The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Olivier Ladislas De Weck - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Weighted Sum Method for multiobjective optimization: a new Method for Pareto front generation
    Structural and Multidisciplinary Optimization, 2006
    Co-Authors: Olivier Ladislas De Weck
    Abstract:

    This paper presents an adaptive Weighted Sum (AWS) Method for multiobjective optimization problems. The Method extends the previously developed biobjective AWS Method to problems with more than two objective functions. In the first phase, the usual Weighted Sum Method is performed to approximate the Pareto surface quickly, and a mesh of Pareto front patches is identified. Each Pareto front patch is then refined by imposing additional equality constraints that connect the pseudonadir point and the expected Pareto optimal solutions on a piecewise planar hypersurface in the $$ {m} $$ -dimensional objective space. It is demonstrated that the Method produces a well-distributed Pareto front mesh for effective visualization, and that it finds solutions in nonconvex regions. Two numerical examples and a simple structural optimization problem are solved as case studies.

  • Adaptive Weighted-Sum Method for bi-objective optimization: Pareto front generation
    Structural and Multidisciplinary Optimization, 2005
    Co-Authors: I.y. Kim, Olivier Ladislas De Weck
    Abstract:

    This paper presents a new Method that effectively determines a Pareto front for bi-objective optimization with potential application to multiple objectives. A traditional Method for multiobjective optimization is the Weighted-Sum Method, which seeks Pareto optimal solutions one by one by systematically changing the weights among the objective functions. Previous research has shown that this Method often produces poorly distributed solutions along a Pareto front, and that it does not find Pareto optimal solutions in non-convex regions. The proposed adaptive Weighted Sum Method focuses on unexplored regions by changing the weights adaptively rather than by using a priori weight selections and by specifying additional inequality constraints. It is demonstrated that the adaptive Weighted Sum Method produces well-distributed solutions, finds Pareto optimal solutions in non-convex regions, and neglects non-Pareto optimal solutions. This last point can be a potential liability of Normal Boundary Intersection, an otherwise successful multiobjective Method, which is mainly caused by its reliance on equality constraints. The promise of this robust algorithm is demonstrated with two numerical examples and a simple structural optimization problem.

P Venkatesh - One of the best experts on this subject based on the ideXlab platform.

  • glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problem
    Applied Soft Computing, 2014
    Co-Authors: Nelson D Jayakumar, P Venkatesh
    Abstract:

    A new glowworm swarm optimization (GSO) algorithm is proposed to find the optimal solution for multiple objective environmental economic dispatch (MOEED) problem. In this proposed approach, technique for order preference similar to an ideal solution (TOPSIS) is employed as an overall fitness ranking tool to evaluate the multiple objectives simultaneously. In addition, a time varying step size is incorporated in the GSO algorithm to get better performance. Finally, to evaluate the feasibility and effectiveness of the proposed combination of GSO algorithm with TOPSIS (GSO-T) approach is examined in four different test cases. Simulation results have revealed the capabilities of the proposed GSO-T approach to find the optimal solution for MOEED problem. The comparison with own coded Weighted Sum Method incorporated GSO (WGSO) and other Methods reported in literatures exhibit the superiority of the proposed GSO-T approach and also the results confirm the potential of the proposed GSO-T approach to solve the MOEED problem.

  • multi objective evolutionary programming for economic emission dispatch problem
    Power and Energy Society General Meeting, 2008
    Co-Authors: P Venkatesh
    Abstract:

    This paper describes a new multi-objective evolutionary programming (MOEP) Method to solve the combined economic emission dispatch (CEED) and economic emission dispatch (EED) problems. The CEED is a bi-objective optimization problem that considers two objectives such as fuel cost and NOx emission. It is converted into a single objective optimization problem using Weighted Sum Method. The EED is a three-objective optimization problem that considers the fuel cost, NOx and SO2 emissions as objectives. Non-dominated solution ranking is employed as selection mechanism in the proposed MOEP for the CEED and EED problems. The developed algorithm is tested for a three-unit and a six-unit systems, and six and 30 bus systems. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto optimal solutions of the multi-objective problems in a single run.

  • multi objective evolutionary programming for economic emission dispatch problem
    Power and Energy Society General Meeting, 2008
    Co-Authors: P Venkatesh, Kwang Y Lee
    Abstract:

    This paper describes a new multi-objective evolutionary programming (MOEP) Method to solve the combined economic emission dispatch (CEED) and economic emission dispatch (EED) problems. The CEED is a bi-objective optimization problem that considers two objectives such as fuel cost and NOx emission. It is converted into a single objective optimization problem using Weighted Sum Method. The EED is a three-objective optimization problem that considers the fuel cost, NOx and SO2 emissions as objectives. Non-dominated solution ranking is employed as selection mechanism in the proposed MOEP for the CEED and EED problems. The developed algorithm is tested for a three-unit and a six-unit systems, and six and 30 bus systems. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto optimal solutions of the multi-objective problems in a single run.

  • application of multi objective evolutionary programming to combined economic emission dispatch problem
    International Joint Conference on Neural Network, 2007
    Co-Authors: D N Jeyakumar, P Venkatesh
    Abstract:

    This paper describes a new multi-objective evolutionary programming (MOEP) Method to solve the combined economic emission dispatch (CEED) problem. CEED is a multi-objective optimization problem by considering the fuel cost and emission as the objectives. It is converted into single objective optimization problem using Weighted Sum Method. Hence the MOEP is proposed by employing the non-dominated solution ranking as selection mechanism for the bi-objective CEED problem. The developed algorithm is tested for a three-unit and a six-unit system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto optimal solutions of the multi-objective CEED problem in a single run.

David Y.h. Pui - One of the best experts on this subject based on the ideXlab platform.

  • Real-time Measurements of the Particle Geometric Surface Area by the Weighted-Sum Method on a University Campus
    Aerosol and Air Quality Research, 2020
    Co-Authors: Leo N.y. Cao, David Y.h. Pui
    Abstract:

    ABSTRACT This study conducted field measurements of the particle geometric surface area (GSA) and number concentrations on a university campus via two real-time approaches: applying the Weighted-Sum (WS) Method and using a Scanning Mobility Particle Sizer (SMPS). The measurements were conducted on 4 subjects: laser printing, 3D printing, machining (waterjet cutting, sanding, and welding), and environmental aerosols. The highest emissions were found with 3D printing and welding; these concentrations were measured in the printer’s enclosure and when the local exhaust ventilation was on, respectively. In general, the two Methods agreed well with each other, with an overall Pearson correlation coefficient of 0.85, although the concentrations constantly fluctuated over a wide range, from 20 to 4 × 104 μm2 cm–3. Since the GSA concentrations reported in this study are the first measurements for some scenarios, our results can serve as a reference for further research as well as for individuals in the vicinity of these emissions.

  • A novel Weighted Sum Method to measure particle geometric surface area in real-time
    Journal of Aerosol Science, 2018
    Co-Authors: Leo N.y. Cao, David Y.h. Pui
    Abstract:

    Abstract This paper reports the development of a novel Method to measure the aerosol geometric surface area (GSA) concentration with a time resolution of a few seconds. In the Method, the commercialized nanoparticle surface area monitor was used and slightly modified. The instrument responses under two different conditions were combined in a Weighted Sum (WS) fashion to correlate with the aerosol GSA concentration. We present the GSA concentration results and comparisons with well-known SMPS data in both laboratory testing and field measurement. For the laboratory testing, the two Methods have a good agreement with a Pearson correlation coefficient of 0.9961; for the field measurements including the indoor and outdoor samplings, both Methods agree well with each other. In addition, the new WS Method is more stable in the clean indoor air and suitable for outdoor environmental sampling with a slight overestimation (125% of SMPS).

Leo N.y. Cao - One of the best experts on this subject based on the ideXlab platform.

  • Real-time Measurements of the Particle Geometric Surface Area by the Weighted-Sum Method on a University Campus
    Aerosol and Air Quality Research, 2020
    Co-Authors: Leo N.y. Cao, David Y.h. Pui
    Abstract:

    ABSTRACT This study conducted field measurements of the particle geometric surface area (GSA) and number concentrations on a university campus via two real-time approaches: applying the Weighted-Sum (WS) Method and using a Scanning Mobility Particle Sizer (SMPS). The measurements were conducted on 4 subjects: laser printing, 3D printing, machining (waterjet cutting, sanding, and welding), and environmental aerosols. The highest emissions were found with 3D printing and welding; these concentrations were measured in the printer’s enclosure and when the local exhaust ventilation was on, respectively. In general, the two Methods agreed well with each other, with an overall Pearson correlation coefficient of 0.85, although the concentrations constantly fluctuated over a wide range, from 20 to 4 × 104 μm2 cm–3. Since the GSA concentrations reported in this study are the first measurements for some scenarios, our results can serve as a reference for further research as well as for individuals in the vicinity of these emissions.

  • A novel Weighted Sum Method to measure particle geometric surface area in real-time
    Journal of Aerosol Science, 2018
    Co-Authors: Leo N.y. Cao, David Y.h. Pui
    Abstract:

    Abstract This paper reports the development of a novel Method to measure the aerosol geometric surface area (GSA) concentration with a time resolution of a few seconds. In the Method, the commercialized nanoparticle surface area monitor was used and slightly modified. The instrument responses under two different conditions were combined in a Weighted Sum (WS) fashion to correlate with the aerosol GSA concentration. We present the GSA concentration results and comparisons with well-known SMPS data in both laboratory testing and field measurement. For the laboratory testing, the two Methods have a good agreement with a Pearson correlation coefficient of 0.9961; for the field measurements including the indoor and outdoor samplings, both Methods agree well with each other. In addition, the new WS Method is more stable in the clean indoor air and suitable for outdoor environmental sampling with a slight overestimation (125% of SMPS).

I.y. Kim - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Weighted-Sum Method for bi-objective optimization: Pareto front generation
    Structural and Multidisciplinary Optimization, 2005
    Co-Authors: I.y. Kim, Olivier Ladislas De Weck
    Abstract:

    This paper presents a new Method that effectively determines a Pareto front for bi-objective optimization with potential application to multiple objectives. A traditional Method for multiobjective optimization is the Weighted-Sum Method, which seeks Pareto optimal solutions one by one by systematically changing the weights among the objective functions. Previous research has shown that this Method often produces poorly distributed solutions along a Pareto front, and that it does not find Pareto optimal solutions in non-convex regions. The proposed adaptive Weighted Sum Method focuses on unexplored regions by changing the weights adaptively rather than by using a priori weight selections and by specifying additional inequality constraints. It is demonstrated that the adaptive Weighted Sum Method produces well-distributed solutions, finds Pareto optimal solutions in non-convex regions, and neglects non-Pareto optimal solutions. This last point can be a potential liability of Normal Boundary Intersection, an otherwise successful multiobjective Method, which is mainly caused by its reliance on equality constraints. The promise of this robust algorithm is demonstrated with two numerical examples and a simple structural optimization problem.

  • adaptive Weighted Sum Method for bi objective optimization
    Journal of the Korean Society for Precision Engineering, 2004
    Co-Authors: I.y. Kim, Olivier De Weck
    Abstract:

    This paper presents a new Method that effectively determines a Pareto front for biobjective optimization with potential application to multiple objectives. A traditional Method for multiobjective optimization is the Weighted Sum Method, which seeks Pareto optimal solutions one by one by systematically changing the weights among the objective functions. Previous research has shown that this Method often produces poorly distributed solutions of a Pareto front, and that it does not find Pareto optimal solutions in non-convex regions. The proposed adaptive Weighted Sum Method focuses on unexplored regions by changing the weights adaptively rather than by using a priori weight selections and by specifying additional inequality constraints. It is demonstrated that the adaptive Weighted Sum Method produces well-distributed solutions, finds Pareto optimal solutions in non-convex regions, and neglects non-Pareto optimal solutions. This last point can be a potential liability of Normal Boundary Intersection, an otherwise successful multiobjective Method, which is mainly caused by its reliance on equality constraints. The promise of the algorithm is demonstrated with two numerical examples and a simple structural optimization problem.