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Optimizing Model Base Predictive Control for Combustion Boiler Process at High Model Uncertainty

CHEMICAL AND BIOCHEMICAL ENGINEERING QUARTERLY 2017年第3期31卷 313-324页

作者： Osman, H. M. King Khalid Univ Dept Chem Engn PO 394 Abha Saudi Arabia

This paper proposes a multi-objective evolutionary algorithm for optimizing model base predictive control (MBPC) tuning parameters applied to the boiling process. The multi-objective evolutionary algorithms are able to incorporate many objective functions that can simultaneously meet robust stability and performance that can satisfy control design objective functions. These promising techniques are successfully implemented to stabilise MBPC at the implications of different levels of model uncertainties. The Pareto optimum technique is able to overcome the problem of trapping the standard genetic algorithms (SGAs) in the local optimum when using the LQ as the objective functions at the price of high model uncertainty. Introducing robust stability and performance objective functions has successfully improved the search procedure for MBPC tuning variables at high model uncertainty.

关键词： model base predictive control multi-objective evolutionary algorithm control tuning

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An improved gradient-based NSGA-II algorithm by a new chaotic map model

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SOFT COMPUTING 2017年第23期21卷 7235-7249页

作者： Liu, Tan Gao, Xianwen Yuan, Qingyun Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Natl Key Lab Integrated Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

Gradient-based non-dominated sorting genetic algorithm II (G-NSGA-II) is successful for solving multi-objective optimization problems. However, the effectiveness of gradient-based hybrid operator is influenced by the distribution of individuals in the population. In order to solve the problem, based on the framework of G-NSGA-II, we propose an improved gradient-based NSGA-II algorithm by introducing a new chaotic map model named IG-NSGA-II. In this algorithm, a new hybrid chaotic map model is first established to initialize population for keeping the diversity of the initial population. Then, the substitution operation of chaotic population candidate is introduced to maintain the diversity and uniformity of the Pareto optimal solution set. Finally, the proposed algorithm is tested on several standard test problems and compared with other algorithms. The experimental results indicate that the proposed algorithm leads to better performance results in terms of the convergence to Pareto front or the diversity of the obtained non-dominated solutions.

关键词： multi-objective evolutionary algorithm Gradient-based hybrid operator Hybrid chaotic map model Substitution operation of chaotic Non-dominated solutions

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Pareto based differential evolution with homeostasis based mutation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第5期32卷 3245-3257页

作者： Singh, Shailendra Pratap Kumar, Anoj Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Allahabad Uttar Pradesh India

Differential evolution algorithm is a very popular nature inspired algorithm and it is used for solving single objective optimization problems. Due to its high convergence rate and ability to produce diverse solutions, it has been extended to solve multiobjective optimization problems. Many versions of DE were proposed however multiobjective versions, where concept of Pareto optimality is used, are most popular. Pareto Based Differential Evolution ( PBDE) is one of them. Although performance of this algorithm is very good, yet its convergence rate can be further improved by minimizing the time complexity of nondominated sorting and by improving the diversity among solutions. This has been implemented by using efficient nondominated algorithm whose time complexity is better than the previous one and a new mutation scheme is implemented in DE which can provide more diversity among solutions. The proposed variant adds one more vector named as Homeostasis mutation vector in the existing mutation vectors to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escaping the situation of stagnation. Performance of proposed algorithm is evaluated on twelve benchmark test functions (bi-objective and tri-objective) on Pareto-optimal front. Performance of proposed algorithm is compared with other state-of-the-art algorithms on five multiobjective evolutionary algorithms (MOEAs).The result verifies that our proposed Homeostasis mutation strategy performs better than other state-of-the-art variants.

关键词： Adaptation optimization multi-objective evolutionary algorithm

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multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 486卷 1-24页

作者： Shang, Ronghua Liu, Huan Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

This paper proposes a relatively all-purpose network clustering technique based on the framework of multi-objective evolutionary algorithms, which can effectively dispose the issue of community detection in unsigned social networks, as well as in signed social networks. Firstly, we formulate a generalized similarity function to construct a similarity matrix, and then a pre-partitioning strategy is projected according to the similarity matrix. The pre-partitioning strategy merely considers nodes with high similarity values, which avoids the interference of noise nodes during the label update phase. In this way, at the initial phase of the algorithm, nodes with strong connections are fleetly gathered into sub communities. Secondly, we elaborately devise a crossover operator, called cross-merging operator, to merge sub-communities generated by the pre-partitioning technique. Moreover, a special mutation operator, based on the similarity matrix of nodes, is implemented to adjust boundary nodes connecting different communities. Finally, to handle different types of networks, we, therefore, have presented the novel multi-objective optimization models for this issue. Through a bulk of rigorous experiments on both unsigned and signed social networks, the preeminent clustering performance illustrate that the proposed algorithm is capable of mining communities effectively. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Community detection multi-objective evolutionary algorithm Similarity matrix Pre-partitioning strategy Social network

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Nadir point estimation for many-objective optimization problems based on emphasized critical regions

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SOFT COMPUTING 2017年第9期21卷 2283-2295页

作者： Wang, Handing He, Shan Yao, Xin Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist Educ Xian 710071 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Nadir points play an important role in many-objective optimization problems, which describe the ranges of their Pareto fronts. Using nadir points as references, decision makers may obtain their preference information for many-objective optimization problems. As the number of objectives increases, nadir point estimation becomes a more difficult task. In this paper, we propose a novel nadir point estimation method based on emphasized critical regions for many-objective optimization problems. It maintains the non-dominated solutions near extreme points and critical regions after an individual number assignment to different critical regions. Furthermore, it eliminates similar individuals by a novel self-adaptive -clearing strategy. Our approach has been shown to perform better on many-objective optimization problems (between 10 objectives and 35 objectives) than two other state-of-the-art nadir point estimation approaches.

关键词： multi-objective evolutionary algorithm Nadir point Critical region Many-objective optimization problem Decision making

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Reducing the loss of agricultural productivity due to compact urban development in municipalities of Switzerland

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2017年第Sep.期65卷 162-177页

作者： Schwaab, Jonas Deb, Kalyanmoy Goodman, Erik Lautenbach, Sven van Strien, Maarten Gret-Regamey, Adrienne Swiss Fed Inst Technol Inst Spatial & Landscape Planning Zurich Switzerland Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Ctr Study Evolut Act BEACON NSF E Lansing MI 48824 USA Univ Bonn IGG Dept Urban Planning & Real Estate Management Bonn Germany

Globally urban growth destroys fertile soils and endangers food security. Fertile soils are often located in the vicinity of existing urban areas. Thus, preserving high-quality soils can conflict with the objective of developing compact urban patterns. In this study, we assess the trade-off between compact urban patterns and urban patterns that can help reduce the loss of agricultural productivity by maintaining fertile agricultural soils. We assess the trade-offs for selected municipalities in Switzerland using a multi-objective evolutionary algorithm to create a front of non-dominated solutions. These results are used as a benchmark against which we compare simulations of a Business-As-Usual urban expansion in Switzerland to estimate the potential for reducing the loss of agricultural productivity. By analysing the front of non-dominated solutions, we show that there are areas of open land that can be converted into residential land without trading-off compactness against agricultural productivity. We show that there is a large potential for reducing the loss of agricultural productivity when optimizing the configuration of urban development. This potential strongly varies between municipalities and seems to depend primarily on the amount of demand for new urban land within each municipality. The proposed methodology of using multi-objective optimization, followed by a post-optimality analysis and including results from business as-usual development can be used to support the decision-making processes in urban planning. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： municipality loss reduction multi-objective evolutionary algorithm agricultural productivity urban pattern Commerce Urban Development Switzerland Business

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The Investment Models Based on the Fundamental and Technical Indicators of Listed Companies

The Investment Models Based on the Fundamental and Technical...

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第37届中国控制会议

作者： Xiaolian Meng Xiuzhen Chen School of Economics and Management Nanjing University of Science and Technology

The fundamental and technical approaches are often used to analyze the operation status of listed companies and the development tendency of the stock market. The investment models using financial ratios and technical indicators are proposed in this article, and a multi-objective evolutionary algorithm with benefits and risks is used to optimize them. Two different chromosomes are used for representing different investment models with real constraints. An empirical study based on 40 stocks of SSE A shares from Cathay Pacific database was conducted to illustrate the practicability of the proposed models. The results show that better stocks and better transaction decisions can be found and selected by the portfolio management method based on fundamental and technical analysis, which could be obtained a portfolio with better result than the market index. The effectiveness and practicality of this investment model is improved more with more fundamental indicators introduced, especially cash flow growth, capital expenditure growth rates and payout ratio.

关键词： multi-objective evolutionary algorithm fundamental analysis technical analysis stock investment

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multi-objective evolutionary algorithm Based on the Fuzzy Similarity Measure

Multi-objective Evolutionary Algorithm Based on the Fuzzy Si...

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International Conference on Advanced Measurement and Test (AMT 2010)

作者： Li, Junfeng Dai, Wenzhan Wang, Huijiao Zhejiang Sci Tech Univ Dept Automat Control Hangzhou Zhejiang Peoples R China

ISBN: (纸本)0878492712

evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. This paper proposes a novel multi-objective evolutionary algorithm based on the fuzzy similarity measure. First, the best solution of every objective among the multi-objectives is obtained and they are regarded on as the referenced vector. Second, the fuzzy similarity measure between every individual and the referenced vector is solved and the fuzzy similarity measure is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.

关键词： multi-objective evolutionary algorithm Fuzzy similarity measure Genetic algorithm

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Statistical Decision Support for Fitness Function Formulation to multi-objective evolutionary algorithms: a Case Study on Computer Automated Exterior Lighting Design

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IEEE LATIN AMERICA TRANSACTIONS 2016年第1期14卷 281-288页

作者： Rocha, H. X. Peretta, I. S. Lima, G. F. M. Boaventura, R. S. Marques, L. G. Yamanaka, K. IFG Campus Itumbiara Itumbiara Goias Brazil Univ Fed Uberlandia Av Engenheiro Dinz 1178CP 593 BR-38400 Uberlandia MG Brazil CGWORKS Uberlandia MG Brazil IFTM Campus Uberlandia Ctr Uberlandia MG Brazil

evolutionary algorithms are stochastic heuristics which can optimize over special functions, known as fitness functions, by manipulating the structure of candidate solutions known as individuals. multi-objective evolutionary algorithms can deal with many objectives to be optimized, whether concurrent or divergent, ending by returning an optimal frontier, i.e. a set of solutions all defined as Pareto optimal. The idea behind using evolutionary algorithms to perform computer automated design is to be able to formulate a fitness function that, starting from a candidate solution, could reflect the impact the evaluated individual has on those objectives to be optimized. The case study presented in this work is an application for computer automated exterior lighting design which has some concurrent objectives to be optimized: the energy efficiency and the illumination quality. This work investigates four metrics to illumination quality and two metrics for energy efficiency as possible proposals to the fitness function formulation. Eight variations were designed as combinations of pairs from those metrics. To help in the decision process, the statistical hypothesis test known as difference of means is then used to enable comparisons between those variations. This test is performed two by two and three decision matrices is then derived, the ones about global uniformity, mean electrical power, and mean efficiency class index. The concept of Pareto's "statistical dominance", defined in this work and based on statistical evidences, indicates a final decision about which one from the previous designed variations of fitness function is the more appropriated for the presented problem.

关键词： multi-objective evolutionary algorithm Fitness Function Formulation Hypothesis Test for Difference of Means Exterior Lighting Design Illumination Quality Energy Efficiency Pareto's Statistical Dominance

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Impact of selection methods on the diversity of many-objective Pareto set approximations 21

Impact of selection methods on the diversity of many-objecti...

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21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems (KES)

作者： Marti, Luis Segredo, Eduardo Sanchez-Pi, Nayat Hart, Emma Univ Fed Fluminense Inst Comp Niteroi RJ Brazil Univ Paris Saclay CNRS INRIA LRI TAU Team Paris France Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland Univ La Laguna Dpto Ingn Informat & Sistemas San Cristobal Laguna Spain Univ Estado Rio De Janeiro DICC IME Rio De Janeiro RJ Brazil

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hypervolume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, yield the best results. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： multi-objective evolutionary algorithm many-objective optimisation selection mechanism diversity preservation

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