A hybrid evolutionary approach to the registration area planning problem

2014 ◽  
Vol 41 (4) ◽  
pp. 1127-1149 ◽  
Author(s):  
Sachchida Nand Chaurasia ◽  
Alok Singh
Keyword(s):  
Evolutionary Approach ◽  
Planning Problem ◽  
Area Planning ◽  
Registration Area

A hybrid grouping genetic algorithm for the registration area planning problem

2007 ◽  
Vol 30 (10) ◽  
pp. 2180-2190 ◽  
Author(s):  
Tabitha James ◽  
Mark Vroblefski ◽  
Quinton Nottingham
Keyword(s):  
Genetic Algorithm ◽  
Planning Problem ◽  
Grouping Genetic Algorithm ◽  
Area Planning ◽  
Registration Area

Registration area planning for PCS networks using genetic algorithms

1998 ◽  
Vol 47 (3) ◽  
pp. 987-995 ◽  
Author(s):  
Tsan-Pin Wang ◽  
Shu-Yuen Hwang ◽  
Chien-Chao Tseng
Keyword(s):  
Genetic Algorithms ◽  
Pcs Networks ◽  
Area Planning ◽  
Registration Area

A grouping genetic algorithm for registration area planning

Omega ◽  
2006 ◽  
Vol 34 (3) ◽  
pp. 220-230 ◽  
Author(s):  
Mark Vroblefski ◽  
Evelyn C. Brown
Keyword(s):  
Genetic Algorithm ◽  
Grouping Genetic Algorithm ◽  
Area Planning ◽  
Registration Area

scholarly journals Optimizing household energy planning in smart cities: A multiobjective approach

2020 ◽  
Author(s):  
Sergio Nesmachnow ◽  
Giovanni Colacurcio ◽  
Diego Gabriel Rossit ◽  
Jamal Toutouh ◽  
Francisco Luna
Keyword(s):  
Recommendation System ◽  
Smart Cities ◽  
User Preferences ◽  
Point Of View ◽  
Energy Utilization ◽  
Evolutionary Approach ◽  
Energy Planning ◽  
Planning Problem ◽  
Usual Model ◽  
Multiobjective Approach

This article presents the advances in the design and implementation of a recommendation system for planning the use of household appliances, focused on improving energy efficiency from the point of view of both energy companies and end-users. The system proposes using historical information and data from sensors to define instances of the planning problem considering user preferences, which in turn are proposed to be solved using a multiobjective evolutionary approach, in order to minimize energy consumption and maximize quality of service offered to users. Promising results are reported on realistic instances of the problem, compared with situations where no intelligent energy planning are used (i.e., ‘Bussiness as Usual’ model) and also with a greedy algorithm developed in the framework of the reference project. The proposed evolutionary approach was able to improve up to 29.0% in energy utilization and up to 65.3% in user preferences over the reference methods.

Modeling the Tracking Area Planning Problem Using an Evolutionary Multi-Objective Algorithm

2017 ◽  
Vol 12 (1) ◽  
pp. 29-41 ◽  
Author(s):  
Lei Chen ◽  
Hai-Lin Liu ◽  
Zhun Fan ◽  
Shengli Xie ◽  
Erik D. Goodman
Keyword(s):  
Planning Problem ◽  
Multi Objective ◽  
Area Planning
Sign in / Sign up

Export Citation Format

Share Document