A diversity-enriched variant of discrete PSO applied to the design of water distribution networks

Montalvo, Idel; Izquierdo, Joaquín; Pérez, R.B.; Iglesias, Pedro

doi:10.1080/03052150802010607

Cited by 48 publications

(29 citation statements)

References 30 publications

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“…This problem can be addressed by a modified Bonferroni procedure which manipulates the α value in a way that protects against capitalization on chance [17]. It should be noted that the test function f 9 is excluded from the modified Bonferroni procedure in Table 9 and Table 10, because all six algorithms could find the optimum solution and have the same performances on f 9 . In Table 9, on the dimensions of 50, the number of (3/3/3) shown in the last column means that there is no significantly difference in performance between DHPSO-d and HGLPSO to all test functions.…”

Section: Analysis Of Results On All Functionsmentioning

confidence: 99%

“…birds flocking while searching for a food source in a given area. Due to its conceptual simplicity and excellent global optimization capability, PSO has been successfully applied to a number of applications, such as DNA sequence compression [7], resource allocation [8] and water distribution network design [9]. In this paper, we mainly focus on the two versions of standard PSO algorithms [10]: a constricted GBest PSO algorithm using a global topology and a constricted LBest PSO algorithm using a local topology.…”

Section: Standard Particle Swarm Optimization (Spso)mentioning

confidence: 99%

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Dynamic Heterogeneous Particle Swarm Optimization

Yang

Sato

2017

IEICE Trans. Inf. & Syst.

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SUMMARYRecently, the Static Heterogeneous Particle Swarm Optimization (SHPSO) has been studied by more and more researchers. In SHPSO, the different search behaviours assigned to particles during initialization do not change during the search process. As a consequence of this, the inappropriate population size of exploratory particles could leave the SHPSO with great difficulties of escaping local optima. This motivated our attempt to improve the performance of SHPSO by introducing the dynamic heterogeneity. The self-adaptive heterogeneity is able to alter its heterogeneous structure according to some events caused by the behaviour of the swarm. The proposed triggering events are confirmed by keeping track of the frequency of the unchanged global best position (p g ) for a number of iterations. This information is then used to select a new heterogeneous structure when p g is considered stagnant. According to the different types of heterogeneity, DHPSO-d and DHPSO-p are proposed in this paper. In, particles dynamically use different rules for updating their position when the triggering events are confirmed. In DHPSO-p, a global gbest model and a pairwise connection model are automatically selected by the triggering configuration. In order to investigate the scalability of and DHPSO-p, a series of experiments with four state-of-the-art algorithms are performed on ten well-known optimization problems. The scalability analysis of and DHPSO-p reveals that the dynamic self-adaptive heterogeneous structure is able to address the exploration-exploitation trade-off problem in PSO, and provide the excellent optimal solution of a problem simultaneously. key words: Particle Swarm Optimization (PSO), dynamic heterogeneity, the gbest topology, the pairwise connection topology

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Section: Analysis Of Results On All Functionsmentioning

confidence: 99%

Section: Standard Particle Swarm Optimization (Spso)mentioning

confidence: 99%

Dynamic Heterogeneous Particle Swarm Optimization

Yang

Sato

2017

IEICE Trans. Inf. & Syst.

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“…To find out the best ranges of c 1 and c 2 , numerical simulations are carried out. Based on the values used in [49,50,[54][55][56][57][58], the test range of c 1 + c 2 is set to [3.0, 5.0]. The average and standard deviation of the optimum solutions for 100 trials of different values of c 1 + c 2 are shown in Table 5, and curves are presented in Figure 6 accordingly.…”

Section: An Example For Forecastingmentioning

confidence: 99%

Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market

et al. 2016

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For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the traditional GM(0, N) model, three major improvements of the proposed model are: (i) the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii) a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii) the model parameters are identified by an improved particle swarm optimization (PSO) instead of the least square method (LSM) for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR) model, the traditional GM(0, N) model and the artificial neural network (ANN) model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold-Mariano (MDM) test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.

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“…There are many heuristic algorithms that are developed in recent years, such as Genetic Algorithms [17][18][19][20][21][22][23], Particle Swarm Optimization [24,25], Shuffled Complex Evolution (SCE) [26,27] and Ant Colony Optimization [28][29][30]. The SCE algorithm is one of the popular optimization algorithms in the river basin model calibration over the past 10 years given that more than 300 different publications referenced the original SCE publications [26,27,31].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Dynamically Dimensioned Search with Sensitivity Information (HDDS-S) and Application to River Basin Management

Chu

Peng

Ding

et al. 2015

Water

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River basin simulation and multi-reservoir optimal operation have been critical for river basin management. Due to the intense interaction between human activities and river basin systems, the river basin model and multi-reservoir operation model are complicated with a large number of parameters. Therefore, fast and stable optimization algorithms are required for river basin management under the changing conditions of climate and current human activities. This study presents a new global optimization algorithm, named as heuristic dynamically dimensioned search with sensitivity information (HDDS-S), to effectively perform river basin simulation and multi-reservoir optimal operation during river basin management. The HDDS-S algorithm is built on the dynamically dimensioned search (DDS) algorithm; and has an improved computational efficiency while maintaining its search capacity compared to the original DDS algorithm. This is mainly due to the non-uniform probability assigned to each decision variable on the basis of its changing sensitivity to the optimization objectives during the adaptive change from global to local search with dimensionality reduced. This study evaluates the new algorithm by comparing its performance with the DDS algorithm on a river basin model calibration problem and a multi-reservoir optimal operation problem. The results obtained indicate that the HDDS-S algorithm outperforms the DDS algorithm in terms of search ability and computational efficiency in the two specific problems. In addition; similar to the DDS algorithm; the HDDS-S algorithm is easy to use as it does not require any parameter OPEN ACCESSWater 2015, 7 2215 tuning and automatically adjusts its search to find good solutions given an available computational budget.

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A diversity-enriched variant of discrete PSO applied to the design of water distribution networks

Cited by 48 publications

References 30 publications

Dynamic Heterogeneous Particle Swarm Optimization

Dynamic Heterogeneous Particle Swarm Optimization

Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market

A Heuristic Dynamically Dimensioned Search with Sensitivity Information (HDDS-S) and Application to River Basin Management

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