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Parallelization of a non-linear multi-objective optimization algorithm: Application to a location problem

APPLIED MATHEMATICS AND COMPUTATION 2015年 255卷 114-124页

作者： Gila Arrondo, Aranzazu Redondo, Juana L. Fernandez, Jose Ortigosa, Pilar M. Univ Murcia Dept Stat & Operat Res Murcia Spain Univ Granada Dept Comp Architecture & Technol CeiA3 Agrifood Campus Int Excellence Granada Spain Univ Almeria CeiA3 Dept Informat Agrifood Campus Int Excellence Almeria Spain

Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly intractable. In order to cope with those problems, several (meta) heuristic procedures have been developed during the last decade whose aim is to obtain a good discrete approximation of the Pareto-front. In this vein, a new multi-objective evolutionary algorithm, called FEMOEA, which can be applied to many nonlinear multi-objective optimization problems, has recently been proposed. Through a comparison with an exact interval branch-and-bound algorithm, it has been shown that FEMOEA provides very good approximations of the Pareto-front. Furthermore, it has been compared to the reference algorithms NSGA-II, SPEA2 and MOEA/D. Comprehensive computational studies have shown that, among the studied algorithms, FEMOEA was the one providing, on average, the best results for all the quality indicators analyzed. However, when the set approximating the Pareto-front must have many points (because a high precision is required), the computational time needed by FEMOEA may not be negligible at all. Furthermore, the memory requirements needed by the algorithm when solving those instances may be so high that the available memory may not be enough. In those cases, parallelizing the algorithm and running it in a parallel architecture may be the best way forward. In this work, a parallelization of FEMOEA, called FEMOEA-Paral, is presented. To show its applicability, a bi-objective competitive facility location and design problem is solved. The results show that FEMOEA-Paral is able to maintain the effectiveness of the sequential version and this by reducing the computational costs. Furthermore, the parallel version shows good scalability. The efficiency results have been analyzed by means of a profiling and tracing toolkit for performance analysis. (C) 2014 Elsevier Inc. All

关键词： Nonlinear multi-objective optimization multi-objective evolutionary algorithm Parallelism Efficiency Effectiveness Tuning and Analysis Utilities Performance System Facility location problem

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Robot Path Planning Based on an Improved multi-objective PSO Method

Robot Path Planning Based on an Improved Multi-Objective PSO...

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2016 International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2016)

作者： Li Di Zheng Zheng Meng Xia Min Xia Economics and Technology Research Institute of State Grid Henan Electric Power Company Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology

ISBN: (纸本)9781510830363

Robot path planning is an important content in the field of robot research. Robot Path Planning is a typical multi-objective optimization problem. The path length, the degree of path smoothness and the degree of security are the optimization objectives in this work. And an improved multi-objective PSO method is used for optimization. In this method, in order to make the particle population multi-objective particle swarm optimization algorithm can quickly converge to the Pareto optimal boundary, an environment selection and a matching selection strategy are proposed. At each iteration of the algorithm, in order to improve the population information exchange and reduce the randomness, the environmental selection and matching selection strategy of SPEA2 are used for multi-objective PSO method, and the particle population can faster convergence to the Pareto optimal boundary. The simulation results verify the method, and the result of proposed method is better than that of multi-objective PSO method, and the simulations indicates that the proposed model is practical for robot path planning.

关键词： multi-objective evolutionary algorithm SPEA2 Robot path planning PSO

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Differential evolution with Pareto tournament for the multi-objective next release problem

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APPLIED MATHEMATICS AND COMPUTATION 2015年 252卷 1-13页

作者： Chaves-Gonzalez, Jose M. Perez-Toledano, Miguel A. Univ Extremadura Dept Comp Sci E-06071 Caceres Spain

Software requirements selection is the engineering process in which the set of new requirements which will be included in the next release of a software product are chosen. This NP-hard problem is an important issue involving several contradictory objectives that 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 cover the highest number of customer requirements. Furthermore, in real instances of the problem, the requirements tackled suffer interactions and other restrictions which complicate the problem. In this paper, we use an adapted multi-objective version of the differential evolution (DE) evolutionary algorithm which has been successfully applied to several real 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, and with three interaction constraints. On the other hand, the original DE 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 to demonstrate the effectiveness of the multiobjective proposal and the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature under a set of public datasets. (c) 2014 Elsevier Inc. All rights reserved.

关键词： Software requirements selection multi-objective evolutionary algorithm Differential evolution metaheuristic Search Based Software Engineering Next release problem

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An Architecture-Based multi-objective Optimization Approach to Testing Resource Allocation

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IEEE TRANSACTIONS ON RELIABILITY 2015年第1期64卷 497-515页

作者： Yang, Bo Hu, Yanmei Huang, Chin-Yu Univ Elect Sci & Technol China Sch Comp Sci & Engn Collaborat Auton Comp Lab Chengdu 610054 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Sichuan Peoples R China Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30043 Taiwan

Software systems are widely employed in society. With a limited amount of testing resource available, testing resource allocation among components of a software system becomes an important issue. Most existing research on the testing resource allocation problem takes a single-objective optimization approach, which may not adequately address all the concerns in the decision-making process. In this paper, an architecture-based multi-objective optimization approach to testing resource allocation is proposed. An architecture-based model is used for system reliability assessment, which has the advantage of explicitly considering system architecture over the reliability block diagram (RBD)-based models, and has good flexibility to different architectural alternatives and component changes. A system cost modeling approach which is based on well-developed software cost models is proposed, which would be a more flexible, suitable approach to the cost modeling of software than the approach adopted by others which is based on an empirical cost model. A multi-objective optimization model is developed for the testing resource allocation problem, in which the three major concerns in the testing resource allocation problem, i.e., system reliability, system cost, and the total amount of testing resource consumed, are taken into consideration. A multi-objective evolutionary algorithm (MOEA), called multi-objective differential evolution based on weighted normalized sum (WNS-MODE), is developed. Experimental studies are presented, and the experiments show several results. 1) The proposed architecture-based multi-objective optimization approach can identify the testing resource allocation strategy which has a good trade-off among optimization objectives. 2) The developed WNS-MODE is better than the MOEA developed in recent research, called HaD-MOEA, in terms of both solution quality and computational efficiency. 3) The WNS-MODE seems quite robust from the sensitivity analysis results.

关键词： Hyper-volume indicator multi-objective evolutionary algorithm software architecture software cost models system reliability assessment

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Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world

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CLIMATIC CHANGE 2015年第3期132卷 373-386页

作者： Kwakkel, Jan H. Haasnoot, Marjolijn Walker, Warren E. Delft Univ Technol Fac Technol Policy & Management NL-2600 GA Delft Netherlands Deltares NL-2600 MH Delft Netherlands Delft Univ Technol Dept Policy Anal Fac Technol Policy & Management NL-2600 GA Delft Netherlands Delft Univ Technol Dept Air Transport & Operat Fac Aerosp Engn NL-2600 GA Delft Netherlands

Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper presents a model-driven approach supporting the development of promising adaptation pathways, and illustrates the approach using a hypothetical case. We use robust optimization over uncertainties related to climate change, land use, cause-effect relations, and policy efficacy, to identify the most promising pathways. For this purpose, we generate an ensemble of possible futures and evaluate candidate pathways over this ensemble using an Integrated Assessment Meta Model. We understand 'most promising' in terms of the robustness of the performance of the candidate pathways on multiple objectives, and use a multi-objective evolutionary algorithm to find the set of most promising pathways. This results in an adaptation map showing the set of most promising adaptation pathways and options for transferring from one pathway to another. Given the pathways and signposts, decision-makers can make an informed decision on a dynamic adaptive plan in a changing environment that is able to achieve their intended objectives despite the myriad of uncertainties.

关键词： Uncertainty multi-objective evolutionary algorithm dynamic Policies Water Management computer-assisted Self tuning Robustness Pathway meta-models

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multi-objective genetic algorithm for missing data imputation

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PATTERN RECOGNITION LETTERS 2015年第Part1期68卷 126-131页

作者： Lobato, Fabio Sales, Claudomiro Araujo, Igor Tadaiesky, Vincent Dias, Lilian Ramos, Leonardo Santana, Adamo Fed Univ Para R Augusto Correa Inst Technol Belem Para Brazil Fed Univ Western Para Engn & Geosci Inst Belem Para Brazil

A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bias imposed by this problem, several treatment methods have been proposed, highlighting the data imputation methods, which can be viewed as an optimization problem where the goal is to reduce the bias caused by the absence of information. Although most imputation methods are restricted to one type of variable whether categorical or continuous. To fill these gaps, this paper presents the multi-objective genetic algorithm for data imputation called MOGAImp, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task. A set of tests for evaluating the performance of the algorithm were applied using 30 datasets with induced missing values;five classifiers divided into three classes: rule induction learning, lazy learning and approximate models;and were compared with three techniques presented in the literature. The results obtained confirm the MOGAImp outperforms some well-established missing data treatment methods. Furthermore, the proposed method proved to be flexible since it is possible to adapt it to different application domains. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Missing data Data imputation multi-objective evolutionary algorithm Genetic algorithm

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Research on Portfolio Model Based on multi-objective Genetic algorithm

Research on Portfolio Model Based on Multi-Objective Genetic...

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2016 4th International Conference on Electrical&Electronics Engineering and Computer Science(ICEEECS 2016)

作者： Haonan Lin South China University of Technology

ISBN: (纸本)9781510835429

Portfolio optimization problem is a multi-objective optimization problem, it is necessary to consider the benefits should also consider the risks and the optimal situation is to achieve the least risk and maximum return. Papers define portfolio optimization problems, analyze the shortcomings of the existing portfolio model and the corresponding solution ideas, introduce multi-objective genetic algorithm and demonstrate the feasibility of multi-objective genetic algorithm in the application portfolio. algorithm design process, detailed process of constructing and building a portfolio investment in line with the actual situation, put forward five objective optimization model from all angles, through a reasonable, optimized simulation test, and then select the algorithm selected from a number of programs better overall performance of the program.

关键词： Portfolio High Performance Computing multi-objective evolutionary algorithm multiple Attribute Decision Making

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Integrated Mechanical and Thermodynamic Optimization of an Engine Linkage Using a multi-objective Genetic algorithm

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JOURNAL OF MECHANICAL DESIGN 2015年第2期137卷 1-4页

作者： Sullivan, Thomas A. van de Ven, James D. Northrop, William F. McCabe, Kieran Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA

In order to improve the thermodynamic efficiency of an internal combustion engine (ICE), a Stephenson-III six-bar linkage is optimized to serve as a replacement for the traditional slider-crank. Novel techniques are presented for formulating the design variables in the kinematic optimization that guarantee satisfaction of the Grashof condition and of transmission angle requirements without the need for an explicit constraint function. Additionally, a nested generalization of the popular NSGA-II algorithm is presented that allows simultaneous optimization of the kinematic, dynamic, and thermodynamic properties of the mechanism. This approach successfully solves the complex six-objective optimization problem, with challenges for future refinement including improvement of the combustion simulation to attain better accuracy without prohibitive computational expense.

关键词： thermodynamic optimization Thermodynamic properties multi-objective evolutionary algorithm thermal efficiency Engines design variable kinematics Genetic algorithm (GA)

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A fuzzy multi-objective portfolio selection model with transaction cost and liquidity 11

A fuzzy multi-objective portfolio selection model with trans...

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11th International Conference on Computational Intelligence and Security (CIS)

作者： Yue, Wei Wang, Yuping Xidian Univ Sch Comp Sci & Technol Xian 710071 Shannxi Peoples R China

ISBN: (纸本)9781467386609

In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entropy function is presented. By using the model, we can find tradeoffs between risk, return and the diversification degree of portfolio. Thirdly, a new multi-objective evolutionary algorithm is designed to solve the proposed model. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed model and the corresponding algorithm.

关键词： Portfolio selection Possibilistic moments Entropy multi-objective evolutionary algorithm

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multi-objective multiagent Credit Assignment in NSGA-II Using Difference Evaluations 14

Multi-Objective Multiagent Credit Assignment in NSGA-II Usin...

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14th International Conference on Autonomous Agents and multiagent Systems (AAMAS)

作者： Yliniemi, Logan Wilson, Drew Tumer, Kagan Oregon State Univ Corvallis OR 97331 USA Austin Peay State Univ Clarksville TN 37044 USA

ISBN: (纸本)9781450334136

Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multiple exploration robots) yet there is little work on their intersection. In this work, we leverage recent advances in single-objective multiagent learning to address multi-objective domains. We focus on the impact of difference evaluation functions (which extracts an agent's contribution to the team objective) on the Non-dominated Sorting Genetic algorithm-II (NSGA-II), a state-of-the-art multi-objective evolutionary algorithm. We derive multiple methods for incorporating difference evaluations into the NSGA-II framework, and test each in a multiagent rover exploration domain, which is a good surrogate for a wide variety of distributed scheduling and resource gathering problems. We show that how and where difference evaluations are incorporated in the NSGA-II algorithm is critical, and can either provide significant benefits or destroy system performance, depending on how it is used. Median performance of the correctly used difference evaluations dominates best-case performance of NSGA-II in a multiagent multi-objective problem.

关键词： multiagent Learning multi-objective Problem multi-objective evolutionary algorithm NSGA-II

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