist nondominated sorting hybrid algorithm, namely ENSHA, is proposed to solve the multi-objective flexible job-shop scheduling problem (MOFJSSP) with sequence-dependent setup times/costs (MOFJSSP_SDST/C). The objectives to be minimized are the maximal completion time (i.e., makespan) and the total setup costs (TSC). The makespan is an efficiency-focused objective whereas the TSC is an economic focused one. Existing works mainly consider the efficiency-focused multiple criteria. The main highlights of this paper are threefold, i.e., the operation-based sequence model, the problem-dependent job assignment rules and the novel evolutionary framework of ENSHA. For the operation-based sequence model, this is the first time that the sequence model of MOFJSSPs has been proposed and the TSC has been treated as an independent objective in MOFJSSPs. For the job assignment rules, the solution representation is first proposed, and then three job assignment rules are specifically designed to decode solutions or sequences into feasible scheduling schemes. For the novel evolutionary framework, it works with two populations, i.e., the main population (MP) and the auxiliary population (AP). First, ENSHA adopts the elitist nondominated sorting method for evolving MP to maintain high-quality solutions regarding both the convergence and diversity. Next, a machine learning strategy based on the estimation of distribution algorithm (EDA) is proposed to learn the valuable information from nondominated solutions in MP for building a probabilistic model. This model is then used to generate the offspring of AP. Furthermore, a simple yet effective cooperation-based refinement mechanism is raised to combine MP and AP, so as to generate MP of the next generation. Finally, experimental results on 39 benchmark instances and a real-life case study demonstrate the effectiveness and application values of the proposed ENSHA. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Nondominated sorting method estimation of distribution algorithm Flexible job shop scheduling problem Sequence-dependent setups

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An exploratory research of elitist probability schema and its applications in evolutionary algorithms

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APPLIED INTELLIGENCE 2014年第4期40卷 695-709页

作者： Zhang, Hong-Guang Liu, Yuan-An Tang, Bi-Hua Liu, Kai-Ming Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100088 Peoples R China

An important problem in the study of evolutionary algorithms is how to continuously predict promising solutions while simultaneously escaping from local optima. In this paper, we propose an elitist probability schema (EPS) for the first time, to the best of our knowledge. Our schema is an index of binary strings that expresses the similarity of an elitist population at every string position. EPS expresses the accumulative effect of fitness selection with respect to the coding similarity of the population. For each generation, EPS can quantify the coding similarity of the population objectively and quickly. One of our key innovations is that EPS can continuously predict promising solutions while simultaneously escaping from local optima in most cases. To demonstrate the abilities of the EPS, we designed an elitist probability schema genetic algorithm and an elitist probability schema compact genetic algorithm. These algorithms are estimations of distribution algorithms (EDAs). We provided a fair comparison with the persistent elitist compact genetic algorithm (PeCGA), quantum-inspired evolutionary algorithm (QEA), and particle swarm optimization (PSO) for the 0-1 knapsack problem. The proposed algorithms converged quicker than PeCGA, QEA, and PSO, especially for the large knapsack problem. Furthermore, the computation time of the proposed algorithms was less than some EDAs that are based on building explicit probability models, and was approximately the same as QEA and PSO. This is acceptable for evolutionary algorithms, and satisfactory for EDAs. The proposed algorithms are successful with respect to convergence performance and computation time, which implies that EPS is satisfactory.

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Modelling and solving a cost-oriented resource-constrained multi-model assembly line balancing problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2018年第11期56卷 3994-4016页

作者： Pereira, Jordi Univ Adolfo Ibanez Fac Engn & Sci Vina Del Mar Chile

A line balancing problem considers the assignment of operations to workstations in an assembly line. While assembly lines are usually associated to mass production of standardised goods, their advantages have led to their widespread use whenever a product-oriented production system is applicable and the benefits of the labour division and specialisation are significant, even when some of its characteristics may deviate from classical assembly lines. In this work, we study a line balancing problem found in the textile industry in which the line must be balanced for multiple types of goods taking into account resource requirements. In order to solve the problem, a hybrid method that combines classical methods for line balancing with an estimation of distribution algorithm is proposed. Computational experiments show that the new procedure improves upon the state of the art when compared using a benchmark set derived from the literature, as well as when compared using data from the manufacturer that originated this research work.

关键词： assembly line balancing combinatorial optimisation lower bound heuristics estimation of distribution algorithm

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A hybrid heuristic for dominating tree problem

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SOFT COMPUTING 2016年第1期20卷 377-397页

作者： Chaurasia, Sachchida Nand Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

Given an undirected, connected, edge-weighted graph, the dominating tree problem (DTP) seeks on this graph a tree of minimum weight such that each node of the graph either belongs to the tree or is adjacent to a node in the tree. This problem is -hard. In this paper, we present an evolutionary algorithm with guided mutation (EA/G) to solve the DTP. This problem has several practical applications in the field of wireless sensor networks. EA/G is a recently proposed evolutionary algorithm that tries to overcome the shortcomings of genetic algorithms (GAs) and estimation of distribution algorithms both, and has the characteristics of both. We have compared the performance of our proposed approach with the state-of-the-art approaches presented in the literature. Computational results show the superiority of our approach in terms of solution quality as well as execution time.

关键词： Constrained optimization Dominating tree estimation of distribution algorithm Guided mutation Heuristic

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Simulation optimization for the vehicle routing problem with time windows using a Bayesian network as a probability model

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期85卷 2505-2523页

作者： Perez-Rodriguez, Ricardo Hernandez-Aguirre, Arturo CIMAT AC Callejon Jalisco S-N Guanajuato 36240 Mexico

The main purpose of the vehicle routing problem (VRP) is to deliver a set of customers with known demands on minimum-travel routes and terminating at the same depot. The vehicle routing problem with time windows (VRPTW) requires the delivery made in a specific time window for every customer and returning to the depot before a due time. Contrary to current research, an estimation of distribution-algorithm-based approach coupled with a simulation model is proposed and developed to solve the problem and implement the solution. The approach mentioned makes use of a Bayesian network as a probability model to describe the distribution of the solution space. Furthermore, the approach taken in this study combines the key advantages of both estimation of distribution algorithms (EDA) and simulation. The simulation is used to model the VRPTW environment, while the EDA is used to guide the overall search process to identify the best performing ones. Solomon's (Oper Res 35:254-265, 1987) instances served as input and test parameters in order to show that there exists a relationship and interaction between vertices and positions on the sequence of the VRPTW solution. A better position for each vertex on the sequence can be estimated through a Bayesian network. Experimental results show that the EDA performance was better in 70 % of the cases, as average, for the number of vehicles used in all the trails with respect the other algorithms proposed as a benchmark for comparison with the EDA scheme.

关键词： estimation of distribution algorithm Vehicle routing problem Bayesian network Simulation optimization

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Feature Subset Selection by Bayesian network-based optimization

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ARTIFICIAL INTELLIGENCE 2000年第1-2期123卷 157-184页

作者： Inza, I Larrañaga, P Etxeberria, R Sierra, B Univ Basque Country Dept Comp Sci & Artificial Intelligence E-20080 San Sebastian Basque Country Spain

A new method for Feature Subset Selection in machine learning, FSS-EBNA (Feature Subset Selection by estimation of Bayesian Network algorithm), is presented. FSS-EBNA is an evolutionary, population-based, randomized search algorithm, and it can be executed when domain knowledge is not available. A wrapper approach, over Naive-Bayes and ID3 learning algorithms, is used to evaluate the goodness of each visited solution. FSS-EBNA, based on the EDA (estimation of distribution algorithm) paradigm, avoids the use of crossover and mutation operators to evolve the populations, in contrast to Genetic algorithms. In absence of these operators, the evolution is guaranteed by the factorization of the probability distribution of the best solutions found in a generation of the search. This factorization is carried out by means of Bayesian networks. Promising results are achieved in a variety of tasks where domain knowledge is not available. The paper explains the main ideas of Feature Subset Selection, estimation of distribution algorithm and Bayesian networks, presenting related work about each concept. A study about the 'overfitting' problem in the Feature Subset Selection process is carried out, obtaining a basis to define the stopping criteria of the new algorithm. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： machine learning supervised learning Feature Subset Selection wrapper predictive accuracy estimation of distribution algorithm estimation of Bayesian Network algorithm Bayesian network overfitting

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