The Experts below are selected from a list of 81588 Experts worldwide ranked by ideXlab platform

Evaristo C Biscaia - One of the best experts on this subject based on the ideXlab platform.

  • genetic algorithm development for multi objective optimization of batch free radical polymerization reactors
    Computers & Chemical Engineering, 2003
    Co-Authors: Claudia Lucia Martins Da Silva, Evaristo C Biscaia
    Abstract:

    An improved genetic algorithm approach, based on a new ranking strategy, has been proposed to conduct multi-objective optimization of chemical engineering problems. New operators have been introduced to enhance the algorithm performance and reduce the computational effort. A Pareto-Set Filter operator has been implemented to avoid missing Pareto optimal points during the evolutionary process. A niche operator has been adopted to prevent genetic drift, and an elitism operator, to insure the propagation of the best result of each objective function. A fitness function based on each rank population size and rank level has been used to determine the reproduction ratio. Constraints are handled through a fuzzy penalty function method. The algorithm has been applied to a batch free-radical styrene polymerization process in order to maximize the monomer conversion rate and minimize the concentration of initiator residue in the product. The algorithm proved to be robust, handling satisfactorily multi-modal and multidimensional problems.

Jiazhong Qian - One of the best experts on this subject based on the ideXlab platform.

  • multi objective optimization of long term groundwater monitoring network design using a probabilistic pareto genetic algorithm under uncertainty
    Journal of Hydrology, 2016
    Co-Authors: Qiankun Luo, Yun Yang, Jiazhong Qian
    Abstract:

    Summary Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objectives and substantial uncertainty arising from insufficient hydraulic conductivity ( K ) data. This study develops a new multi-objective simulation–optimization model involving four objectives: minimizations of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for LTGM network design problems. Then a new probabilistic Pareto genetic algorithm (PPGA) coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, is developed to search for the Pareto-optimal solutions to the multi-objective LTGM problems under uncertainty of the K -fields. The PPGA integrates the niched Pareto genetic algorithm with probabilistic Pareto sorting scheme to deal with the uncertainty of objectives caused by the uncertain K -field. Also, the elitist selection strategy, the operation library and the Pareto solution Set Filter are conducted to improve the diversity and reliability of Pareto-optimal solutions by the PPGA. Furthermore, the sampling strategy of noisy genetic algorithm is adopted to cope with the uncertainty of the K -fields and improve the computational efficiency of the PPGA. In particular, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology in finding Pareto-optimal sampling network designs of LTGM systems through a two-dimensional hypothetical example and a three-dimensional field application in Indiana (USA). Comprehensive analysis demonstrates that the proposed PPGA can find Pareto optimal solutions with low variability and high reliability and is a promising tool for optimizing multi-objective LTGM network designs under uncertainty.

Dan Li - One of the best experts on this subject based on the ideXlab platform.

  • multiobjective optimization design with pareto genetic algorithm
    Journal of Structural Engineering-asce, 1997
    Co-Authors: Franklin Y Cheng, Dan Li
    Abstract:

    This paper presents a constrained multiobjective (multicriterion, vector) optimization methodology by integrating a Pareto genetic algorithm (GA) and a fuzzy penalty function method. A Pareto GA generates a Pareto optimal subSet from which a robust and compromise design can be selected. This Pareto GA consists of five basic operators: reproduction, crossover, mutation, niche, and the Pareto-Set Filter. The niche and the Pareto-Set Filter are defined, and fitness for a multiobjective optimization problem is constructed. A fuzzy-logic penalty function method is developed with a combination of deterministic, probabilistic, and vague environments that are consistent with GA operation theory based on randomness and probability. Using this penalty function method, a constrained multiobjective optimization problem is transformed into an unconstrained one. The functions of a point (string, individual) thus transformed contain information on a point's status (feasible or infeasible), position in a search space, and distance from a Pareto optimal Set. Sample cases investigated include a multiobjective integrated structural and control design of a truss, a 72-bar space truss with two criteria, and a four-bar truss with three criteria. Numerical experimental results demonstrate that the proposed method is highly efficient and robust.

Claudia Lucia Martins Da Silva - One of the best experts on this subject based on the ideXlab platform.

  • genetic algorithm development for multi objective optimization of batch free radical polymerization reactors
    Computers & Chemical Engineering, 2003
    Co-Authors: Claudia Lucia Martins Da Silva, Evaristo C Biscaia
    Abstract:

    An improved genetic algorithm approach, based on a new ranking strategy, has been proposed to conduct multi-objective optimization of chemical engineering problems. New operators have been introduced to enhance the algorithm performance and reduce the computational effort. A Pareto-Set Filter operator has been implemented to avoid missing Pareto optimal points during the evolutionary process. A niche operator has been adopted to prevent genetic drift, and an elitism operator, to insure the propagation of the best result of each objective function. A fitness function based on each rank population size and rank level has been used to determine the reproduction ratio. Constraints are handled through a fuzzy penalty function method. The algorithm has been applied to a batch free-radical styrene polymerization process in order to maximize the monomer conversion rate and minimize the concentration of initiator residue in the product. The algorithm proved to be robust, handling satisfactorily multi-modal and multidimensional problems.

Qiankun Luo - One of the best experts on this subject based on the ideXlab platform.

  • multi objective optimization of long term groundwater monitoring network design using a probabilistic pareto genetic algorithm under uncertainty
    Journal of Hydrology, 2016
    Co-Authors: Qiankun Luo, Yun Yang, Jiazhong Qian
    Abstract:

    Summary Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objectives and substantial uncertainty arising from insufficient hydraulic conductivity ( K ) data. This study develops a new multi-objective simulation–optimization model involving four objectives: minimizations of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for LTGM network design problems. Then a new probabilistic Pareto genetic algorithm (PPGA) coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, is developed to search for the Pareto-optimal solutions to the multi-objective LTGM problems under uncertainty of the K -fields. The PPGA integrates the niched Pareto genetic algorithm with probabilistic Pareto sorting scheme to deal with the uncertainty of objectives caused by the uncertain K -field. Also, the elitist selection strategy, the operation library and the Pareto solution Set Filter are conducted to improve the diversity and reliability of Pareto-optimal solutions by the PPGA. Furthermore, the sampling strategy of noisy genetic algorithm is adopted to cope with the uncertainty of the K -fields and improve the computational efficiency of the PPGA. In particular, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology in finding Pareto-optimal sampling network designs of LTGM systems through a two-dimensional hypothetical example and a three-dimensional field application in Indiana (USA). Comprehensive analysis demonstrates that the proposed PPGA can find Pareto optimal solutions with low variability and high reliability and is a promising tool for optimizing multi-objective LTGM network designs under uncertainty.