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IEEE Congress on evolutionary Computation

作者： de Souza, Thatiana C. N. Goldbarg, Elizabeth F. G. Goldbarg, Marco C. Univ Fed Rio Grande do Norte Dept Informat & Appl Math Natal RN Brazil

ISBN: (纸本)9781479904549;9781479904532

To distribute oil derivatives by multi-product pipelines is an important problem faced by the petroleum industry. Some researchers deal with it as a discrete problem where batches of products flow in a network. Minimizing delivery time is a usual objective handled by engineers when scheduling products in pipeline networks. Nevertheless, other costs may also be considered such as losses due to interfaces and electrical energy. Losses due to interfaces occur when different products sent consecutively contaminate each other. Usually, no separation devices exist between batches of different products and losses due to interface can be significant. The price paid for electrical energy varies during the day, so it is important also to try to minimize this cost. These three minimization objectives are considered, simultaneously, i.e. delivery time at demand nodes, interface losses and electrical energy cost. A transgenetic algorithm is proposed and applied to thirty random instances. The results obtained with the proposed method are compared with those produced by an NSGAII algorithm.

关键词： distribution network oil derivatives multi-objective evolutionary algorithm

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Optimal protection of complex networks exposed to a terrorist hazard: A multi-objective evolutionary approach

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Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2008年第3期222卷 327-335页

作者： Salazar a, D.E. Rocco s, C.M. Zio, E. Universidad de Las Palmas de Gran Canaria Spain Universidad Central de Venezuela United States Politecnico di Milano United States

The present paper proposes an approach for prioritizing the protection of a network system exposed to a terrorist attack. The approach is based on a multi-objective optimization (MO) formulation for finding Pareto optimal solutions with respect to two indicators measuring the damage that the attack may cause: the time to reach all network destination nodes (TTRAD) and the average number of persons affected (ANPA). The MO is tackled by means of a multi-objective evolutionary algorithm (MOEA) that combines the basic concepts of dominance with the general characteristics of evolutionary algorithms. Within this optimization scheme, the goodness of each alternative protection scheme is quantified by a combination of cellular automata (CA) and Monte Carlo (MC) simulation. Numerical examples illustrate how the approach is capable of identifying effective protection schemes. © 2008, Institution of Mechanical Engineers. All rights reserved.

关键词： cellular automata critical infrastructures Monte Carlo simulation multi-objective evolutionary algorithm multi-objective optimization network systems security

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Time-Jerk Synthetic Optimal Trajectory Planning of Robot Using Strength Pareto evolutionary algorithm

Time-Jerk Synthetic Optimal Trajectory Planning of Robot Usi...

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International Conference on Advanced Measurement and Test (AMT 2010)

作者： Yan, Jie Gong, Daoxiong Beijing Univ Technol Sch Elect Informat & Control Engn Beijing 100124 Peoples R China

ISBN: (纸本)9780878492695

The Strength Pareto evolutionary algorithm (SPEA) is adopted to find time-jerk synthetic optimal trajectory of a hexapod robot in the joint space. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of via points to assure overall continuity of velocity and acceleration. Taken both the execution time and minimax approach of jerk as objectives, and expressed the kinematics constraints as upper bounds on the absolute values of velocity and acceleration, the mathematic model of time-jerk synthetic optimal trajectory planning is built. Finally, SPEA is adopted to optimize the stair-climbing trajectory of a hexapod robot, the simulation results show that this method can solve the trajectory planning problem effectively, and the stair-climbing trajectory can meet the contradictory objective functions of high speed and low robot vibration well.

关键词： multi-objective evolutionary algorithm minimum time-jerk cubic splines hexapod robot stair-climbing

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evolutionary multi-objective Approach to Control of Mobile Robot in Unknown Environment

Evolutionary Multi-Objective Approach to Control of Mobile R...

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22nd International Symposium on Information Communication and Automation Technologies

作者： Konjicija, Samim Avdagic, Zikrija Fac Elect Engn Dept Control & Elect Sarajevo Bosnia & Herceg Fac Elect Engn Dept Informat Technol Sarajevo Bosnia & Herceg

ISBN: (纸本)9781424442201

This paper describes how a non-stationary multi-objective optimization model can be used for synthesis of control of mobile robot in unknown environment. The modelled problem is solved using multi-objective genetic algorithm (MOGA). Results of experiments conducted in simulation environment demonstrate the application of the described approach.

关键词： mobile robot multi-objective evolutionary algorithm dynamic optimization

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Sensor Event Mining with Hybrid Ensemble Learning and evolutionary Feature Subset Selection Model 3

Sensor Event Mining with Hybrid Ensemble Learning and Evolut...

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IEEE International Conference on Big Data

作者： Mehdiyev, Nijat Krumeich, Julian Werth, Dirk Loos, Peter German Res Ctr Artificial Intelligence DFKI Inst Informat Syst IWi Saarbrucken Germany

ISBN: (纸本)9781479999255

Recent advancements in sensor technology offer opportunities to manage business processes in a proactive manner. To enable an effective and real-time monitoring, sensor data have to be treated and processed in an event processing manner. Complex Event Processing is an efficient technology that detects useful complex events by matching primitive sensor events using event patterns. Event patterns can be represented as templates that combine primitive events by temporal, logical, spatial and sequential correlations to detect more complex events. Identifying event patterns out of streaming data with a high data volume and velocity is a challenging task. In this paper, we propose an Ensemble Model consisting of a crisp and fuzzy rule based classifiers in order to derive decision rules as event patterns. Before implementing the ensemble classifier directly to the streaming data, we select the most influential feature subset using a multi-objective evolutionary algorithm. The performance of the proposed model was evaluated using real data obtained from accelerometer sensors. Promising results with high accuracy and appropriate level of computational complexity were obtained and discussed.

关键词： multi-objective evolutionary algorithm feature subset selection rule induction complex event processing ensemble learning

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Endowing the MIA Cloud Autoscaler with Adaptive evolutionary and Particle Swarm multi-objective Optimization algorithms 1

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20th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Yannibelli, Virginia Pacini, Elina Monge, David Mateos, Cristian Rodriguez, Guillermo ISISTAN UNICEN CONICET Tandil Buenos Aires Argentina UNCUYO ITIC Mendoza Argentina Fac Ingn UNCuyo Mendoza Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (数字)9783030898175

ISBN: (纸本)9783030898175;9783030898168

PSE (Parameter Sweep Experiments) applications represent a relevant class of computational applications in science, engineering and industry. These applications involve many computational tasks that are both resource-intensive and independent. For this reason, these applications are suited for Cloud environments. In this sense, Cloud autoscaling approaches are aimed to manage the execution of different kinds of applications on Cloud environments. One of the most recent approaches proposed for autoscaling PSE applications is MIA, which is based on the multi-objective evolutionary algorithm NSGA-III. We propose to endow MIA with other multi-objective optimization algorithms, to improve its performance. In this respect, we consider two well-known multi-objective optimization algorithms named SMS-EMOA and SMPSO, which have significant mechanic differences with NSGA-III. We evaluate MIA endowed with each of these algorithms, on three real-world PSE applications, considering resources available in Amazon EC2. The experimental results show that MIA endowed with each of these algorithms significantly outperforms MIA based on NSGA-III.

关键词： Parameter Sweep Experiments Cloud autoscaling multi-objective evolutionary algorithm multi-objective particle swarm optimization CloudSim Amazon EC2

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evolutionary algorithm with AHP Decision-making Method for Cloud Workflow Service Composition 7

Evolutionary Algorithm with AHP Decision-making Method for C...

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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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A Novel Diversity Maintenance Scheme for evolutionary multi-objective Optimization

A Novel Diversity Maintenance Scheme for Evolutionary Multi-...

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14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Gee, Sen Bong Qiu, Xin Tan, Kay Chen Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9783642412783;9783642412776

Recently, decomposition-based multi-objective evolutionary algorithm (MOEA/D) has received increasing attentions due to its simplicity and decent optimization performance. In the presence of the deceptive optimum, the weight vector approach used in MOEA/D may not be able to prevent the population traps into local optimum. In this paper, we propose a new algorithm, namely Diversity Preservation multi-objective evolutionary algorithm based on Decomposition (DivPre-MOEA/D), which uses novel diversity maintenance scheme to enhance the performance of MOEA/D. The proposed algorithm relaxes the dependency of the weight vector approach on approximated ideal vector to maintain diversity of the population. The proposed algorithm is evaluated on CEC-09 test suite and compared the optimization performance with MOEA/D. The experiment results show that DivPre-MOEA/D can provide better solutions spread along the Pareto front.

关键词： Diversity-preservation multi-objective evolutionary algorithm decomposition

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A Novel Hybrid evolutionary Strategy and its Periodization with multi-objective Genetic Optimizers

A Novel Hybrid Evolutionary Strategy and its Periodization w...

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2010 IEEE World Congress on Computational Intelligence

作者： Kaufmann, Paul Knieper, Tobias Platzner, Marco Univ Gesamthsch Paderborn Dept Comp Sci D-33098 Paderborn Germany

ISBN: (纸本)9781424481262

This work investigates the effects of the periodization of local and global multi-objective search algorithms. To this, we introduce a model for periodization and define a new multi-objective evolutionary algorithm adopting concepts from evolutionary Strategies and NSGAII. We show that our method, especially when periodized with standard multi-objective genetic algorithms, excels for the evolution of digital circuits on the Cartesian Genetic Programming model as well as on some standard benchmarks such as the ZDT6.

关键词： Cartesian genetic programming genetic algorithms hybrid evolutionary strategy multi-objective evolutionary algorithm multi-objective genetic optimizers multi-objective search algorithms periodization search problems

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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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