Hybrid Particle Swarm Optimization Applied to Recovery Scheduling of Large-Scale Flight Delays

2008 ◽  
Author(s):  
Weiqiang Liu ◽  
Min Zhu ◽  
Xinru Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Particle Swarm ◽  
Flight Delays ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

scholarly journals Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2792 ◽  
Author(s):  
Hyunseok Kim ◽  
Dongjun Suh
Keyword(s):  
Particle Swarm Optimization ◽  
Data Fusion ◽  
Large Scale ◽  
Synergistic Effects ◽  
Particle Swarm ◽  
Sensor Data ◽  
Fusion Model ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.

scholarly journals Hybrid particle swarm optimization and tabu search for the design of large-scale water distribution networks

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
José Eloim Silva de Macêdo ◽  
José Roberto Gonçalves de Azevedo ◽  
Saulo de Tarso Marques Bezerra
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Water Distribution ◽  
Large Scale ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

ABSTRACT Water distribution network (WDN) optimization has received special attention from various technicians and researchers, mainly due to its high costs of implementation, operation and maintenance. However, the low computational efficiency of most developed algorithms makes them difficult to apply in large-scale WDN design problems. This article presents a hybrid particle swarm optimization and tabu search (H-PSOTS) algorithm for WDN design. Incorporating tabu search (TS) as a local improvement procedure enables the H-PSOTS algorithm to avoid local optima and show satisfactory performance. Pure particle swarm optimization (PSO) and H-PSOTS algorithms were applied to three benchmark networks proposed in the literature: the Balerma irrigation network, the ZJ network and the Rural network. The hybrid methodology obtained good results when seeking an optimal solution and revealed high computational performance, making it a new option for the optimal design of real water distribution networks.

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