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evolutionary multiobjective optimization of steel structural systems in tall buildings 1

4th International Conference on evolutionary Multi-Criterion optimization (EMO 2007)

作者： Kicinger, Rafal Obayashi, Shigeru Arciszewski, Tomasz George Mason Univ Volgenau Sch Informat Technol & Engn 4400 Univ Dr MS 6C1 Fairfax VA 22030 USA Tohoku Univ Inst Fluid Sci Aoba Ku Sendai 9808577 Japan

ISBN: (数字)9783540709282

ISBN: (纸本)9783540709275

This paper presents results of extensive computational experiments in which evolutionary multiobjective algorithms were used to find Pareto-optimal solutions to a complex structural design problem. In particular, Strength-Pareto evolutionary Algorithm 2 (SPEA2) was combined with a mathematical programming method to find optimal designs of steel structural systems in tall buildings with respect to two objectives (both minimized): the total weight and the maximum horizontal displacement of a tall building. SPEA2 was employed to determine Pareto-optimal topologies of structural members (topology optimization) whose cross-sections were subsequently optimized by the mathematical programming method (sizing optimization). The paper also presents the shape of the Pareto front in this two-dimensional objective space and discusses its dependence on the building's aspect ratio. The results reported provide both qualitative and quantitative knowledge regarding the relationship between the two objectives. They also show the trade-offs involved in the process of conceptual and detailed design of complex structural systems in tall buildings.

关键词： evolutionary multiobjective optimization structural design Pareto front tall buildings

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evolutionary multiobjective optimization on a chip

Evolutionary multiobjective optimization on a chip

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IEEE Workshop on Evolvable and Adaptive Hardware

作者： Bonissone, Stefano Subbu, Raj Gen Elect Global Res 1 Res Circle Niskayuna NY 12309 USA

ISBN: (纸本)9781424406999

The majority of research in Evolvable Hardware is focused on evolving logic for deployment on reconfigurable hardware. There are far fewer reports concerned with the implementation of evolutionary Algorithms (EAs) in hardware. The focus of our research is directed toward using reconfigurable hardware as a means to speed up evolutionary search, and in particular evolutionary multiobjective optimization (EMO). evolutionary multiobjective optimization utilizes an evolutionary search to find solutions to difficult multiobjective optimization problems. We present an implementation of an EMO algorithm in reconfigurable hardware, and discuss how it may be utilized in practical deployment situations.

关键词： evolutionary multiobjective optimization Evolvable hardware Field programmable gate array Neural networks

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A Hybrid Estimation of Distribution Algorithm with Decomposition for Solving the multiobjective Multiple Traveling Salesman Problem

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2012年第5期42卷 682-691页

作者： Shim, V. A. Tan, K. C. Cheong, C. Y. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Inst High Performance Comp Dept Comp Sci Singapore 138632 Singapore

evolutionary multiobjective optimization with decomposition, in which the algorithm is not required to differentiate between the dominated and nondominated solutions, is one of the promising approaches in dealing with multiple conflicting objectives. In this paper, the estimation of distribution algorithm (EDA) is integrated into the decomposition framework. The search behavior of the algorithm is further enhanced by hybridizing local search metaheuristic approaches with the decomposition EDA. Three local search techniques, including hill climbing, simulated annealing, and evolutionary gradient search, are considered. A novel multiobjective formulation of the multiple traveling salesman problem is proposed. The hybrid algorithms are used to solve the formulated problem with different number of objective functions, salesmen, and problem sizes. The effectiveness and efficiency of the algorithms are tested and benchmarked against several state-of-the-art multiobjective evolutionary paradigms.

关键词： Estimation of distribution algorithms (EDAs) evolutionary gradient search (EGS) evolutionary multiobjective optimization hill climbing (HC) multiple traveling salesman problem (mTSP) simulated annealing (SA) univariate modeling (UM)

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A Study on the Use of multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2012年第2期5卷 231-253页

作者： Trawinski, Krzysztof Cordon, Oscar Quirin, Arnaud European Ctr Soft Comp Mieres Mieres 33600 Spain

In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strategy. A state-of-the-art evolutionary multiobjective algorithm, namely NSGA-II, is used to provide a component classifier selection and improve FURIA-based fuzzy MCS. We propose five different fitness functions based on three different optimization criteria, accuracy, complexity, and diversity. Twenty UCI high dimensional datasets were considered in order to conduct the experiments. A combination between accuracy and diversity criteria provided very promising results, becoming competitive with classical MCS learning methods.

关键词： Fuzzy rule-based multiclassification systems bagging FURIA genetic selection of individual classifiers diversity measures evolutionary multiobjective optimization NSGA-II

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An evolutionary Bayesian belief network methodology for participatory decision making under uncertainty: An application to groundwater management

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INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2012年第3期8卷 456-461页

作者： Farmani, Raziyeh Henriksen, Hans Jorgen Savic, Dragan Butler, David Univ Exeter Ctr Water Syst N Pk Rd Exeter EX4 4QF Devon England

An integrated participatory approach based on Bayesian belief network (BBN) and evolutionary multiobjective optimization is proposed as an efficient decision-making tool in complex management problems. The proposed methodology incorporates all the available evidence and conflicting objectives to evaluate implications of alternative actions in the decision-making process and suggests best decision pathways under uncertainty. A BBN provides a framework within which the contributions of stakeholders can be taken into account. It allows a range of different factors and their probabilistic relationship to be considered simultaneously. It takes into account uncertainty by assigning probability to those variables whose states are not certain. The integration of BBN with evolutionary multiobjective optimization allows the analysis of tradeoff between different objectives and incorporation and acknowledgement of a broader set of decision goals into the search and decision-making process. The proposed methodology can be used as a decision support tool to model decision-making processes for complex problems. It deals with uncertainties in decision making pertaining to human behavior and checks for consistency of the developed BBN structure and the parameters of the probabilistic relationship by uncovering discrepancies in the decision analysis process (e.g., bias in completeness or redundancy of the model based on a utility function). It generates a set of efficient management options (appropriate combinations of interventions) that balances conflicting objectives. The effectiveness of the proposed methodology is discussed through application to a real case study. It is shown that it successfully identifies any inconsistencies in the developed BBN models and generates large numbers of management options that achieve an optimal tradeoff between different objectives. Integr Environ Assess Manag 2012;8: 456461. (c) SETAC

关键词： Bayesian belief network evolutionary multiobjective optimization Management Validation

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An Intelligent System to Address Occupational Health of Workers Exposed to High Risk Jobs

An Intelligent System to Address Occupational Health of Work...

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IEEE Congress on evolutionary Computation (CEC)

作者： Srivastava, Sanjay Anand, Yogesh K. Dayalbagh Educ Inst Dept Mech Engn Agra Uttar Pradesh India

ISBN: (纸本)9781467315098

Workers in labor-intensive manufacturing units, in general, maximize their earnings by subjecting themselves to greater risk of occupational health hazards (RoOHH) mainly due to economic reasons. To embark upon this issue, we introduce an intelligent system employing artificial neural network (ANN) and non-dominated sorting genetic algorithm (NSGA-II). Experimentations are carried out in a brick manufacturing unit in India. Observations spell out that firing is the most severe job among others. A job-combination approach is incorporated wherein firing workers do another job along with firing to reduce their exposure to high temperature zone while maintaining their earnings to a satisfactory level. RoOHH is measured in terms of risk assessment score (RAS). ANN models the psychological responses of workers in terms of RAS, and facilitates the evaluation of one of the fitness function of NSGA-II. NSGA-II searches for optimal work schedules in a job-combination to minimize RAS and maximize earnings simultaneously.

关键词： occupational health brick manufacturing evolutionary multiobjective optimization artificial neural network risk assessment score

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multiobjective evolutionary algorithms: A survey of the state of the art

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SWARM AND evolutionary COMPUTATION 2011年第1期1卷 32-49页

作者： Zhou, Aimin Qu, Bo-Yang Li, Hui Zhao, Shi-Zheng Suganthan, Ponnuthurai Nagaratnam Zhang, Qingfu E China Normal Univ Comp Sci & Technol Dept Shanghai 200241 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Xi An Jiao Tong Univ Sch Sci Xian 710049 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented. (C) 2011 Elsevier B.V. All rights reserved.

关键词： multiobjective optimization evolutionary multiobjective optimization multiobjective evolutionary algorithms Multicriteria decision making

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An advanced multiobjective genetic algorithm design for the time and space assembly line balancing problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第1期61卷 103-117页

作者： Chica, Manuel Cordon, Oscar Damas, Sergio European Ctr Soft Comp Mieres 33600 Spain ETS Informat & Telecomunicac Dept Comp Sci & Artificial Intelligence Granada 18071 Spain

Time and space assembly line balancing considers realistic multiobjective versions of the classical assembly line balancing industrial problems involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. In addition to their multi-criteria nature, the different problems included in this field inherit the precedence constraints and the cycle time limitations from assembly line balancing problems, which altogether make them very hard to solve. Therefore, time and space assembly line balancing problems have been mainly tackled using multiobjective constructive metaheuristics. Global search algorithms in general - and multiobjective genetic algorithms in particular - have shown to be ineffective to solve them up to now because the existing approaches lack of a proper design taking into account the specific characteristics of this family of problems. The aim of this contribution is to demonstrate the latter assumption by proposing an advanced multiobjective genetic algorithm design for the 1/3 variant of the time and space assembly line balancing problem which involves the joint minimization of the number and the area of the stations given a fixed cycle time limit. This novel design takes the well known NSGA-II algorithm as a base and considers the use of a new coding scheme and sophisticated problem specific operators to properly deal with the said problematic questions. A detailed experimental study considering 10 different problem instances (including a real-world instance from the Nissan plant in Barcelona, Spain) will show the good yield of the new proposal in comparison with the state-of-the-art methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Assembly lines Time and space assembly line balancing evolutionary multiobjective optimization NSGA-II

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Aquifers Management through evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)

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WATER RESOURCES MANAGEMENT 2011年第14期25卷 3883-3909页

作者： Molina, Jose-Luis Farmani, Raziyeh Bromley, John Univ Politecn Valencia Inst Water Engn & Environm Valencia 46022 Spain Univ Exeter Ctr Water Syst Exeter EX4 4QF Devon England Univ Oxford Ctr Water Res Oxford OX1 3QY England

An approach for the integration of Object-Oriented Bayesian Networks (OOBNs) and evolutionary multiobjective optimization (EMO) is proposed for integrated water resource management and decision support. Bayesian Networks (BNs) offer a novel and powerful tool for modelling complex water systems, facilitating the use of hierarchical modelling by improving the efficiency and communication between the different parts of a model. EMO offers a range of non-dominated optimal management solutions on a Pareto front that facilitate the identification of tradeoffs among conflicting criteria regarding stakeholder's preferences. The integrated tool provides new possibilities for undertaking an integrated analysis where stakeholder participation can play an important role. It is used for simultaneously analysing the whole water system, characterising uncertainty as probabilities and evaluating different management options. The tool is applied to an overexploited water system located in Southern Spain that is supplied totally by groundwater. In this study, a complex model based on BNs is designed and used as the core of the study. The transition to evolutionary Bayesian networks (EOBNs) allows stakeholder involvement to be utilized more effectively for designing and evaluating the model's consistency, and taking into account their conflicting interests.

关键词： Stakeholders' participation Aquifers management Decision support systems Bayesian Networks evolutionary multiobjective optimization

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Diversity Management in evolutionary Many-Objective optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2011年第2期15卷 183-195页

作者： Adra, Salem F. Fleming, Peter J. Univ Sheffield Dept Comp Sci Sheffield S1 3JD S Yorkshire England Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

In evolutionary multiobjective optimization, the task of the optimizer is to obtain an accurate and useful approximation of the true Pareto-optimal front. Proximity to the front and diversity of solutions within the approximation set are important requirements. Most established multiobjective evolutionary algorithms (MOEAs) have mechanisms that address these requirements. However, in many-objective optimization, where the number of objectives is greater than 2 or 3, it has been found that these two requirements can conflict with one another, introducing problems such as dominance resistance and speciation. In this paper, two diversity management mechanisms are introduced to investigate their impact on overall solution convergence. They are introduced separately, and in combination, and tested on a set of test functions with an increasing number of objectives (6-20). It is found that the inclusion of one of the mechanisms improves the performance of a well-established MOEA in many-objective optimization problems, in terms of both convergence and diversity. The relevance of this for many-objective MOEAs is discussed.

关键词： Diversity requirement evolutionary multiobjective optimization

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