Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems

Montalvo, Idel; Izquierdo, Joaquín; Pérez-García, Rafael; Herrera, Manuel

doi:10.1016/j.engappai.2010.01.015

Cited by 72 publications

(41 citation statements)

References 44 publications

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“…On the other hand, some heuristic algorithms, for example, genetic algorithm (GA) [19][20][21] and particle swarm optimization (PSO) algorithm [22,23], have also emerged in water demand-related forecasting. Without the constraint of continuity of functions, GA is characteristic of inner implicit parallelism and better global searching capability.…”

Section: Introduction Wmentioning

confidence: 99%

City Water Demand Forecasting Based on Improved BP Neural Network

Xing¹,

You²,

Zhang³

et al. 2017

JRST

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City water demand forecasting is of great significance in reducing the cost of electricity consumption and municipal planning. Back-propagation (BP) neural network has been widely adopted in water demand forecasting in recent years. But BP performs unsatisfactorily in terms of training time and global searching ability, so in this paper we improve BP by two heuristic algorithms, namely, genetic algorithm (ga) and particle swarm optimization (PSO), respectively. The testing and verification of the three algorithms (BP, ga+BP, PSo+BP) have been conducted on real-life water demand forecasting of Beijing city. The obtained results demonstrate that, in spite of the execution time consumed, both ga+BP and PSo+BP performed with higher accuracy and less errors than BP. The obtained results also illustrate that PSo+BP slightly outperformed ga+BP in terms of forecasting accuracy.

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Section: Introduction Wmentioning

confidence: 99%

City Water Demand Forecasting Based on Improved BP Neural Network

Xing¹,

You²,

Zhang³

et al. 2017

JRST

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“…Particle swarm optimization (PSO) is a class of metaheuristics inspired by ethology and simulates the movements of zooids in a bird flock or fish school [1,7,20,21,[28][29][30][31][32]. In PSO, the population is termed as a swarm, and an individual is termed as a particle.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multiswarm Comprehensive Learning Particle Swarm Optimization

Estevez

2018

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Multiswarm comprehensive learning particle swarm optimization (MSCLPSO) is a multiobjective metaheuristic recently proposed by the authors. MSCLPSO uses multiple swarms of particles and externally stores elitists that are nondominated solutions found so far. MSCLPSO can approximate the true Pareto front in one single run; however, it requires a large number of generations to converge, because each swarm only optimizes the associated objective and does not learn from any search experience outside the swarm. In this paper, we propose an adaptive particle velocity update strategy for MSCLPSO to improve the search efficiency. Based on whether the elitists are indifferent or complex on each dimension, each particle adaptively determines whether to just learn from some particle in the same swarm, or additionally from the difference of some pair of elitists for the velocity update on that dimension, trying to achieve a tradeoff between optimizing the associated objective and exploring diverse regions of the Pareto set. Experimental results on various two-objective and three-objective benchmark optimization problems with different dimensional complexity characteristics demonstrate that the adaptive particle velocity update strategy improves the search performance of MSCLPSO significantly and is able to help MSCLPSO locate the true Pareto front more quickly and obtain better distributed nondominated solutions over the entire Pareto front.

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“…El ajuste de los parámetros supone una inversión de recursos inicial, que en ocasiones redunda en tediosas tareas de ensayo y error, sobre todo cuando no se tiene idea de qué valores utilizar para la solución de un problema concreto. En este trabajo se presenta una propuesta en la que el algoritmo PSO es capaz de autogestionar todos sus parámetros con excepción del tamaño de la población, permitiendo esto que el diseñador pueda desentenderse de estas tareas y se concentre mucho más en la parte del diseño en cuestión (Montalvo et al, 2010a).…”

Section: Comentariosunclassified

“…En este trabajo, además de utilizar el control adaptativo para el parámetro de inercia , introducido por (Kennedy y Eberhart, 1995), se propone un control auto-adaptativo para los demás parámetros (Montalvo et al, 2010a (Yao et al, 1999). El papel de  es crítico.…”

Section: Consideraciones Sobre Los Parámetros a Utilizarunclassified

“…Parameter setting represents an initial investment of resources, which sometimes results in tedious trial and error sessions, especially when there are no guidelines as to which values to use to solve a particular problem. This document presents a proposal in which the PSO algorithm self manages all of its parameters with the exception of population size, thus enabling the designer to ignore these tasks and concentrate on the design (Montalvo et al, 2010a).…”

Section: Observationsmentioning

confidence: 99%

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Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization

Arango¹

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Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems

Cited by 72 publications

References 44 publications

City Water Demand Forecasting Based on Improved BP Neural Network

City Water Demand Forecasting Based on Improved BP Neural Network

Adaptive Multiswarm Comprehensive Learning Particle Swarm Optimization

Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization

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