This paper presents two algorithms, which are a nondominated sorting genetic algorithm ii (NSGA-ii) and an indicator-based multi-objective local search (IBMOLS), for solving a bi-objective p-Median problem. The bi-obj...
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ISBN:
(纸本)9781467369022
This paper presents two algorithms, which are a nondominated sorting genetic algorithm ii (NSGA-ii) and an indicator-based multi-objective local search (IBMOLS), for solving a bi-objective p-Median problem. The bi-objective p-Median problem is a problem of finding p location points to install facilities from a set of m candidates. This problem considers two objectives: minimizing the sum of the distances from each customer to the nearest facility and minimizing the sum of the costs to install each facility in the selected location points. NSGA-ii and IBMOLS are efficient algorithms in the area of multi-objective optimization. Experiments are conducted on generated instances. Hypervolume values of the approximate Pareto fronts are computed and the obtained results from IBMOLS and NSGA-ii are compared.
This paper presents a novel design procedure for the coordinated tuning of rotor side converter (RSC) and grid side converter (GSC) controllers of doubly fed induction generator (DFIG) wind turbine system. The RSC and...
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This paper presents a novel design procedure for the coordinated tuning of rotor side converter (RSC) and grid side converter (GSC) controllers of doubly fed induction generator (DFIG) wind turbine system. The RSC and GSC controller parameters are determined by simultaneously optimizing the controller performance indices. The performance indices considered are maximum peak overshoot (MPOSx), settling time (Tssx) of the generator speed and the maximum peak overshoot (MPOSVdc), maximum peak undershoot (MPUSVdc) and settling time (TssVdc) of DC link voltage. The coordinated controller design is carried out in two steps. First step is to arrive at the analytical expression that relates the performance indices and the controller parameters. This is achieved using response surface methodology (RSM) thereby saving significant computational time. In the second step the determination of controller parameters is posed as a constrained multiobjective optimization problem. The constrained multiobjective optimization problem is solved using NSGAii (nondominated sorting genetic algorithm ii). The proposed methodology is tested on a sample system with DFIG based WECS. Simulation results demonstrate the effectiveness of the proposed methodology. (C) 2014 Elsevier Ltd. All rights reserved.
Coupling matrix synthesis technique based on multi-objective evolutionary algorithm (MOEA) is proposed for pseudoelliptic low-pass filter prototypes with lossy resonators and arbitrary topology. MOEA-based synthesis c...
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Coupling matrix synthesis technique based on multi-objective evolutionary algorithm (MOEA) is proposed for pseudoelliptic low-pass filter prototypes with lossy resonators and arbitrary topology. MOEA-based synthesis can deal with multi-objective functions so that different filter characteristics of high-performance lossy filter, such as return loss, out of band rejection and passband flatness, can be optimised simultaneously. In the proposed MOEA-based lossy filter synthesis, a chromosome pair coding model is defined to treat real and imaginary parts of complex coupling matrix of a lossy filter, and additional constraints on Q-factor to characterise lossy resonator in the filter are applied. For demonstration, non-dominated sortinggeneticalgorithmii with extended arithmetic crossover and adaptive mutation operator are used to synthesise three symmetric/asymmetric lossy filters. The results indicate that the proposed MOEA-based synthesis technique can provide perfect passband ripple as required in high-performance microwave filters.
A method for the optimization of sensor locations in water distribution networks is presented with respect to effective and efficient detection of contaminations. The optimization problem is formulated as a twin-objec...
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ISBN:
(纸本)9781424453634
A method for the optimization of sensor locations in water distribution networks is presented with respect to effective and efficient detection of contaminations. The optimization problem is formulated as a twin-objective minimization problem with the objectives being the sensor cost and the risk of contamination. Unlike past approaches, the risk of contamination is explicitly evaluated as the product of the non-detection probability of an intrusion by a given set of sensors and the consequence of that failure (expressed as effected population). An Importance-based Sampling Method is presented and used to effectively determine the relative importance of contamination events, thus reducing the overall computation time. The above problem is solved by using the Non-dominated sortinggeneticalgorithmii (NSGA-ii). The methodology is tested on a case study involving the water distribution system of Almelo (Netherlands) and the potential intrusion of E. coli bacteria. The results obtained show that the algorithm is capable of efficiently solving the above problem. The estimated Pareto front suggests that a reasonable level of contaminant protection can be achieved using a small number of strategically located sensors.
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