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A multi-objective Routing Strategy for QoS and Energy Awareness in Software-Defined Networks

IEEE COMMUNICATIONS LETTERS 2017年第11期21卷 2416-2419页

作者： Fernandez-Fernandez, Adriana Cervello-Pastor, Cristina Ochoa-Aday, Leonardo Univ Politecn Cataluna Dept Network Engn Castelldefels 08860 Spain

Energy consumption is a key concern in the deployment and operation of current data networks, for which software-defined networks (SDNs) have become a promising alternative. Although several works have been proposed to improve the energy efficiency, these techniques may lead to performance degradations when QoS requirements are neglected. Inspired by this problem, this letter introduces a new routing strategy, jointly considering QoS requirements and energy awareness in SDN with in-band control traffic. To that end, we present a complete formulation of the optimization problem and implement a multi-objective evolutionary algorithm. Simulation results validate the performance improvement on critical network parameters.

关键词： Software-defined networks energy-aware routing quality of service multi-objective evolutionary algorithm in-band control traffic

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Strategies of multi-objective optimization in drug discovery and development

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EXPERT OPINION ON DRUG DISCOVERY 2011年第9期6卷 871-884页

作者： Nicolotti, Orazio Giangreco, Ilenia Introcaso, Antonellina Leonetti, Francesco Stefanachi, Angela Carotti, Angelo Univ Bari Dipartimento Farmacochim I-70125 Bari Italy

Introduction: Drug discovery and development is a typical multi-objective problem and its successes or failures depend on the simultaneous control of numerous, often conflicting, molecular and pharmacological properties. multi-objective optimization strategies represent a new approach to capture the occurrence of varying optimal solutions based on trade-offs among the objectives taken into account. In view of this, multi-objective optimization aims to discover a set of satisfactory compromises that may in turn be used to find the global optimal solution by optimizing numerous dependent properties simultaneously. Areas covered: The authors review the potential of multi-objective strategies in a number of fields including: drug library design;substructure mining;the derivation of quantitative structure-activity relationship models;ranking of docking poses. The authors also discuss the potential of multi-objective strategies in controlling competing properties for absorption, distribution, metabolism and elimination/toxicity optimization. Expert opinion: It is very clear to those who work in drug discovery and development that the success of rational drug design is largely dependent on the control of a number of, often conflicting, objectives. Therefore, multi-objective optimization methods, which have recently been introduced to the field of molecular discovery, represent the ultimate frontier in chemoinformatics. The widespread use of these multi-objective techniques has provided new opportunities in medicinal chemistry as seen through its use in a number of applications for chemoinformatics both within academia and the pharmaceutical industry.

关键词： chemoinformatics multi-criterion optimization multi-objective evolutionary algorithm multi-objective optimization

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objective reduction based on nonlinear correlation information entropy

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SOFT COMPUTING 2016年第6期20卷 2393-2407页

作者： Wang, Handing Yao, Xin Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist Educ Xian 710071 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

It is hard to obtain the entire solution set of a many-objective optimization problem (MaOP) by multi-objective evolutionary algorithms (MOEAs) because of the difficulties brought by the large number of objectives. However, the redundancy of objectives exists in some problems with correlated objectives (linearly or nonlinearly). objective reduction can be used to decrease the difficulties of some MaOPs. In this paper, we propose a novel objective reduction approach based on nonlinear correlation information entropy (NCIE). It uses the NCIE matrix to measure the linear and nonlinear correlation between objectives and a simple method to select the most conflicting objectives during the execution of MOEAs. We embed our approach into both Pareto-based and indicator-based MOEAs to analyze the impact of our reduction method on the performance of these algorithms. The results show that our approach significantly improves the performance of Pareto-based MOEAs on both reducible and irreducible MaOPs, but does not much help the performance of indicator-based MOEAs.

关键词： multi-objective optimization objective reduction Nonlinear correlation information entropy multi-objective evolutionary algorithm Dimension reduction

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Distributed multi-objective optimization for SNP-SNP interaction detection

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METHODS 2024年 221卷 55-64页

作者： Li, Fangting Zhao, Yuhai Xu, Tongze Zhang, Yuhan Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Coll Med & Biol Informat Engn Shenyang Peoples R China

The detection of complex interactions between single nucleotide polymorphisms (SNPs) plays a vital role in genome-wide association analysis (GWAS). The multi-objective evolutionary algorithm is a promising technique for SNP-SNP interaction detection. However, as the scale of SNP data further increases, the exponentially growing search space gradually becomes the dominant factor, causing evolutionary algorithm (EA)-based approaches to fall into local optima. In addition, multi-objective genetic operations consume significant amounts of time and computational resources. To this end, this study proposes a distributed multi-objective evolutionary framework (DM-EF) to identify SNP-SNP interactions on large-scale datasets. DM-EF first partitions the entire search space into several subspaces based on a space-partitioning strategy, which is nondestructive because it guarantees that each feasible solution is assigned to a specific subspace. Thereafter, each subspace is optimized using a multi-objective EA optimizer, and all subspaces are optimized in parallel. A decomposition-based multi-objective firework optimizer (DCFWA) with several problem-guided operators was designed. Finally, the final output is selected from the Pareto-optimal solutions in the historical search of each subspace. DM-EF avoids the preference for a single objective function, handles the heavy computational burden, and enhances the diversity of the population to avoid local optima. Notably, DM-EF is load-balanced and scalable because it can flexibly partition the space according to the number of available computational nodes and problem size. Experiments on both artificial and real-world datasets demonstrate that the proposed method significantly improves the search speed and accuracy.

关键词： Distributed computing SNP-SNP interactions multi-objective evolutionary algorithm Space-partitioning strategy

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Simulation and multicriteria optimization modeling approach for regional water restoration management

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ANNALS OF OPERATIONS RESEARCH 2014年第1期219卷 123-140页

作者： Udias, Angel Efremov, Roman Galbiati, Lorenzo Canamon, Israel Univ Rey Juan Carlos Dept Estadist & Invest Operat Madrid Spain Agencia Catalana Aigua Barcelona Spain Univ Politecn Madrid Dept Matemat Aplicada & Metodos Informat Madrid Spain

This paper presents a modeling framework that intends to select the optimal robust wastewater reclamation program of measures (PoM) to achieve the European Water Framework Directive (WFD) objectives in the inner Catalonia watersheds. The integrative methodological tool developed incorporates a water quality model to simulate the effects of the PoM used to reduce pollution pressures on the hydrologic network. A multi-objective evolutionary algorithm (MOEA) helps to identify efficient trade-offs between PoM cost and water quality. Interactive Decisions Map (IDM)-a multi-criteria visualization-based decision support tool is used to provide a clear idea of the trade-off between water status and the cost to achieve such situation. Lastly, a stochastic simulation model to analyze the sensitivity under varied environmental uncertainties is run. Moreover, the tool is oriented to guide water managers in their decision-making processes. Additionally, this paper analyzes the results of the application of the management tool in the inner Catalan watershed in order to perform the European WFD. This tool has had a key role in the design of part of the PoM which shall be implemented to achieve objectives of the WFD in 2015 in all the Catalan catchments.

关键词： European Water Framework Directive Waste water reclamation management multi-objective evolutionary algorithm Uncertain

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An algorithm for traffic grooming in WDM optical mesh networks with multiple objectives

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TELECOMMUNICATION SYSTEMS 2005年第3-4期28卷 369-386页

作者： Prathombutr, P Stach, J Park, E Univ Missouri Sch Interdisciplinary Comp & Engn Kansas City MO 64110 USA

This paper studies a traffic grooming in wavelength-division multiplexing (WDM) mesh networks for the SONET/SDH streams requested between node pairs. The traffic could be groomed at the access node before converting to an optical signal carried in the all-optical network. We design a virtual topology with a given physical topology to satisfy multiple objectives and constraints. The grooming problem of a static demand is considered as an optimization problem. The traditional algorithms found in the literatures mostly focus on a single objective either to maximize the performance or to minimize the cost. We propose a multi-objective evolutionary algorithm to solve a grooming problem that optimizes multiple objectives all together at the same time. In this paper we consider the optimization of three objectives: maximize the traffic throughput, minimize the number of transceivers, and minimize the average propagation delay or average hop counts. The simulation results show that our approach is superior to an existing heuristic approaches in an acceptable running time.

关键词： multi-objective evolutionary algorithm grooming WDM mesh optical networks Pareto optimal

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multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions

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evolutionary COMPUTATION 2019年第2期27卷 291-311页

作者： Ceberio, Josu Calvo, Borja Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20018 Spain BCAM Bilbao 48009 Spain

In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multi-objective algorithms in their optimisation. In this article, we follow up this idea by presenting a methodology for multi-objectivising combinatorial optimisation problems based on elementary landscape decompositions of their objective function. Under this framework, each of the elementary landscapes obtained from the decomposition is considered as an independent objective function to optimise. In order to illustrate this general methodology, we consider four problems from different domains: the quadratic assignment problem and the linear ordering problem (permutation domain), the 0-1 unconstrained quadratic optimisation problem (binary domain), and the frequency assignment problem (integer domain). We implemented two widely known multi-objective algorithms, NSGA-II and SPEA2, and compared their performance with that of a single-objective GA. The experiments conducted on a large benchmark of instances of the four problems show that the multi-objective algorithms clearly outperform the single-objective approaches. Furthermore, a discussion on the results suggests that the multi-objective space generated by this decomposition enhances the exploration ability, thus permitting NSGA-II and SPEA2 to obtain better results in the majority of the tested instances.

关键词： multi-objectivisation combinatorial optimisation elementary landscape decomposition multi-objective evolutionary algorithm

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A coordinated voltage/reactive power control method for multi-TSO power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 20-28页

作者： Zhang, Anan Li, Hongwei Liu, Fan Yang, Honggeng SW Petr Univ Sch Elect Engn & Informat Chengdu 610500 Peoples R China Sichuan Elect Power Res Inst Chengdu 610000 Peoples R China Sichuan Univ Sch Elect Engn & Informat Chengdu 610065 Peoples R China

multi area power systems work most often with a poor inter-regional coordination about reactive power concerns. Poor coordinated operation may not achieve significant improvements in the quality and efficiency of power system operation, and may even increase the risk of blackout for multi-TSO (Transmission System Operators) power systems. This paper focused on the voltage/reactive power coordinated control. Voltage/reactive power interactions between interconnected power networks were derived from the augmented active and reactive power decoupled network equations. According to distribution computing concept, a novel voltage/reactive power control model was presented in this paper, which could optimize the active power losses of both local network and its interconnected areas. And the model's data communication was simple: only an equivalent susceptance matrix and the optimal reactive power injection value for external network need be communicated irregularly. Moreover, this model could avoid raising confidentiality issues because it need not exchange explicit structure and constraints information between different TSOs. Efficacy of the proposed model was illustrated through simulations on two IEEE systems and an application to an actual system. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Voltage control Reactive power control Coordinated control Active and reactive power decoupled network equations multi-objective evolutionary algorithm

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A Possibilistic Mean-Downside Risk-Skewness Model for Efficient Portfolio Selection

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2013年第3期21卷 585-595页

作者： Vercher, Enriqueta Bermudez, Jose D. Univ Valencia Dept Estadist & Invest Operat Valencia Spain

This paper presents a new possibilistic model for the portfolio selection problem. The uncertainty of future returns on a given portfolio is modeled using LR-fuzzy numbers. Some possibilistic moments are considered to measure the risk of and return on the investment. Since the joint possibility distribution of the returns on the assets is unknown, we consider the returns on a given portfolio as the historical dataset instead of considering the individual returns on the assets as the dataset. We introduce a coefficient of possibilistic skewness in order to incorporate a measurement of the asymmetry of the fuzzy return on a given portfolio. We solve the multi-objective optimization problems that are associated with the possibilistic mean-downside risk-skewness model by using an evolutionary procedure to generate efficient portfolios. The procedure provides different patterns of investment, whose portfolios meet the explicit restrictions imposed by the investor. Thus, from among the points in the efficient frontier, the investor may select a portfolio that optimizes an economically meaningful objective function. The performance of this approach is tested using a dataset of assets from the Spanish stock market.

关键词： Interval-valued expectation LR-fuzzy numbers mean-downside risk-skewness (MDRS) model multi-objective evolutionary algorithm portfolio selection possibilistic moments

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Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 76卷 188-206页

作者： Chen, Xin Li, Yibing Wang, Kaipu Wang, Lei Liu, Jie Wang, Jun Wang, Xi Vincent Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Wuhan Business Univ Sch Mech & Elect Engn Wuhan 430056 Peoples R China Wuhan Univ Technol Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China KTH Royal Inst Technol Dept Prod Engn S-10044 Stockholm Sweden

Mass personalization manufacturing (MPM), an emerging production pattern, aims to improve enterprise profit in modern industries. However, the processing of heterogeneous orders from the consumers complicates such production scheduling problem. In addition, different scale tasks should adopt different splitting strategies in practical manufacturing, which makes the task splitting method more worthy of investigation. Towards MPM, this paper presents a distributed hybrid flowshop scheduling problem with variable task splitting (DHFSP-VTS) to minimize the makespan and total energy consumption simultaneously. Meanwhile, the VTS allows the tasks to be split into different sublots so they can save setup and transfer time. To solve these problems, we present an order modularization processing method that can categorize multiple types of orders into specific generation tasks, and a highly effective reinforcement learning-multiple objective evolutionary algorithm based on decomposition (RLMOEA/D) is designed. In RL-MOEA/D, there are three features: (1) three initial rules are used for initialization based on the current splitting scheme that can increase the diversity of solutions;(2) the reinforcement learning agent uses the Q-learning mechanism to dynamically select the scheme of task splitting as action;(3) a neighborhood search strategy improves the exploitation ability and expand the solution space. To verify the effectiveness of RL-MOEA/D, the MOEA/Ds based on four splitting schemes and four RL combined meta-heuristics are compared on 18 instances. The results show that RL-MOEA/D can obtain the best optimization and stability of all the other comparison algorithms. Therefore, it's a new technique to solve DHFSP with large-scale tasks, especially for the problem of MPM.

关键词： Reinforcement learning Distributed hybrid flowshop scheduling Mass personalization manufacturing Variable task splitting multi-objective evolutionary algorithm

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