نتایج جستجو برای: non dominated sorting genetic algorithm nsga ii

تعداد نتایج: 3021917  

2014
Eric C. van Berkum

The optimization of infrastructure planning in a multimodal network is defined as a multiobjective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train stations and the frequency of public transport lines. For a case study the Pareto set is estimated b...

Journal: :Aerospace 2022

The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. characteristic a laminar separation bubble and an associated drag rise. This paper presents framework for design airfoil. contributions proposed research twofold. First, convolutional neural network (CNN) designed aerodynamic coefficient prediction airfoils. Data generation discu...

Journal: :Energy Systems 2021

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization quantum efficiency maximization. evaluated structures of 15 different designs simulated by varying material types photodiode doping strategies. At first, non-dominated sorting genetic algorithm~II (NSGA-II) produced Pareto-optimal-solutions sets respe...

Over the past two decades, maritime transportation and container traffic worldwide has experienced rapid and continuous growth. With the increase in maritime transportation volume, the issue of greenhouse gas (GHG) emission has become one of the new concerns for port managers. Port managers and government agencies for sustainable development of maritime transportation considered "green ports" t...

The optimal design of a plate-fin recuperator of a 200-kW microturbine was studied in this paper. The exergy efficiency, pressure drop and total cost were selected as the three important objective functions of the recuperator. Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were respectively employed for single-objective and multi-objective optimizations. By opt...

Optimizing gate scheduling at airports is an old, but also a broad problem. The main purpose of this problem is to find an assignment for the flights arriving at and departing from an airport, while satisfying a set of constraints.A closer look at the literature in this research line shows thatin almost all studies airport gate processing time has been considered as a fix parameter. In this res...

Journal: :CoRR 2014
M. Rathna Devi A. Anju

Algorithms developed for scheduling applications on heterogeneous multiprocessor system focus on a single objective such as execution time, cost or total data transmission time. However, if more than one objective (e.g. execution cost and time, which may be in conflict) are considered, then the problem becomes more challenging. This project is proposed to develop a multiobjective scheduling alg...

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...

Journal: :Applied sciences 2021

The grinding product particle size is the most crucial operational index of mineral processes. and consistency directly affects subsequent dressing sintering. In this paper, a novel expert system proposed for guiding operating variables to keep stable with wildly varying ore properties. First, case-based reasoning (CBR) introduced describe whole process historical data experience. Second, gener...

2002
M. Lahanas N. Milickovic D. Baltas N. Zamboglou K. Karouzakis

We compare the efficiency of the NSGA-II algorithm for the brachytherapy dose optimization problem with and without supporting solutions. A local search method enhances the efficiency of the algorithm. In comparison to a fast simulated annealing algorithm the supported hybrid NSGA-II algorithm provides much faster many non-dominated solutions. An archiving of all non-dominated solutions is usef...

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