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Sidhartha Panda - One of the best experts on this subject based on the ideXlab platform.

  • multi objective pid controller tuning for a facts based damping stabilizer using non dominated sorting genetic algorithm ii
    International Journal of Electrical Power & Energy Systems, 2011
    Co-Authors: Sidhartha Panda
    Abstract:

    Abstract Design of an optimal controller requires optimization of multiple performance measures that are often noncommensurable and competing with each other. Design of such a controller is indeed a multi-objective optimization problem. Non-Dominated Sorting in Genetic Algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems. This paper investigates the application of NSGA-II technique for the tuning of a Proportional Integral Derivate (PID) controller for a Flexible AC Transmission System (FACTS)-based stabilizer. The design objective is to improve the damping of power system when subjected to a disturbance with minimum control effort. The proposed technique is applied to generate Pareto set of global optimal Solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise Solution from the obtained Pareto Solution set. Simulation results are presented and compared with a conventionally designed PID controller under various loading conditions and disturbances to show the effectiveness and robustness of the proposed approach. Finally, the proposed design approach is extended to a multi-machine power system to damp the modal oscillations with minimum control efforts.

  • application of non dominated sorting genetic algorithm ii technique for optimal facts based controller design
    Journal of The Franklin Institute-engineering and Applied Mathematics, 2010
    Co-Authors: Sidhartha Panda
    Abstract:

    Design of an optimal controller requires optimization of multiple performance measures that are often noncommensurable and competing with each other. Design of such a controller is indeed a multi-objective optimization problem. Non-dominated sorting in genetic algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems. This paper investigates the application of NSGA-II technique for the design of a flexible AC transmission system (FACTS)-based controller. The design objective is to improve the stability of the power system with minimum control effort. The proposed technique is applied to generate Pareto set of global optimal Solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise Solution from the obtained Pareto Solution set. Further, a detailed analysis on the selection of control signals (both local and remote signals) on the effectiveness of the proposed controller is carried out and simulation results are presented under various loading conditions and disturbances to show the effectiveness and robustness of the proposed approach.

  • multi objective evolutionary algorithm for sssc based controller design
    Electric Power Systems Research, 2009
    Co-Authors: Sidhartha Panda
    Abstract:

    In this paper, an evolutionary multi-objective optimization approach is employed to design a static synchronous series compensator (SSSC)-based controller. The design objective is to improve the transient performance of a power system subjected to a severe disturbance by damping the multi-modal oscillations namely; local mode, inter-area mode and inter-plant mode. A genetic algorithm (GA)-based Solution technique is applied to generate a Pareto set of global optimal Solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise Solution from the obtained Pareto Solution set. Simulation results are presented and compared with a PI controller under various disturbances namely; three-phase fault, line outage, loss of load and unbalanced faults to show the effectiveness and robustness of the proposed approach.

  • A multi-objective GA method for generating Pareto Solutions for coordinated design of PSS and TCSC
    International Journal of Intelligent Systems Technologies and Applications, 2009
    Co-Authors: Sidhartha Panda, Narayana Prasad Padhy, R. N. Patel
    Abstract:

    This paper presents a Multi-objective Genetic Algorithm (MGA) approach to generate Pareto Solution set for coordinated design of a Power System Stabiliser (PSS) and a Flexible AC Transmission System (FACTS) controller. The design objective is minimisation of both angle and voltage time trajectory deviations with respect to a post-contingency equilibrium point for a power system installed with a PSS and a FACTS controller. The optimal controller parameters are coordinately determined in a generator-infinite-bus test system. Simulation results are presented to show the effectiveness of the proposed approach in damping the power system oscillations and improving the system voltage profile.

Chenqi Liu - One of the best experts on this subject based on the ideXlab platform.

  • Pareto and Niche Genetic Algorithm for Storage Location Assignment Optimization Problem
    2008 3rd International Conference on Innovative Computing Information and Control, 2008
    Co-Authors: Xuebo Chen, Chenqi Liu
    Abstract:

    Class-based storage and storage location assignment implementation decisions have significant impact on the required storage space and product picking efficiency in an automated warehouse. A multiobjective mathematical model was proposed for storage location assignment to capture the above. The rack stability and order picking frequency were incorporated based on the class strategy. A genetic algorithm with Pareto-optimization and niche technique was developed to solve the problem. The algorithm included two arithmetic operators: Pareto Solution sets filter and niche technique besides selection, crossover and mutation operators. Computational experience with randomly generated data sets and an industrial case shows that the policies are more effective than class-based storage policy only, and enhance the operational efficiency of an automated storage/retrieval system, as well as a CIMS system. The improved genetic algorithm can be applied to handle large real life problems efficiently.

Olarn Wongwirat - One of the best experts on this subject based on the ideXlab platform.

  • searching optimization route by using Pareto Solution with ant algorithm for mobile robot in rough terrain environment
    International Conference on Control Automation Robotics and Vision, 2016
    Co-Authors: Anuntapat Anuntachai, Arit Thammano, Olarn Wongwirat
    Abstract:

    A problem related to searching routes of a mobile robot involves finding the route that has the shortest distance and consumes the least energy, or an energy-efficient route. An ant colony optimization (ACO) algorithm can be used to solve this problem, but only on a flat terrain, since energy is depended on the distance. The adapted ACO can also be applied for searching the energy-efficient routes in the rough terrain, but it is difficult to achieve both criteria, simultaneously. In the rough terrain, the least energy route might not have the shortest distance. Also, the route having the shortest distance might not consume the least energy. In this scenario, an optimized route is required. This paper proposes a method to find the optimized route of a mobile robot in terms of energy and distance on the rough terrain by using a Pareto Solution with adapted ACO algorithm. In the proposed method, the adapted ACO is applied for searching a set of routes that consumes the least energy. Then, the Pareto Solution is deployed to find the optimized route in terms of energy and distance. The experiment was conducted by simulation to verify the proposed searching method. The experimental result shows that the optimized route having appropriate energy and distance can be found. It can be implied that this optimized route is the energy-efficient route in rough terrain environment.

  • ICARCV - Searching optimization route by using Pareto Solution with ant algorithm for mobile robot in rough terrain environment
    2016 14th International Conference on Control Automation Robotics and Vision (ICARCV), 2016
    Co-Authors: Anuntapat Anuntachai, Arit Thammano, Olarn Wongwirat
    Abstract:

    A problem related to searching routes of a mobile robot involves finding the route that has the shortest distance and consumes the least energy, or an energy-efficient route. An ant colony optimization (ACO) algorithm can be used to solve this problem, but only on a flat terrain, since energy is depended on the distance. The adapted ACO can also be applied for searching the energy-efficient routes in the rough terrain, but it is difficult to achieve both criteria, simultaneously. In the rough terrain, the least energy route might not have the shortest distance. Also, the route having the shortest distance might not consume the least energy. In this scenario, an optimized route is required. This paper proposes a method to find the optimized route of a mobile robot in terms of energy and distance on the rough terrain by using a Pareto Solution with adapted ACO algorithm. In the proposed method, the adapted ACO is applied for searching a set of routes that consumes the least energy. Then, the Pareto Solution is deployed to find the optimized route in terms of energy and distance. The experiment was conducted by simulation to verify the proposed searching method. The experimental result shows that the optimized route having appropriate energy and distance can be found. It can be implied that this optimized route is the energy-efficient route in rough terrain environment.

  • An application of Pareto Solution with adapted ACO for searching optimal route of a mobile robot in rough terrain environment
    2016 16th International Conference on Control Automation and Systems (ICCAS), 2016
    Co-Authors: Anuntapat Anuntachai, Arit Thammano, Olarn Wongwirat
    Abstract:

    A challenge in searching an optimal route of a mobile robot involves finding the route that has the shortest distance and consumes the least energy. To solve this problem, an ant colony optimization (ACO) algorithm can be used, but only on a flat terrain, since the energy depends directly on the distance. In a rough terrain, the least energy route might not be the shortest distance. Also, the shortest distance route might not be the least energy. This is due to a factor of slope in the route. Although our adapted ACO can be used for searching energy-efficient routes in the rough terrain, it is difficult to achieve the shortest distance simultaneously. This paper proposes a novel method to find an optimal route of a mobile robot in rough terrain environment by using a Pareto Solution with adapted ACO. In the proposed method, the adapted ACO is used to search two sets of route, i.e., one contains the least energy and another one contains the shortest distance. Then, the Pareto Solution is deployed to find the optimal route in terms of energy and distance by adopting a distance vector for selection. The experiment was performed by simulation to verify the proposed searching method. The experimental results show that the proposed searching method can prescribe the optimal value for choosing the route provided by adapted ACO.

Faouzi Masmoudi - One of the best experts on this subject based on the ideXlab platform.

  • Pareto optimal Solution selection for a multi site supply chain planning problem using the vikor and topsis methods
    International Journal of Service Science Management Engineering and Technology, 2017
    Co-Authors: Houssem Felfel, Omar Ayadi, Faouzi Masmoudi
    Abstract:

    In this paper, a multi-objective, multi-product, multi-period production and transportation planning problem in the context of a multi-site supply chain is proposed. The developed model attempts simultaneously to maximize the profit and to maximize the product quality level. The objective of this paper is to provide the decision maker with a front of Pareto optimal Solutions and to help him to select the best Pareto Solution. To do so, the epsilon-constraint method is adopted to generate the set of Pareto optimal Solutions. Then, the technique for order preference by similarity to ideal Solution (TOSIS) is used to choose the best compromise Solution. The multi-criteria optimization and compromise Solution (VIKOR), a commonly used method in multiple criteria analysis, is applied in order to evaluate the selected Solutions using TOPSIS method. This paper offers a numerical example to illustrate the Solution approach and to compare the obtained results using TOSIS and VIKOR methods.

  • integrated ahp topsis approach for Pareto optimal Solution selection in multi site supply chain planning
    International Conference Design and Modeling of Mechanical Systems, 2017
    Co-Authors: Houssem Felfel, Faouzi Masmoudi
    Abstract:

    In this paper, a multi-objective, multi-period, multi-product stochastic model for a multi-site supply chain planning problem under demand uncertainty is proposed. The decisions to be made include the amounts of product to be produced, the amounts of products to be transported between the different sites and customers as well as the amounts of inventory of finished or semi-finished products. The developed model aims simultaneously to minimize the expected total cost, to maximize the customer demand satisfaction level and to minimize the downside risk. The e-constraint method is applied to solve the considered model and to generate the set of Pareto optimal Solutions. This set of Pareto represents the trade-off between the different objective functions. Then, an integrated approach of the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is applied in order to select the best compromise Pareto Solution. A numerical example is presented to illustrate the proposed approach.

Xuebo Chen - One of the best experts on this subject based on the ideXlab platform.

  • Pareto and Niche Genetic Algorithm for Storage Location Assignment Optimization Problem
    2008 3rd International Conference on Innovative Computing Information and Control, 2008
    Co-Authors: Xuebo Chen, Chenqi Liu
    Abstract:

    Class-based storage and storage location assignment implementation decisions have significant impact on the required storage space and product picking efficiency in an automated warehouse. A multiobjective mathematical model was proposed for storage location assignment to capture the above. The rack stability and order picking frequency were incorporated based on the class strategy. A genetic algorithm with Pareto-optimization and niche technique was developed to solve the problem. The algorithm included two arithmetic operators: Pareto Solution sets filter and niche technique besides selection, crossover and mutation operators. Computational experience with randomly generated data sets and an industrial case shows that the policies are more effective than class-based storage policy only, and enhance the operational efficiency of an automated storage/retrieval system, as well as a CIMS system. The improved genetic algorithm can be applied to handle large real life problems efficiently.