Meta-Lamarckian learning in multi-objective optimization for mobile social network search

2018 ◽  
Vol 67 ◽  
pp. 70-93 ◽  
Author(s):  
Andreas Konstantinidis ◽  
Savvas Pericleous ◽  
Christoforos Charalambous
2015 ◽  
Vol 18 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Enrique Campbell ◽  
Joaquín Izquierdo ◽  
Idel Montalvo ◽  
Amilkar Ilaya-Ayza ◽  
Rafael Pérez-García ◽  
...  

A novel methodology to sectorize water supply networks (WSNs) depending on a main transmission line is presented in this paper. The methodology is based on concepts derived from the social network theory and graph theory (namely, community detection and shortest path respectively); and also on a multi-objective optimization process by means of agent swarm optimization (ASO). A series of energy, operative, and economic criteria are optimized in this process. The core idea is to form sectors over the distribution network based on communities found using a community detection algorithm (Walktrap). The methodology is flexible and enables the technical staff in water utilities to make decisions at different stages. It has been tested by generating four feasible solutions over a portion of a real WSN.


2017 ◽  
Vol 10 (5) ◽  
pp. 371
Author(s):  
Arakil Chentoufi ◽  
Abdelhakim El Fatmi ◽  
Molay Ali Bekri ◽  
Said Benhlima ◽  
Mohamed Sabbane

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