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Simulation and multicriteria optimization modeling approach for regional water restoration management

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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Robust optimization of nonlinear impulsive rendezvous with uncertainty

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Science China(Physics,Mechanics & Astronomy) 2014年第4期57卷 731-740页

作者： LUO YaZhong YANG Zhen LI HengNian College of Aerospace Science and Engineering National University of Defense Technology State Key Laboratory of Astronautic Dynamics

The optimal rendezvous trajectory designs in many current research efforts do not incorporate the practical uncertainties into the closed loop of the design.A robust optimization design method for a nonlinear rendezvous trajectory with uncertainty is proposed in this *** performance index related to the variances of the terminal state error is termed the robustness performance index,and a two-objective optimization model(including the minimum characteristic velocity and the minimum robustness performance index)is formulated on the basis of the Lambert algorithm.A multi-objective,non-dominated sorting genetic algorithm is employed to obtain the Pareto optimal solution *** is shown that the proposed approach can be used to quickly obtain several inherent principles of the rendezvous trajectory by taking practical errors into ***,this approach can identify the most preferable design space in which a specific solution for the actual application of the rendezvous control should be chosen.

关键词： rendezvous and docking uncertainty robust optimization multi-objective evolutionary algorithm

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Optimal Testing Resource Allocation for Modular Software Systems based-on multi-objective evolutionary algorithms with Effective Local Search Strategy

Optimal Testing Resource Allocation for Modular Software Sys...

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IEEE 2nd Workshop on Memetic Computing (MC)

作者： Yu Shuaishuai Fei Dong Bin Li USTC Dep Elect Sci & Technol Hefei Peoples R China

ISBN: (纸本)9781467358910

Software testing is a very important part in software projects. As a key issue in software testing, Optimal Testing Resource Allocation Problems (OTRAPs) have drawn more and more attention recently. Along with the rapid increasing of the scale and complexity of software systems, the problems become more and more difficult to solve. Although some single objective optimization approaches had been used to solve such problems, quite a number of flaws were observed with these approaches, such as trapping into local optima, high computational complexity and few available optimal solutions. In this paper, to solve the problem of few available optimal solutions, an effective local search (ELS) is introduced into two effective multi-objective evolutionary algorithms: Nondominated Sorting Genetic algorithm II (NSGA-II) and Harmonic Distance Based multi-objective evolutionary algorithm (HaD-MOEA), advantages of this strategy over pure multi-objective approaches are testified on two OTRAPs with parallel-series modular software systems. To deal with the problem of high computational complexity, the proposed ELS is also embedded into another effective multi-objective algorithm, multi-objective evolutionary algorithm based on Decomposition (MOEA/D) to solve OTRAPs. Comprehensive experimental studies show the better performance over the state-of-the-art multi-objective approaches for OTRAPs.

关键词： multi-objective evolutionary algorithm parallel-series modular software system effective local search (ELS) NSGA-II MOEA/D software testing reliability and testing cost

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Computational Complexity Measures for Many-objective Optimization Problems

Computational Complexity Measures for Many-objective Optimiz...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

multi-objective Optimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "Many-objective Optimization Problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a multi-objective evolutionary algorithm (MOEA) and its MaOP counterpart. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： multi-objective optimization problem MOP many-objective optimization problem MaOP computational complexity multi-objective evolutionary algorithm MOEA

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DMOPSO: Dual multi-objective Particle Swarm Optimization

DMOPSO: Dual Multi-Objective Particle Swarm Optimization

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IEEE Congress on evolutionary Computation (CEC)

作者： Lee, Ki-Baek Kim, Jong-Hwan Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 305701 South Korea

ISBN: (纸本)9781479914883

Since multi-objective optimization algorithms (MOEAs) have to find exponentially increasing number of nondominated solutions with the increasing number of objectives, it is necessary to discriminate more meaningful ones from the other nondominated solutions by additionally incorporating user preference into the algorithms. This paper proposes dual multi-objective particle swarm optimization (DMOSPO) by introducing secondary objectives of maximizing both user preference and diversity to the nondominated solutions obtained for primary objectives. The proposed DMOSPO can induce the balanced exploration of the particles in terms of user preference and diversity through the dual-stage of nondominated sorting such that it can generate preferable and diverse nondominated solutions. To demonstrate the effectiveness of the proposed DMOPSO, empirical comparisons with other state-of-the-art algorithms are carried out for benchmark functions. Experimental results show that DMOPSO is competitive with the other compared algorithms and properly reflects the user's preference in the optimization process while maintaining the diversity and solution quality.

关键词： multi-objective evolutionary algorithm multi-objective Particle Swarm Optimization Dual-stage dominance check Crowding distance User preference

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Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization 37

Facial recognition using composite correlation filters desig...

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Conference on Applications of Digital Image Processing XXXVII

作者： Cuevas, Andres Diaz-Ramirez, Victor H. Kober, Vitaly Trujillo, Leonardo Inst Politecn Nacl CITEDI Ave Parque 1310 Tijuana 22510 BC Mexico Chelyabinsk State Univ Dept Math Moscow 22860 Russia Inst Tecnol Tijuana Tijuana 22414 Mexico Inst Politecn Nacl CITEDI Tijuana 22510 Mexico

ISBN: (纸本)9781628412444

Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of composite correlation filters designed with multi-objective combinatorial optimization. Given a large set of available face images having variations in pose, gesticulations, and global illumination, a proposed algorithm synthesizes composite correlation filters by optimization of several performance criteria. The resultant filters are able to reliably detect and correctly classify face images of different subjects even when they are corrupted with additive noise and nonhomogeneous illumination. Computer simulation results obtained with the proposed approach are presented and discussed in terms of efficiency in face detection and reliability of facial classification. These results are also compared with those obtained with existing composite filters.

关键词： Facial recognition composite correlation filters multi-objective evolutionary algorithm combinatorial optimization

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Proposing an Efficient Combination of Interesting Measures for Mining Association Rules via NSGA-II

Proposing an Efficient Combination of Interesting Measures f...

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International Congress on Technology, Communication and Knowledge (ICTCK)

作者： Rokh, Babak Mirvaziri, Hamid Eftekhari, Mahdi Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran

ISBN: (纸本)9781479980215

Selecting accurate and simple association rules that efficiently cover all data samples is very important in knowledge discovery. There are several measures to assess accuracy and relations in a rule. This poses a challenge for researchers to select effective measures. Combining different measures via multi-objective evolutionary algorithms is an effective method to select suitable association rules. Therefore in this paper NSGAII algorithm is employed for rule selection via different combination of existing measures (support, certainty factor, change of support, Yao and Liu's one way support, cosine and lift) as objectives. The contributions of the paper are twofold. Firstly, some existing measures are modified. Secondly, several experiments are done to evaluate the performance of different combinations of measures through NSGA-II. The experimental results show that the combination of certainty factor and square of cosine measures are more effective in rule selection.

关键词： Association rules interesting measures multi-objective evolutionary algorithm NSGA-II

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Combining Crowding Estimation in objective and Decision Space With multiple Selection and Search Strategies for multi-objective evolutionary Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第3期44卷 378-393页

作者： Xia, Hu Zhuang, Jian Yu, Dehong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Many multi-objective evolutionary algorithms (MOEAs) have been successful in approximating the Pareto Front. However, well-distributed solutions in the objective and decision spaces are still required in many real-life applications. In this paper, a novel MOEA is proposed to this problem. Distinct from other MOEAs, the proposed algorithm suggests a framework, which includes two crowding estimation methods, multiple selection methods for mating and search strategies for variation, to improve the MOEA's searching ability, and the diversity of its solutions. The algorithm emphasizes the importance of using the decision space and the objective space diversities. The objective space crowding and decision space crowding distances are designed using different ideas. To produce new individuals, three different types of mating selections and their respective search strategies are constructed for the main population and the two sparse populations, with the help of the two crowding measurements. Finally, based on the experimental tests on 17 unconstrained multi-objective optimization problems, the proposed algorithm is demonstrated to have better results compared to several state-of-the-art MOEAs. A detailed analysis on the effectiveness and robustness of the framework is also presented.

关键词： multi-objective evolutionary algorithm decision space diversity multiple search strategies multiple mating selections

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Study on multi-objective Optimization of Oil Production Process 11

Study on Multi-objective Optimization of Oil Production Proc...

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11th World Congress on Intelligent Control and Automation

作者： Liu, Tan Gao, Xianwen Wang, Lina Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479958252

By analyzing the mechanism of oil production process and the relationship between each subsystem, a multi-objective optimization model is proposed, which is with maximizing overall oil production, minimizing overall water production and comprehensive energy consumption for per ton oil as the goals, and then a multi-objective evolutionary algorithm NSGA-II is used for solving the multi-objective optimization model in this paper. Finally, the actual production process in a block of an oil production operation area is taken as background for simulation study, the results showed that the effectiveness and rationality of the model and it laid the foundation for the energy saving of oil production process.

关键词： oil production process multi-objective optimization multi-objective evolutionary algorithm NSGA-II

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A Novel Hybrid multi-objective Optimization Framework: Rotating the objective Space

A Novel Hybrid Multi-objective Optimization Framework: Rotat...

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10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Qiu, Xin Huang, Ye Xu, Jian-Xin Tan, Kay Chen Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore 117456 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

ISBN: (纸本)9783319135632;9783319135625

multi-objective evolutionary algorithms (MOEAs) are popular approaches for solving multi-objective problems (MOPs). One representative method is Non-dominated Sorting Genetic algorithm II (NSGA-II), which has achieved great success in the field by introducing non-dominated sorting into survival selection. However, as a common issue for dominance-based algorithms, the performance of NSGA-II will decline in solving problems with 3 or more objectives. This paper aims to circumvent this issue by incorporating the concept of decomposition into NSGA-II. A grouping-based hybrid multi-objective optimization framework is proposed for tackling 3-objective problems. Original MOP is decomposed into several scalar subproblems, and each group of population is assigned with two scalar subproblems as new objectives. In order to better cover the whole objective space, new objective spaces are formulated via rotating the original objective space. Simulation results show that the performance of the proposed algorithm is competitive when dealing with 3-objective problems.

关键词： multi-objective evolutionary algorithm hybrid decomposition

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