scholarly journals Inverse Analysis in Civil Engineering: Applications to Identification of Parameters and Design of Structural Material Using Mono or Multi-Objective Particle Swarm Optimization

10.5772/39077 ◽  
2012 ◽  
Author(s):  
M. Fontan ◽  
A. Ndiaye ◽  
D. Breysse ◽  
P. Castr
Keyword(s):  
Particle Swarm Optimization ◽  
Structural Material ◽  
Inverse Analysis ◽  
Civil Engineering ◽  
Particle Swarm ◽  
Engineering Applications ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Identification Of Parameters

Multi-objective particle swarm optimization method with balanced diversity and convergence

2013 ◽  
Vol 33 (7) ◽  
pp. 1926-1929 ◽  
Author(s):  
Huantong GENG ◽  
Jun GAO ◽  
Tingting JIA ◽  
Zhengxue WU
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Particle Swarm Optimization Method

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

Dynamic Multi Objective Particle Swarm optimization with Cooperative Agents

2020 ◽  
Author(s):  
Najwa Kouka ◽  
Raja Fdhila ◽  
Amir Hussain ◽  
Adel M. Alimi
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cooperative Agents
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