Results 191 to 200 of about 5,089,183 (236)

Optimization of LDO voltage regulators by NSGA-II

2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2016
Two different low-dropout (LDO) voltage regulators are optimized by applying the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). First, from a sensitivity analysis a set of design variables are selected to establish a reduced chromosome for performing multi-objective optimization by NSGA-II.
Jesus Lopez-Arredondo, Esteban Tlelo-Cuautle, Francisco V. Fernández 0001   +2 more
openaire   +1 more source

Multiobjective Vehicle Routing Optimization With Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II

IEEE transactions on intelligent transportation systems (Print)
This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire
Rixin Wu, Ran Wang, J. Hao, Qiang Wu, Ping Wang, D. Niyato   +5 more
semanticscholar   +1 more source

Parallelization and Optimization of NSGA-II on Sunway TaihuLight System

IEEE Transactions on Parallel and Distributed Systems, 2021
Sunway TaihuLight system is the first supercomputer offering a peak performance over 100 PFlops, which can be utilized to parallelize Non-dominated Sorting Genetic Algorithm II (NSGA-II), a standard approach to multi-objective optimization. However, insufficient off-chip memory bandwidth and limited scratchpad memory capacity of the supercomputer ...
Xin Liu 0081, Jun Sun, Lin Zheng, Su Wang 0005, Yao Liu 0017, Tongquan Wei   +5 more
openaire   +1 more source

A Hybrid NSGA-II for Matching Biomedical Ontology

2018
Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called ...
Xingsi Xue, Jie Chen, Junfeng Chen, Dongxu Chen   +3 more
openaire   +1 more source

Who's better? PESA or NSGA II?

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007
According to the no free lunch (NFL) theorems all black-box algorithms perform equally well when compared over the entire set of optimization problems. An important problem related to NFL is finding a test problem for which a given algorithm is better than another given algorithm.
Laura Diosan, Mihai Oltean
openaire   +1 more source

Applying NSGA-II for Solving the Ontology Alignment Problem

2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013
Achieving semantic interoperability is an essential task for all distributed and open knowledge based systems. Currently, the best technology recognized for fulfilling this complex task is represented by ontologies. Unfortunately, in turn, the power of ontological representation is reduced by the semantic heterogeneity problem which affects two ...
Giovanni Acampora, Uzay Kaymak, Vincenzo Loia, Autilia Vitiello   +3 more
openaire   +4 more sources

Multi-objective Feature Selection with NSGA II

2007
This paper deals with the multi-objective definition of the feature selection problem for different pattern recognition domains. We use NSGA II the latest multi-objective algorithm developed for resolving problems of multi-objective aspects with more accuracy and a high convergence speed.
Tarek M. Hamdani, Jin-Myung Won, Adel M. Alimi, Fakhri Karray   +3 more
openaire   +1 more source

Alleviate the Hypervolume Degeneration Problem of NSGA-II

2011
A number of multiobjective evolutionary algorithms, together with numerous performance measures, have been proposed during past decades. One measure that has been popular recently is the hypervolume measure, which has several theoretical advantages. However, the well-known nondominated sorting genetic algorithm II (NSGA-II) shows a fluctuation or even ...
Fei Peng, Ke Tang 0001
openaire   +1 more source

Improving the NSGA-II Performance with an External Population

2015
The NSGA-II algorithm is among the best performing ones in the area of multiobjective optimization. The classic version of this algorithm does not utilize any external population. In this work several techniques of reintroducing specimens from the external population back to the main one are proposed.
openaire   +1 more source

AP-NSGA-II: An Evolutionary Multi-objective Optimization Algorithm Using Average-Point-Based NSGA-II

2014
Multi-objective optimization involves optimizing a number of objectives simultaneously, and it becomes challenging when the objectives conflict each other, i.e., the optimal solution of one objective function is different from that of other. These problems give rise to a set of trade-off optimal solutions, popularly known as Pareto-optimal solution ...
Prabhujit Mohapatra, Santanu Roy
openaire   +1 more source