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Mapping of time-consuming multitask applications on a cloud system by multiobjective Differential Evolution

PARALLEL COMPUTING 2015年 48卷 40-58页

作者： De Falco, Ivanoe Scafuri, Umberto Tarantino, Ernesto Natl Res Council Italy Inst High Performance Comp & Networking I-80131 Naples Italy

Cloud computing is on-demand provisioning of virtual resources aggregated together so that by specific contracts users can lease access to their combined power. Here we hypothesize a new form of service contract by means of which users do not explicitly require resources, but simply supply information about their time-consuming multitask applications and specify their needs through some quality of service (QoS) parameters. The individuation of the virtual machines (VMs) onto which map and execute them is left to the cloud manager. Unfortunately the task/node mapping, already known as NP-hard for conventional parallel systems, becomes more challenging when application tasks must be run on VMs hosted on heterogeneous and shared cloud nodes, and when it must comply with QoS requests too. To support this new cloud service, a novel mapper tool, based on a multiobjective Differential Evolution algorithm, is proposed. Such a tool defines the mapping of the tasks on the VMs with the aim to exploit as much as possible the available cloud resources without penalizing the execution time of the submitted applications and, at the same time, to respect users' QoS requests. To reveal the robustness of this evolutionary tool, an experimental analysis on artificial time-consuming parallel applications, modeled as task interaction graphs, has been effected. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Cloud computing Mapping Differential Evolution multiobjective problems

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Second order necessary conditions in set constrained differentiable vector optimization

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2003年第2期58卷 299-317页

作者： Jiménez, B Novo, V Univ Salamanca Fac Econ & Empresa Dept Econ & Hist Econ Salamanca 37007 Spain Univ Nacl Educ Distancia ETSII Dept Matemat Aplicada Madrid 28080 Spain

We state second order necessary optimality conditions for a vector optimization problem with an arbitrary feasible set and an order in the final space given by a pointed convex cone with nonempty interior. We establish, in finite-dimensional spaces, second order optimality conditions in dual form by means of Lagrange multipliers rules when the feasible set is defined by a function constrained to a set with convex tangent cone. To pass from general conditions to Lagrange multipliers rules, a generalized Motzkin alternative theorem is provided. All the involved functions are assumed to be twice Frechet differentiable.

关键词： multiobjective problems second order necessary conditions for efficiency Lagrange multipliers second order tangent set

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Optimality criteria and duality in multiobjective programming involving nonsmooth invex functions

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JOURNAL OF GLOBAL OPTIMIZATION 2006年第4期35卷 593-606页

作者： Nobakhtian, S. Univ Isfahan Dept Math Esfahan Iran

In this paper a generalization of invexity is considered in a general form, by means of the concept of K-directional derivative. Then in the case of nonlinear multiobjective programming problems where the functions involved are nondifferentiable, we established sufficient optimality conditions without any convexity assumption of the K-directional derivative. Then we obtained some duality results.

关键词： cone approximation duality efficient solution multiobjective problems

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Simulated annealing for multiobjective control system design

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 1997年第6期144卷 582-588页

作者： Whidborne, JF Gu, DW Postlethwaite, I Univ London Kings Coll Dept Mech Engn London WC2R 2LS England Univ Leicester Dept Engn Leicester LE1 7RH Leics England

Engineering design problems are generally multiobjective in that they require several, generally conflicting requirements to be met. One approach to solving a multiobjective design problem is to formulate the problem as a set of algebraic inequalities that must be satisfied for a successful design. Based on simulated annealing, two algorithms to solve a set of inequalities are proposed. The performance of the algorithms is tested on several control system design problems.

关键词： inequality solutions multiobjective problems simulated annealing

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Resource allocation scheme for CCRN using hybrid Giza Pyramids construction-based complex-valued satin bowerbird optimization

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JOURNAL OF SUPERCOMPUTING 2023年第5期79卷 4687-4712页

作者： Justus, J. Jean Anuradha, M. St Josephs Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India SA Engn Coll Dept CSE Chennai Tamil Nadu India

In cooperative cognitive radio networks (CCRN), Primary Users (PUs) share their unused spectrum resources with Secondary Users in order to facilitate cooperative communication in cognitive radio networks (SUs). The novel hybrid Giza Pyramids construction-based Complex valued Satin Bower Optimization (GPC-CSBO) algorithm is proposed in this paper to tackle the multiobjective resource allocation problem in CCRN. To operate the cooperative intermediate nodes and increase their throughput, a variety of constraints are taken into consideration, including the amount of power transmitted, QoS constraints, channel capacity, energy consumption, the priority of resource requests from secondary users (SUs), throughput, fairness evaluation constraints, etc. By constructing a cluster of SUs with the help of the SU base station, the GPC algorithm is used for effective load balancing. By providing 11 different constraints as an input to the hybrid GPC-CSBO algorithm, the SUs message request is prioritized and queued. The hybrid GPC-CSBO algorithm selects an optimal path between the SU to the PU base station based on fairness. The efficiency of the proposed algorithm is evaluated in an IEEE 802.11 WLAN area by comparing the proposed methodology with existing techniques. Throughput, energy efficiency, fairness index, delay, packet delivery, and network lifetime are taken into consideration when determining how effective the proposed approach is.

关键词： Cooperative cognitive radio networks Resource allocation Throughput maximization Giza Pyramids construction Complex-valued satin bowerbird optimization multiobjective problems

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Evaluate the Effectiveness of multiobjective Evolutionary Algorithms by Box Plots and Fuzzy TOPSIS

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 733-743页

作者： Yu, Xiaobing Li, Chenliang Chen, Hong Yu, Xianrui Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Jiangsu Peoples R China

Now, there are a lot of multiobjective evolutionary algorithms (MOEAs) available and these MOEAs argue that they are competitive. In fact, these results are generally unfair and unfaithful. In order to make fair comparison, comprehensive performance index system is established. The weights among the performance index system are solved by an adaptive differential evolution (ADE) algorithm. An approach is proposed to estimate MOEAs based on box plots and fuzzy TOPSIS. Box plots are employed to depict features of performance indicators and fuzzy TOPSIS is used to make evaluation. Experiments have been tested on IEEE CEC2009. The experiment results have revealed that the evaluation approach is effective, fair, and faithful when evaluating MOEAs. (c) 2019 The Authors. Published by Atlantis Press SARL.

关键词： multiobjective problems MOEAs Box plots Fuzzy TOPSIS

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Genetic algorithm for solving a gas lift optimization problem

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2007年第1-2期59卷 84-96页

作者： Tapabrata, Ray Sarker, Ruhul Univ New S Wales Australian Def Force Acad Sch Informat Technol & Elect Engn Canberra ACT 2600 Australia Univ New S Wales Australian Def Force Acad Sch Aerosp Civil & Mech Engn Canberra ACT 2600 Australia

In this paper, we discuss a practical gas lift optimization problem for oil production. Artificial gas lift is a process of oil extraction where gas is injected into the oil wells to pump out the oil in the tubing. The total gas used for oil production is constrained by daily availability limits and limits on maximum injection volume into each well. The oil produced from each well is known to be a nonlinear function of the gas injected into it and varies between wells. The problem is to identify and inject the optimal amount of gas into each well to maximize the total amount of oil production from the reservoir on a daily basis. The problem has long been of practical interest to all major oil exploration companies as it has a potential of deriving large financial benefits. Considering the complexity of the problem, we have used an evolutionary algorithm to solve various classes of this problem. We have also introduced a multiobjective formulation which is attractive as it eliminates the need to solve such problems on a daily basis while maintaining the quality of solutions. Our results show significant improvement over the existing practices. (C) 2007 Elsevier B.V. All rights reserved.

关键词： gas lift optimization oil production planning evolutionary computation genetic algorithms multiobjective problems

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Incremental multiple objective genetic algorithms

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第3期34卷 1325-1334页

作者： Chen, Q Guan, SU Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore

This paper presents a new genetic algorithm approach to multiobjective optimization problems-incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.

关键词： incremental problem solving multiobjective genetic algorithm multiobjective problems multiobjective optimization vector optimization

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Optimal resource allocation in 5G system using modified lion algorithm

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IET COMMUNICATIONS 2021年第1期15卷 126-141页

作者： Kanaga Suba Raja, Subramanian Bamila Virgin Louis, Augusteen Easwari Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India St Xaviers Catholic Coll Engn Dept Comp Sci & Engn Nagercoil India

This research work intends to examine the resource allocation issues within the hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) systems, which includes the NOMA and orthogonal multiple access (OMA) modes. This is exploited to attain the energy efficiency (EE) and spectral efficiency (SE) tradeoff with the minimum rate requirements of users. The entire degree of freedom that is incorporated within the resource allocation comprised user clustering, power allocation, choice of multiple access (MA) modes, and subcarrier assignment as well. This work mainly focuses on the resource allocation issue having every minimum rate requirement of users for the SE-EE tradeoff in hybrid MC-NOMA systems. Moreover, this work aims to make this possible with the incorporation of the optimisation aspect since it is the most significant way of solving multi-objective problems. Here, the resource allocation along with power allocation seems to be the crucial fact that is modelled or designed as the single-objective problem. Thereby, the subcarriers, and powers are optimally allocated by means of a new algorithm named Lion algorithm with Probabilistic Mating, which is the modification of the Lion algorithm. Finally, the performance of the proposed work is compared over other state-of-the-art methods in terms of cost analysis.

关键词： lion algorithm resource allocation hybrid MC-NOMA systems multiobjective problems optimisation OFDM modulation resource allocation issue resource allocation comprised user clustering optimal resource allocation hybrid multicarrier nonorthogonal multiple access systems multiple access modes energy conservation SE-EE tradeoff power allocation multi-access systems

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CONVEX COMPOSITE multiobjective NONSMOOTH PROGRAMMING

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MATHEMATICAL PROGRAMMING 1993年第3期59卷 325-343页

作者： JEYAKUMAR, V YANG, XQ Department of Applied Mathematics University of New South Wales Kensington Australia

This paper examines nonsmooth constrained multi-objective optimization problems where the objective function and the constraints are compositions of convex functions, and locally Lipschitz and Gateaux differentiable functions. Lagrangian necessary conditions, and new sufficient optimality conditions for efficient and properly efficient solutions are presented. Multi-objective duality results are given for convex composite problems which are not necessarily convex programming problems. Applications of the results to new and some special classes of nonlinear programming problems are discussed. A scalarization result and a characterization of the set of all properly efficient solutions for convex composite problems are also discussed under appropriate conditions.

关键词： COMPOSITE FUNCTIONS NONSMOOTH PROGRAMMING multiobjective problems NECESSARY AND SUFFICIENT CONDITIONS SCALARIZATIONS

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