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A multi-Criteria Approach to Rank the Municipalities of the States of Mexico by its Marginalization Level: The Case of Jalisco

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2017年第2期16卷 473-513页

作者： Leyva Lopez, Juan Carlos Solano Noriega, Jesus Jaime Gastelum Chavira, Diego Alonso Univ Occidente Econ & Management Sci Dept Blvd Lola Beltran & Blvd Rolando Arjona Culiacan Sinaloa Mexico

Marginalization studies of a population are tools that enable the Mexican government to understand and compare the socio-demographic situation of different regions of the country. The goal is to implement effectively various programs of social or economic development whose aims are to ght against the population's lag, which has affected the quality of life of Mexican citizens. In this paper, a multi-criteria approach for ranking the municipalities of the states of Mexico by their levels of marginalization is proposed, and the case of Jalisco, Mexico, is presented. The approach uses the ELECTRE III method to construct a mediumsized valued outranking relation and then employs a new multi-objective evolutionary algorithm (MOEA) based on the nondominated sorting genetic algorithm (NSGA) II to exploit the relation to obtain a recommendation. The results of this application can be useful for policymakers, planners, academics, investors, and business leaders. This study also contributes to an important, yet relatively new, body of application-based literature that investigates multi-criteria approaches to decision-making that use fuzzy theory and evolutionary multi-objective optimization methods. A comparison of the ranking obtained with the proposed methodology and the stratification created by the National Population Council of Mexico shows that the methodology presented is consistent and yields reliable results for this problem.

关键词： multi-criteria decision analysis municipal marginalization ranking problem ELECTRE III multi-objective evolutionary algorithms

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multi-objective evolutionary Neural Architecture Search with Weight-Sharing Supernet

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APPLIED SCIENCES-BASEL 2024年第14期14卷

作者： Liang, Junchao Zhu, Ke Li, Yuan Li, Yun Gong, Yuejiao South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Deep neural networks have played a crucial role in the field of deep learning, achieving significant success in practical applications. The architecture of neural networks is key to their performance. In the past few years, these architectures have been manually designed by experts with rich domain knowledge. Additionally, the optimal neural network architecture can vary depending on specific tasks and data distributions. Neural Architecture Search (NAS) is a class of techniques aimed at automatically searching for and designing neural network architectures according to the given tasks and data. Specifically, evolutionary-computation-based NAS methods are known for their strong global search capability and have aroused widespread interest in recent years. Although evolutionary-computation-based NAS has achieved success in a wide range of research and applications, it still faces bottlenecks in training and evaluating a large number of individuals during optimization. In this study, we first devise a multi-objective evolutionary NAS framework based on a weight-sharing supernet to improve the search efficiency of traditional evolutionary-computation-based NAS. This framework combines the population optimization characteristic of evolutionary algorithms with the weight-sharing ideas in one-shot models. We then design a bi-population MOEA/D algorithm based on the proposed framework to effectively solve the NAS problem. By constructing two sub-populations with different optimization objectives, the algorithm can effectively explore network architectures of various sizes in complex search spaces. An inter-population communication mechanism further enhances the algorithm's exploratory capability, enabling it to find network architectures with uniform distribution and high diversity. Finally, we conduct performance comparison experiments on image classification datasets of different scales and complexities. Experimental results demonstrate the effectiveness of the proposed

关键词： deep learning neural architecture search multi-objective evolutionary algorithms image classification

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Project scheduling: A multi-objective evolutionary algorithm that optimizes the effectiveness of human resources and the project makespan

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ENGINEERING OPTIMIZATION 2013年第1期45卷 45-65页

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Fac Cs Exactas ISISTAN Res Inst Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn CONICET Buenos Aires DF Argentina

In this article, the project scheduling problem is addressed in order to assist project managers at the early stage of scheduling. Thus, as part of the problem, two priority optimization objectives for managers at that stage are considered. One of these objectives is to assign the most effective set of human resources to each project activity. The effectiveness of a human resource is considered to depend on its work context. The other objective is to minimize the project makespan. To solve the problem, a multi-objective evolutionary algorithm is proposed. This algorithm designs feasible schedules for a given project and evaluates the designed schedules in relation to each objective. The algorithm generates an approximation to the Pareto set as a solution to the problem. The computational experiments carried out on nine different instance sets are reported.

关键词： project scheduling human resource assignment multi-skilled resources effectiveness of human resources multi-objective evolutionary algorithms

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Robust multi-fidelity design of a micro re-entry unmanned space vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2011年第G11期225卷 1195-1209页

作者： Minisci, E. Vasile, M. Liqiang, H. Univ Strathclyde Dept Mech Engn Glasgow G1 1XJ Lanark Scotland Univ Glasgow Sch Engn Glasgow Lanark Scotland Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian Shaanxi Peoples R China

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preli

关键词： multi-objective evolutionary algorithms robust multi-disciplinary design meta-modelling optimal control unmanned space vehicles

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Minimizing total weighted tardiness and flow time for unrelated parallel machines scheduling with matching-improvement evolutionary algorithms

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IFAC Proceedings Volumes 2009年第4期42卷 1043-1048页

作者： Chiuh-Cheng Chyu Wei-Shung Chang Department of Industrial Engineering and Management Yuan-Ze University Taiwan

This paper addresses unrelated parallel machine scheduling problems with two minimization objectives: total weighted flow time and tardiness, and presents two hybrid methods based on (1) non-dominated sorting genetic algorithms (NSGA-II) and (2) strength Pareto evolutionary algorithm (SPEA). These algorithms were implemented in a different manner according to the following two features: (1) using random or fixed weighted sum direction search (RWSD or FWSD); (2) including or not including a bipartite weighted matching problem (BWMP). The performance of the algorithms is evaluated via two benchmark instances generated by a method in the literature. The experimental results indicate that algorithms with RWSD are superior to those with FWSD, and those including BWMP outperforms those not, in terms of proximity and spread metrics. In particular, NSGA-II with RWSD and BWMP performs best for the large size instance, whereas SPEA with RWSD and BWMP excels other algorithms in solving the medium size instance. Nevertheless, algorithms without BWMP spend much less computation time than others under the same termination criterion

关键词： multi-objective evolutionary algorithms unrelated parallel machines scheduling weighted flow time weighted tardiness weighted bipartite matching algorithm

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New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles

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ADVANCES IN MECHANICAL ENGINEERING 2020年第2期12卷

作者： Nguyen Huy Truong Dinh-Nam Dao Inst Mil Mech Engn Hanoi Vietnam Le Quy Don Tech Univ Control Technol Coll Hanoi 100000 Vietnam

In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.

关键词： NSGA-III algorithm multi-objective particle swarm optimization algorithm optimal solution Powertrain mount system stiffness multi-objective evolutionary algorithms mounting system

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multi-objective evolutionary optimization of unsupervised latent variables of turning process

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APPLIED SOFT COMPUTING 2022年 120卷

作者： de Melo, Simone Aparecida Pereira, Robson Bruno Dutra Reis, Allexandre Fortes da Silva Lauro, Carlos Henrique Brandao, Lincoln Cardoso Univ Fed Sao Joao del Rei Sao Joao Del Rei MG Brazil Ctr Innovat Modeling & Optimizat Syst CIMOS Sao Joao Del Rei MG Brazil Ctr Innovat Sustainable Mfg CIMS Sao Joao Del Rei MG Brazil

Manufacturing process modeling and optimization is a challenging task due to the numerous objectives to be considered in the optimization. Generally, the optimization of these processes requires many objective optimization methods to deal with four or more objective functions. However, the correlation structure of the outputs cannot be disregarded. In this work, it is proposed the unsupervised learning of the outputs together with multi-objective evolutionary optimization of the turning process of AISI 4340 steel considering three scenarios varying the tool nose radius. A central composite design varying the process parameters is used to conduct the experimental tests. After tests and measurements of quality and productivity outputs the p correlated observed outputs are firstly transformed in m unobserved latent variables through factor analysis using principal axis extraction method and varimax rotation, with m < p. Subsequently, the relation between the process parameters and the scores of latent variables is modeled through response surface methodology. multi-objective evolutionary optimization methods are applied in the reduced and uncorrelated set of response models of the transformed outputs. The multi-objective algorithms are compared through hypervolume metric and the pseudo-weights approach is used to decision making. The proposed method can also be applied in other multi-response processes with correlated outputs. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Unsupervised learning Factor analysis multi-objective evolutionary algorithms Turning process

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MRMOGA: a new parallel multi-objective evolutionary algorithm based on the use of multiple resolutions

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2007年第4期19卷 397-441页

作者： Lopez Jaimes, Antonio Coello Coello, Carlos A. CINVESTAV IPN Evolutionary Computat Grp EVOCINVDept Intn ElectSecc Computac Mexico City 07360 DF Mexico

In this paper, we introduce MRMOGA (multiple Resolution multi-objective Genetic Algorithm), a new parallel multi-objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well-defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state-of-the-art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi-objective optimization problems in parallel, particularly when dealing with large search spaces. Copyright (c) 2006 John Wiley & Soris, Ltd.

关键词： parallel processing multi-objective evolutionary algorithms parallel multi-objective evolutionary algorithms multi-objective optimization evolutionary algorithms

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Topology structure optimization of evolutionary hierarchical fuzzy systems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 238卷

作者： Zhao, Tao Zhu, Yue Xie, Xiangpeng Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

In this paper, a new method of hierarchical fuzzy system modeling for high-dimensional regression problems is proposed, which is called multi-objective evolutionary hierarchical fuzzy regression system (MOEHFRS). Different from the existing hierarchical fuzzy systems with fixed topology, the proposed MOEHFRS improves the accuracy of the model, reduces the total number of rules, and eliminates unnecessary features by flexibly constructing topology. In the process of topology evolution, MOEHFRS can exchange and combine different sub-fuzzy systems to achieve the selection and reuse of important features, as well as the elimination of unnecessary features, which improves the diversity of topology and enables the model to be well applied to high-dimensional regression problems. Different combinations of sub-fuzzy systems will result in different performance and number of rules. A new multi-objective evolutionary optimization algorithm is proposed to simultaneously optimize the number of rules and the accuracy of the model, which achieves the balance between complexity and accuracy of MOEHFRS. The proposed method is validated on 13 real world regression datasets and compared with other 5 methods. The results show that MOEHFRS is effective and advanced in terms of accuracy, number of rules, retention of important features and universality in different regression problems.

关键词： Fuzzy rule-based systems Hierarchical fuzzy systems Topology structure optimization multi-objective evolutionary algorithms High-dimensional regression problems

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Optimization of supply diversity for the self-assembly of simple objects in two and three dimensions

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NATURAL COMPUTING 2011年第1期10卷 551-581页

作者： Vieira, Fabio R. J. Barbosa, Valmir C. Univ Fed Rio de Janeiro Programa Engn Sistemas & Computacao COPPE BR-21941972 Rio De Janeiro Brazil

The field of algorithmic self-assembly is concerned with the design and analysis of self-assembly systems from a computational perspective, that is, from the perspective of mathematical problems whose study may give insight into the natural processes through which elementary objects self-assemble into more complex ones. One of the main problems of algorithmic self-assembly is the minimum tile set problem, which in the extended formulation we consider, here referred to as MTSP, asks for a collection of types of elementary objects (called tiles) to be found for the self-assembly of an object having a pre-established shape. Such a collection is to be as concise as possible, thus minimizing supply diversity, while satisfying a set of stringent constraints having to do with important properties of the self-assembly process from its tile types. We present a study of what, to the best of our knowledge, is the first practical approach to MTSP. Our study starts with the introduction of an evolutionary heuristic to tackle MTSP and includes selected results from extensive experimentation with the heuristic on the self-assembly of simple objects in two and three dimensions, including the possibility of tile rotation. The heuristic we introduce combines classic elements from the field of evolutionary computation with a problem-specific variant of Pareto dominance into a multi-objective approach to MTSP.

关键词： Algorithmic self-assembly Minimum tile set problem multi-objective evolutionary algorithms

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