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Set-based concept selection in multi-objective problems involving delayed decisions

JOURNAL OF ENGINEERING DESIGN 2010年第6期21卷 619-646页

作者： Avigad, Gideon Moshaiov, Amiram ORT Braude Coll Engn Dept Mech Engn Karmiel Israel Tel Aviv Univ Dept Mech Engn IL-69978 Tel Aviv Israel

This paper introduces a computational approach to support concept selection in multi-objective design. It is motivated by: (1) a common need to delay some decisions during conceptual design due to the presence of uncertainties;and (2) intentional delay of decisions for the purpose of maintaining several optional concepts, as suggested by the concurrent engineering procedure of Toyota. Here, for the first time, a multi-objective set-based concept (SBC) selection problem with delayed decisions is formulated and solved. SBCs are conceptual solutions, which are represented by sets of particular solutions, with each concept having a one-to-many relation with the objective space. Several novel notions, such as higher-level concepts, multi-model concepts and robust concepts to delayed decisions, are defined and used. These lead to an auxiliary multi-objective decision problem. The auxiliary objectives are concept optimality and variability, both paramount to concept selection, with concept variability strongly supporting the idea of intentionally keeping several useful alternatives as long as possible. Academic and engineering examples are provided to demonstrate the proposed approach and its applicability to real-life problems. The results demonstrate that the suggested technique may well support the process of delayed decision either when needed or when deliberately done.

关键词： multi-criteria decision-making multi-objective evolutionary algorithms conceptual design uncertainties robustness design space representation

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multi-objective design of PV-wind-diesel-hydrogen-battery systems

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RENEWABLE ENERGY 2008年第12期33卷 2559-2572页

作者： Dufo-Lopez, Rodolfo Bernal-Agustin, Jose L. Univ Zaragoza Dept Elect Engn Zaragoza 50018 Spain

This paper presents, for the first time, a triple multi-objective design of isolated hybrid systems minimizing, simultaneously, the total cost throughout the useful life of the installation, pollutant emissions CO2) and unmet load. For this task, a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combination of components of the hybrid system and control strategies. As an example of application, a complex PV-wind-diesel-hydrogen-battery system has been designed, obtaining a set of possible solutions (Pareto Set). The results achieved demonstrate the practical utility of the developed design method. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： hybrid systems multi-objective design multi-objective evolutionary algorithms genetic algorithms

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Selection mechanisms based on the maximin fitness function to solve multi-objective optimization problems

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INFORMATION SCIENCES 2016年 332卷 131-152页

作者： Menchaca-Mendez, Adriana Coello Coello, Carlos A. IPN CINVESTAV Dept Comp Mexico City 07300 DF Mexico

In this paper, we study three selection mechanisms based on the maximin fitness function and we propose another one. These selection mechanisms give rise to the following MOEAs: "MC-MOEA", "MD-MOEA", "MH-MOEA" and "MAH-MOEA". We validated them using standard test functions taken from the specialized literature, having from three up to ten objective functions. We compare these four MOEAs among them and also with respect to MOEA/D (which is based on decomposition), and to SMS-EMOA (which is based on the hypervolume indicator). Our preliminary results indicate that "MD-MOEA" and "MAH-MOEA" are promising alternatives for solving MOPs with either low or high dimensionality. (C) 2015 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithms Selection operators Maximin fitness function

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A genetic-based framework for solving (multi-criteria) weighted matching problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2003年第1期149卷 77-101页

作者： Medaglia, AL Fang, SC N Carolina State Univ Dept Ind Engn Raleigh NC 27695 USA Univ Los Andes Fac Ingn Dept Ingn Ind Bogota AA 4976 Colombia N Carolina State Univ Grad Program Operat Res Raleigh NC 27695 USA

The purpose of this paper is to present a flexible genetic-based framework for solving the multi-criteria weighted matching problem (mc-WMP). In the first part of this paper, we design a genetic-based framework for solving the ordinary weighted matching problem. We present an extensive analysis of the quality of the results and introduce a methodology for tuning its parameters. In the second part, we develop a modified genetic-based algorithm for solving the mc-WMP. The algorithm generates a significant and representative portion of the Pareto optimal set. To assess the performance of the algorithm, we conduct computational experiments with two and three criteria. The potential of the proposed aligorithm is demonstrated by comparing to a multi-objective simulated annealing algorithm. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： genetic algorithms multi-objective evolutionary algorithms multi-criteria optimization weighted matching

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An Investigation into Many-objective Optimization Problems: A Case Study of the Dial-a-Ride Problem

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IEEE LATIN AMERICA TRANSACTIONS 2022年第1期20卷 73-81页

作者： Viana, Renan J. dos S. Martins, Flavio V. Cruzeiro Wanner, Elizabeth F. CEFET MG Programa Pos Grad Modelagem Matemat & Computac Belo Horizonte MG Brazil CEFET MG Dept Comp Belo Horizonte MG Brazil

multi-objective optimization problems with more than three objectives are commonly referred to as many-objective optimization problems. Usually, this class of problem brings new and complex challenges to the current optimization methods, mainly maintaining the right balance between convergence and diversity. During the last years, various approaches have been proposed to solve many-objective problems. However, most existing experimental comparative studies are restricted to continuous problems. Few studies have encompassed the most recently proposed state-of-the-art approaches and made an experimental comparison applied to combinatorial optimization problems. Aiming to fill this gap, this paper presents a comparative analysis with eight algorithms covering various categories to solve a many-objective Dial-a-Ride problem. The results show that different observations can be made about the algorithms' behavior when using different test sets. Also, algorithms originally proposed to deal with problems with up to three objectives have overcome recently proposed ones.

关键词： Many-objective optimization dial-a-ride problem multi-objective evolutionary algorithms combinatorial optimization

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Two-archive method for aggregation-based many-objective optimization

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INFORMATION SCIENCES 2018年 422卷 305-317页

作者： Cai, Lei Qu, Shiru Cheng, Guojian Xian Univ Technol Fac Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Xian Shiyou Univ Sch Comp Sci Xian 710065 Shaanxi Peoples R China

In this paper, a novel two-archive method is proposed for solving many-objective optimization problems. Our aim is to exploit the advantages of using two separate archives to balance the convergence and diversity. To this end, two updating strategies based on the aggregation-based framework are presented and incorporated into the two-archive method. In addition, we further extend this method by eliminating the restricted neighbourhood models. The proposed algorithms have been tested extensively on a number of well-known benchmark problems with 3-20 objectives. Experimental results reveal that the proposed algorithms work well on the many-objective optimization problems with different characteristics. (C) 2017 Elsevier Inc. All rights reserved.

关键词： evolutionary computation multi-objective evolutionary algorithms Many-objective optimization Two archives Aggregation-based method

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Compact modeling of the effects of illumination on the contact region of organic phototransistors

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ORGANIC ELECTRONICS 2019年 70卷 113-121页

作者： Romero, A. Jimenez, C. Gonzalez, J. Lopez-Varo, P. Deen, M. J. Jimenez-Tejada, J. A. Univ Granada CITIC UGR Dept Arquitectura & Tecnol Computadores E-18071 Granada Spain Univ Granada Dept Electron & Tecnol Computadores E-18071 Granada Spain Inst Photovoltaique Ile de France 30 RD 128 F-91120 Palaiseav France McMaster Univ Dept Elect & Comp Engn 1280 Main St West Hamilton ON L8S 4K1 Canada

A good modeling of degrading effects in an electronic device, such as the contact region of organic phototransistors (OPTs), can be favorably used to better describe and optimize the performance of the whole device. Furthermore, a proper design of the contacts can enhance the exciton dissociation and the extraction of photogenerated charge in the device. In this work, a compact model for OPTs is developed. This model is valid for all the operation regimes of the transistors. It includes a model for the contact region of the device that incorporates the effects of illumination. The compact model and the contact region model are validated with published experimental data from several OPTs under different illumination conditions. The tool used to validate the model is an evolutionary parameter extraction procedure developed in a previous work. The results show that both photoconductive and photovoltaic effects impact the intrinsic region of the transistor, as well as the electrical behavior of the contact region. The parameters used in the contact region model are linked to these photovoltaic and photoconductive effects.

关键词： Phototransistors Thin-film transistor Device parameters Modeling Contact effects Many-objective optimization multi-objective evolutionary algorithms Photovoltaic effect Photoconductive effect

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Solving multi-objective truss structural optimization problems considering natural frequencies of vibration and automatic member grouping

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evolutionary INTELLIGENCE 2024年第2期17卷 653-678页

作者： Carvalho, Erica C. R. Carvalho, Jose Pedro G. Bernardino, Heder S. Lemonge, Afonso C. C. Hallak, Patricia H. Vargas, Denis E. C. Univ Fed Juiz de Fora Grad Program Computat Modeling Juiz De Fora Brazil Univ Fed Rio de Janeiro Civil Engn Program COPPE Rio De Janeiro Brazil Univ Fed Juiz de Fora Inst Exact Sci Dept Comp Sci Juiz De Fora Brazil Univ Fed Juiz de Fora Sch Engn Dept Appl & Computat Mech Juiz De Fora Brazil Fed Ctr Technol Educ Minas Gerais Dept Math Belo Horizonte Brazil

Minimizing the mass of a structure and maximizing its first natural frequency of vibration are conflicting objectives of real interest in structural design. To avoid problems with resonance, which can lead to their collapse, structures can be designed by making the first natural frequency of vibration high. Furthermore, it is crucial to stay away from excitation frequencies. Here we formulate and solve multi-objective structural optimization problems of trusses with these conflicting objectives. This type of problem is uncommon in the literature, since the natural frequencies of vibration are generally set as constraints rather than as objective functions. The generalized differential evolution 3 (GDE3), the nondominated sorting genetic algorithm II (NSGA-II), decision space-based niching (DN-NSGA-II), the competitive mechanism-based multi-objective particle swarm optimizer (CMOPSO), and the MOPSO with multiple search strategies (MMOPSO) are the algorithms used in this paper. Additionally, cardinality constraints are used to manage the difficulty of discovering the best member grouping, which is very effective in addressing the problems analyzed in this paper. The experiments refer to the 10-, 25-, 72-, and 200-bar trusses. Each involves two analyses, taking into account the performance indicators and the use of a multi tournament decision (MTD) method to extract the desired solutions. Furthermore, the design variables of each extracted solution, including its optimized topology, are provided.

关键词： multi-objective truss structural optimization multi-objective evolutionary algorithms Natural frequencies of vibration Cardinality constraints Automatic member grouping

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Set-based concept selection in multi-objective problems: optimality versus variability approach

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JOURNAL OF ENGINEERING DESIGN 2009年第3期20卷 217-242页

作者： Avigad, Gideon Moshaiov, Amiram ORT Braude Coll Engn Dept Mech Engn Karmiel Israel Tel Aviv Univ Dept Mech Engn IL-69978 Tel Aviv Israel

This paper presents a novel approach to support the selection of conceptual solutions to multi-objective problems. The proposed method involves a comparison between concepts, based on the performances of sets of solutions that represent them. The set-based comparison of concepts is consistent with the so-called Toyota set-based concurrent engineering process. Such an approach discourages early exploitation of solutions and promotes extended exploration of the design space by means of sets of solutions. Both optimality and variability of concepts are considered, and their measures are devised to pose the selection problem as an auxiliary multi-objective problem. The auxiliary objectives are to maximise optimality and to maximise the variability. This highlights the inherent multi-objectivity of concept selection and supports decision-making under the possible contradictory nature of optimality and variability of concepts. Both academic and engineering problems are used to demonstrate the approach and to expose the inherent subjectivity of the measures, which are dependent on the selection of a window of interest by the decision-makers.

关键词： multi-objective optimisation multi-criteria decision-making set-based measures multi-objective evolutionary algorithms conceptual design

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Energy System Diagnosis by a Fuzzy Expert System with Genetically Evolved Rules

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INTERNATIONAL JOURNAL OF THERMODYNAMICS 2008年第3期11卷 115-121页

作者： Toffolo, Andrea Lazzaretto, Andrea Univ Padua Dept Mech Engn Via Venezia 1 I-35131 Padua Italy

The problem of malfunction diagnosis in energy systems can be approached using an expert system which compares the experimental data measured by the plant acquisition system and the calculated data evaluated by a plant simulator under the same operating conditions. In this paper the rules that form the "knowledge base" of the expert system are not assigned heuristically by trying to code the expertise of plant personnel, as it is usually done, but they are artificially and randomly generated by the recombination and selection operators of an evolutionary algorithm. A two-objective optimization problem is set up, in order to search for the optimal sets of rules having the minimum complexity but simultaneously maximizing the number of correct fault identifications for a given set of malfunctioning operating conditions. A global and a local approach are applied to a real test case, a two-shaft gas turbine used as the gas section of a combined-cycle cogeneration plant, in order to evaluate the potentialities and the limits of this methodology.

关键词： Diagnosis fuzzy expert system multi-objective evolutionary algorithms

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