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IEEE 7th International Conference on Cloud

作者： Zhang, Miao Liu, Li Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467395601

multiple Cloud services can be composed as a single service to fulfill the execution of large-scale workflow application. It faces trade-offs among several QoS metrics causing by users' preferences. Most existing works utilize multi-objective evolutionary algorithms to address this problem. However, it is hardly to express different users' preferences. This paper proposes an evolutionary algorithm for Cloud service composition, combining NSGA-II with Decision-Making method to calculate Crowding Distance with triangular fuzzy number based on AHP. The experimental results show that our approach could precisely capture users' preferences, and perform better in scalability than other general multi-objective algorithms.

关键词： Cloud computing Services Composition multi-objective evolutionary algorithm AHP

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Survey of multi objective evolutionary algorithms

Survey of Multi Objective Evolutionary Algorithms

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Vachhani, Vimal L. Dabhi, Vipul K. Prajapati, Harshadkumar B. Dharmsinh Desai Univ Dept Informat Technol Nadiad India

ISBN: (纸本)9781479970759

multi-objective optimization aims at simultaneously optimizing two or more objectives of a problem. multi-objective evolutionary algorithms (MOEAs) are widely accepted and useful for solving real world multi-objective problems. When we have two or more conflicting objectives of a problem then we can apply MOEA. MOEA generates a set of non-dominated solutions at the end of run, which is called Pareto set. The Pareto front contains set of Pareto solutions. Any MOEA aims to improve (i) convergence of population towards true Pareto front and (ii) diversity of solutions belonging to Pareto set. Generally, an external archive is used by MOEAs to maintain a set of non-dominated Pareto set solutions. Sometimes, Pareto set contains more number of solutions than the size of archive. This paper presents survey of various methods used by different MOEAs for reducing the size of Pareto set while maintaining solutions diversity. It presents comparison of these methods along with their advantages and disadvantages. The paper concludes by giving limitation of crowding distance based method in various scenarios.

关键词： multi-objective evolutionary algorithm Pareto front Convergence Diversity

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Applying an evolutionary algorithm for the Analysis of Mental Disorders in Macro-urban Areas: The Case of Barcelona

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SPATIAL ECONOMIC ANALYSIS 2015年第3期10卷 270-288页

作者： Salinas-Perez, Jose Alberto Rodero-Cosano, Maria Luisa Garcia-Alonso, Carlos Ramon Salvador-Carulla, Luis Univ Loyola Andalucia Dept Psychol Sociol & Social Work Cordoba Spain Univ Loyola Andalucia Dept Math & Engn Cordoba Spain Univ Sydney Ctr Disabil Res & Policy Sydney NSW 2006 Australia

Spatial analysis is widely used to study geographic patterns of diseases. To locate groups of close spatial units where the treated prevalence is significantly high or low, the latest contribution is a tool based on a multi-objective evolutionary algorithm, which has not yet been used in macro-urban areas: this study is the first attempt for this purpose. To do so, spatial distribution of the treated prevalence of mental disorders in basic health areas was analysed within the Barcelona metropolitan zone during 2009. The results highlight inequitable zones that need further attention, and geographically weighted regression shows that socio-economic factors influence treated prevalence although there may be additional factors involved.

关键词： multi-objective evolutionary algorithm treated prevalence spatial clusters macro-urban areas

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multi-objective optimization using NSGA-II for power distribution system reconfiguration

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2015年第1期25卷 38-53页

作者： Vitorino, Romeu M. Jorge, Humberto M. Neves, Luis P. Polytech Inst Leiria Sch Technol & Management P-2411901 Leiria Portugal Univ Coimbra Dept Elect Engn & Computers P-3030290 Coimbra Portugal R&D Unit INESC Coimbra P-3000033 Coimbra Portugal

This study proposes a new strategy to solve the problem of radial power distribution system (RDS) reconfiguration in a multi-objective and constrained environment. Due to the presence of various conflicting objectives and constraints, the proposed strategy uses the Elitist Non-Dominated Sorting Genetic algorithm-II (NSGA-II), an effective evolutionary multi-objective optimization technique. NSGA-II determines a set of pareto-optimal solutions for the power distribution system topology, considering power losses, reliability and investment in tie-switches. The methodology adopted to evaluate the RDS reliability uses a non-sequential Monte Carlo Simulation and is focused on the impacts of branch failures for interruption energy assessment. The effectiveness of the proposed methodology is demonstrated on a 69 bus RDS. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： distribution system optimization multi-objective evolutionary algorithm network reconfiguration power losses reliability

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Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

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SCIENCE OF THE TOTAL ENVIRONMENT 2015年 515卷 39-48页

作者： Dong, Feifei Liu, Yong Su, Han Zou, Rui Guo, Huaicheng Peking Univ Coll Environm Sci & Engn Key Lab Water & Sediment Sci MOE Beijing 100871 Peoples R China Tetra Tech Inc Fairfax VA 22030 USA Yunnan Key Lab Pollut Proc & Management Plateau L Kunming 650034 Peoples R China Peking Univ Inst Water Sci Beijing 100871 Peoples R China

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely "optimal" solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multiobjective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Stochastic multi-objective evolutionary algorithm Pareto fronts Tradeoff analysis Back propagation neural network

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Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

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OPTICS COMMUNICATIONS 2015年 338卷 77-89页

作者： Diaz-Ramirez, Victor H. Cuevas, Andres Kober, Vitaly Trujillo, Leonardo Awwal, Abdul Inst Politecn Nacl CITEDI Tijuana 22510 BC Mexico CICESE Dept Comp Sci Ensenada 22860 Baja California Mexico Inst Tecnol Tijuana Tijuana 22500 BC Mexico Lawrence Livermore Natl Lab Natl Ignit Facil Livermore CA 94551 USA

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Object recognition Composite correlation filters multi-objective evolutionary algorithm Combinatorial optimization

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An Improved Ideal Point Setting in multiobjective evolutionary algorithm Based on Decomposition

An Improved Ideal Point Setting in Multiobjective Evolutiona...

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International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration

作者： Fan, Zhun Li, Wenji Cai, Xinye Lin, Huibiao Hu, Kaiwen Yin, Haibin Shantou Univ Dept Elect Engn Shantou 515063 Guangdong Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781467383127

In this paper, we propose an improved ideal point setting method in the framework of MOEA/D. MOEA/D decomposes a multi-objective optimisation problem into a number of scalar optimisation problems and optimise them simultaneously. The performance of MOEA/D highly relates to its decomposition method, and the proposed ideal point setting approach is used in the weighted Tchebycheff (TCH) and penalty-based boundary intersection (PBI) decomposition approach. It expands the region of search in the objective space by transforming the original ideal point into its symmetric point and changes the search direction of each subproblems in MOEA/D. In order to verify the proposed ideal point setting method, we design a set of multi-objective problems(MOPs). The proposed method is compared with the original MOEA/D-TCH and MOEA/D-PBI on MOPs. The experimental results demonstrate that our proposed ideal point setting method performs well in terms of both diversity and convergence.

关键词： multi-objective evolutionary algorithm Ideal Point Setting

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Teaching learning based optimization with Pareto tournament for the multiobjective software requirements selection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 43卷 89-101页

作者： Chaves-Gonzalez, Jose M. Perez-Toledano, Miguel A. Navasa, Amparo Univ Extremadura Dept Comp Sci Caceres Spain

Software requirements selection is a problem which consists of choosing the set of new requirements which will be included in the next release of a software package. This NP-hard problem is an important issue involving several contradictory objectives which have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to satisfy the highest number of customer requirements. Furthermore, when managing real instances of the problem, the requirements tackled suffer interactions and other restrictions which make the problem even harder. In this paper, a novel multi-objective teaching learning based optimization (TLBO) algorithm has been successfully applied to several instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer's satisfaction. In addition, three interaction constraints have been also managed. In this context, the original TLBO algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out in order to prove the effectiveness of the multiobjective proposal. In fact, the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Software requirements selection multi-objective evolutionary algorithm Teaching learning based optimization Search Based Software Engineering Next Release Problem Swarm intelligence

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Bi-objective optimisation for scheduling the identical parallel batch-processing machines with arbitrary job sizes, unequal job release times and capacity limits

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第6期53卷 1680-1711页

作者： Abedi, Mehdi Seidgar, Hany Fazlollahtabar, Hamed Bijani, Rohollah Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Iran Univ Sci & Technol Fac Ind Engn Tehran Iran Mehr Alborz Univ Dept Ind Management Tehran Iran

This paper deals the scheduling identical parallel batch-processing machines (BPMs) that each machine can be process a group of jobs as a batch simultaneously. The paper presents a new bi-objective-mixed integer linear programming model for BPM in which arbitrary job size, unequal release time and capacity limits are considered as realistic assumptions occur in the manufacturing environments. The objectives are to minimise the makespan and the total weighted earliness and tardiness of jobs (just in time). After developing a new bi-objective model, an e-constraint method is proposed to solve the problem. This problem has been known as Np-hard. Therefore, two multi-objective optimisation methods, namely, fast non-dominated sorting genetic algorithm (NSGA-II) and multi-objective imperialist competitive algorithm (MOICA) are employed to find the pareto-optimal front for large-sized problems. The parameters of the proposed algorithms are calibrated using Response surface methodology (RSM) and the performances of the proposed algorithms on the problems of various sizes are analysed and the computational results clarify that MOICA outperform than NSGA-II in quality of solutions and computational time.

关键词： multi-objective evolutionary algorithm makespan release time scheduling parallel batch-processing machine (BPM) just-in-time scheduling

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Robust routing and channel allocation in multi-hop cognitive radio networks

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WIRELESS NETWORKS 2015年第1期21卷 127-137页

作者： Dong, Bei Wu, Jianshe Jiao, Licheng Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Jointly consider routing and spectrum selection is essentially and necessary in multi-hop cognitive radio networks. System cost and throughput are commonly used to evaluate performance of routing and spectrum selection schemes. Traditional methods mostly translate these metrics into a single objective function, and corresponding weights are allocated to each metric representing impact on the entire network performance. Optimal solutions of these approaches are sensitive to the weight settings which are usually hard to appropriately chosen. In this work, the task of routing and channel allocation is modeled as a two-objective optimization problem. Two conflicting metric functions system total throughput and total cost are optimized simultaneously, and a novel memetic algorithm which adopts a new neighborhood search procedure is proposed to solve this problem. Incorporated with robustness consideration on routing, a new robustness metric is also presented to work as a decision mechanism to ensure the robustness of the entire network. The aim of this task is to find the best compromise routing and channel allocation scheme on system throughput, cost and robustness among the feasible solution set. Simulation results demonstrate that the optimal solution set obtained by the memetic algorithm can clearly show the conflicting relationship of the system cost and throughput when choosing different routing and channel selection schemes. The best solution made by the additional robustness metric among these solutions can achieve the best performance of the cognitive radio network.

关键词： Cognitive radio Cognitive radio networks Routing Robustness multi-objective evolutionary algorithm

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