The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform

Jasbir S. Arora - One of the best experts on this subject based on the ideXlab platform.

  • The weighted sum method for multi-objective optimization: new insights
    Structural and Multidisciplinary Optimization, 2010
    Co-Authors: R. Timothy Marler, Jasbir S. Arora
    Abstract:

    As a common concept in multi-objective optimization, minimizing a weighted sum constitutes an independent method as well as a component of other methods. Consequently, insight into characteristics of the weighted sum method has far reaching implications. However, despite the many published applications for this method and the literature addressing its pitfalls with respect to depicting the Pareto optimal set, there is little comprehensive discussion concerning the conceptual significance of the weights and techniques for maximizing the effectiveness of the method with respect to a Priori Articulation of preferences. Thus, in this paper, we investigate the fundamental significance of the weights in terms of preferences, the Pareto optimal set, and objective-function values. We determine the factors that dictate which solution point results from a particular set of weights. Fundamental deficiencies are identified in terms of a Priori Articulation of preferences, and guidelines are provided to help avoid blind use of the method.

  • Survey of multi-objective optimization methods for engineering
    Structural and Multidisciplinary Optimization, 2004
    Co-Authors: R. Timothy Marler, Jasbir S. Arora
    Abstract:

    A survey of current continuous nonlinear multi-objective optimization ({MOO}) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a Priori Articulation of preferences, methods with a posteriori Articulation of preferences, and methods with no Articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of {MOO} as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.

R. Timothy Marler - One of the best experts on this subject based on the ideXlab platform.

  • The weighted sum method for multi-objective optimization: new insights
    Structural and Multidisciplinary Optimization, 2010
    Co-Authors: R. Timothy Marler, Jasbir S. Arora
    Abstract:

    As a common concept in multi-objective optimization, minimizing a weighted sum constitutes an independent method as well as a component of other methods. Consequently, insight into characteristics of the weighted sum method has far reaching implications. However, despite the many published applications for this method and the literature addressing its pitfalls with respect to depicting the Pareto optimal set, there is little comprehensive discussion concerning the conceptual significance of the weights and techniques for maximizing the effectiveness of the method with respect to a Priori Articulation of preferences. Thus, in this paper, we investigate the fundamental significance of the weights in terms of preferences, the Pareto optimal set, and objective-function values. We determine the factors that dictate which solution point results from a particular set of weights. Fundamental deficiencies are identified in terms of a Priori Articulation of preferences, and guidelines are provided to help avoid blind use of the method.

  • Survey of multi-objective optimization methods for engineering
    Structural and Multidisciplinary Optimization, 2004
    Co-Authors: R. Timothy Marler, Jasbir S. Arora
    Abstract:

    A survey of current continuous nonlinear multi-objective optimization ({MOO}) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a Priori Articulation of preferences, methods with a posteriori Articulation of preferences, and methods with no Articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of {MOO} as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.

Helton Do Nascimento Alves - One of the best experts on this subject based on the ideXlab platform.

  • A Multi-population Hybrid Algorithm to Solve Multi-objective Remote Switches Placement Problem in Distribution Networks
    Journal of Control Automation and Electrical Systems, 2015
    Co-Authors: Helton Do Nascimento Alves
    Abstract:

    This paper presents a multi-population hybrid algorithm to solve the switches placement problem in distribution networks considering remote and manual switches. A genetic algorithm in conjunction with local search procedure is used. In the procedure, reliability index, remote–manual controlled switch and investment costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a Priori Articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented, and the results are compared to the solution given by other techniques. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in distribution feeders.

  • CIES - A Multi-Population Genetic Algorithm to solve multi-objective remote switches allocation problem in distribution networks
    2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), 2014
    Co-Authors: Helton Do Nascimento Alves, Railson Severiano De Sousa
    Abstract:

    This paper presents a Multi-Population Genetic Algorithm to solve the switches allocation problem in electric distribution networks considering remote and manual switches. In the procedure, reliability index, remote or manual controlled switch and investments costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a Priori Articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in electric distribution feeders.

  • A Multi-Population Genetic Algorithm to solve multi-objective remote switches allocation problem in distribution networks
    2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), 2014
    Co-Authors: Helton Do Nascimento Alves, Railson Severiano De Sousa
    Abstract:

    This paper presents a Multi-Population Genetic Algorithm to solve the switches allocation problem in electric distribution networks considering remote and manual switches. In the procedure, reliability index, remote or manual controlled switch and investments costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a Priori Articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in electric distribution feeders.

Railson Severiano De Sousa - One of the best experts on this subject based on the ideXlab platform.

  • CIES - A Multi-Population Genetic Algorithm to solve multi-objective remote switches allocation problem in distribution networks
    2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), 2014
    Co-Authors: Helton Do Nascimento Alves, Railson Severiano De Sousa
    Abstract:

    This paper presents a Multi-Population Genetic Algorithm to solve the switches allocation problem in electric distribution networks considering remote and manual switches. In the procedure, reliability index, remote or manual controlled switch and investments costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a Priori Articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in electric distribution feeders.

  • A Multi-Population Genetic Algorithm to solve multi-objective remote switches allocation problem in distribution networks
    2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), 2014
    Co-Authors: Helton Do Nascimento Alves, Railson Severiano De Sousa
    Abstract:

    This paper presents a Multi-Population Genetic Algorithm to solve the switches allocation problem in electric distribution networks considering remote and manual switches. In the procedure, reliability index, remote or manual controlled switch and investments costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a Priori Articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in electric distribution feeders.

Xun Xu - One of the best experts on this subject based on the ideXlab platform.

  • a weighted rough set based fuzzy axiomatic design approach for the selection of am processes
    The International Journal of Advanced Manufacturing Technology, 2017
    Co-Authors: Pai Zheng, Yuanbin Wang, Xun Xu
    Abstract:

    Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regard to two aspects, preference rating flexibility and performance evaluation objectivity. We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes. An example based on the previous research work of AM machine selection is given to validate its robustness for the Priori Articulation of AM process selection decision support.

  • Selection of additive manufacturing processes
    Rapid Prototyping Journal, 2017
    Co-Authors: Yuanbin Wang, Robert Blache, Xun Xu
    Abstract:

    Purpose This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings All current studies follow a sequential decision process and focus on an “a Priori” Articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” Articulation of preferences approach is proposed to support DfAM and an iterative design process. Originality/value This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

  • Selection of additive manufacturing processes
    Rapid Prototyping Journal, 2017
    Co-Authors: Yuanbin Wang, Robert Blache, Xun Xu
    Abstract:

    Purpose This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings All current studies follow a sequential decision process and focus on an “a Priori” Articulation of preferences approach. This kind of method has limitations for the user in the early design stage ...