نتایج جستجو برای: non dominated sorting genetic algorithm

تعداد نتایج: 2547679  

2010
Jaydev Sharma F. Batrinu E. Carpaneto G. Chicco M. De Donno P. Postolache C. Toader

In this paper, one of the evolutionary algorithm based method, Non-Dominated Sorting Genetic Algorithm (NSGA) has been presented for the Volt / Var control in power distribution systems with dispersed generation (DG). The proposed method is better suited for volt/var control problems. Genetic algorithm approach is used due to its broad applicability, ease of use and high accuracy. A multi-objec...

Journal: :Fundam. Inform. 2008
Ashish Ghosh Mrinal Kanti Das

In this paper a new concept of ranking among the solutions of the same front, along with elite preservation mechanism and ensuring diversity through the nearest neighbor method is proposed for multi-objective genetic algorithms. This algorithm is applied on a set of benchmark multi-objective test problems and the results are compared with that of NSGA-II (a similar algorithm). The proposed algo...

Warranty is a powerful implement for marketing strategy that is used by manufacturersand creates satisfaction for consumers by ensuring to compensate for incorrect operation of the product. Warranty serviceresults ina cost named warranty cost for a manufacturer.This cost is a function of warranty policy, regions, and product failures pattern. Since this service coversthe cost of uncertain failu...

2004
Kuntinee Maneeratana Kittipong Boonlong Nachol Chaiyaratana

This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multiobjective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a nondominated sorting genetic algorithm (NSGA) and a controlled elitist nondominated sorting genetic algorithm (CNSGA). The resulting algori...

This study introduces a green location, routing and inventory problem with customer satisfaction, backup distribution centers and risk of routes in the form of a non-linear mixed integer programming model. In this regard, time window is considered to increase the customer satisfaction of the model and transportation risks is taken into account for the reliability of the system. In addition, dif...

In the facility location problem usually reducing total transferring cost and time are common objectives. Designing of a network with hub facilities can improve network efficiency. In this study a new model is presented for P-hub covering location problem. In the p-hub covering problem it is attempted to locate hubs and allocate customers to established hubs while allocated nodes to hubs are in...

Trip distribution deals with estimation of trips distributed among origins and destinations and is one of the important stages in transportation planning. Since in the real world, trip distribution models often have more than one objective, multi-objective models are developed to cope with a set of conflict goals in this area. In a proposed method of adapted non-dominated sorting algorithm (ANS...

2012
Chih-Hao Lin Pei-Ling Lin

Multi-objective optimization (MO) is a highly demanding research topic because many realworld optimization problems consist of contradictory criteria or objectives. Considering these competing objectives concurrently, a multi-objective optimization problem (MOP) can be formulated as finding the best possible solutions that satisfy these objectives under different tradeoff situations. A family o...

This paper proposes a bi-objective model for the facility location problem under a congestion system. The idea of the model is motivated by applications of locating servers in bank automated teller machines (ATMS), communication networks, and so on. This model can be specifically considered for situations in which fixed service facilities are congested by stochastic demand within queueing frame...

2003
Mario Costa Edmondo A. Minisci

An evolutionary multi-objective optimization tool based on an estimation of distribution algorithm is proposed. The algorithm uses the ranking method of non-dominated sorting genetic algorithm-II and the Parzen estimator to approximate the probability density of solutions lying on the Pareto front. The proposed algorithm has been applied to different types of test case problems and results show...

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