Using Particle Swarm Optimization Algorithm Based on Multi-Objective Function in Reconfigured System for Optimal Placement of Distributed Generation

Maleh, Mohammad Salehi; Soleymani, Soodabeh; Nezhad, Ramtin Rasouli; Ghadimi, Noradin

doi:10.1166/jbic.2013.1043

Cited by 16 publications

(4 citation statements)

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“…Therefore, valuable information can be shared amongst swarm members to suggest a common objective which leads individuals toward an optimal direction. PSO has been used to solve several types of optimization problems [ 15 ], including function optimization and parameter optimization [ 16 ] and shows promise for nonlinear function optimization [ 17 – 22 ]. In PSO, possible solutions are represented as the particles.…”

Section: Methodsmentioning

confidence: 99%

Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

Yang

Lin

Chuang

et al. 2014

BioMed Research International

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Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.

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Section: Methodsmentioning

confidence: 99%

Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

Yang

Lin

Chuang

et al. 2014

BioMed Research International

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“…For the computation of a location optimization model and distribution routing optimization, the gravity centre location model is mainly calibrated by the iterative method. Various heuristic algorithms are used to solve the NP hard problems, mainly including the ant colony algorithm [24], the genetic algorithm [4] , the particle swarm optimization algorithm [25,26] and corresponding improvements [27], and the fruit fly optimization algorithm [28]. Hiassat A [29] considered the characteristics of perishable goods, established an optimization model of the distribution centre inventory problem, and used a genetic algorithm and local search heuristic method to solve it.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Optimization Model of Agricultural Product Distribution in Remote Rural Areas

2020

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Due to the lack of systematically optimized logistics networks in many remote rural areas, the online sales of agricultural products in these areas have the disadvantages of high cost, high damage and slow speed. To address these logistic problems, this paper proposes a two-stage layout optimization model of agricultural product joint distribution centres based on the geographical features of remote rural areas. The number and location of distribution centres are selected in two stages to optimize the logistics network. Chengkou County, located in Chongqing city, China, has been selected as the study area. In the first stage, AP clustering is carried out using the distance between the logistics nodes of villages and towns, and the transit station of the source of agricultural products is obtained using the correlation between the nodes, which are regarded as alternative locations for the second-level logistics nodes. In the second stage, a joint distribution centre optimization model with the lowest cost is constructed. A fruit fly optimization algorithm is used to select the optimal locations from the alternative locations as the second-level logistics nodes and obtain the specific delivery path. Optimizing the logistics network of remote rural areas can reduce the logistics costs of agricultural products in these areas, promote online sales of agricultural products, and provide the government and logistics companies with theoretical references to open up new markets in remote areas.

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“…For the mentioned problem, several strategies have been published by researchers in recent years . Autoregressive (AR) in Ref.…”

Section: Introductionmentioning

confidence: 99%

Day‐ahead price forecasting based on hybrid prediction model

et al. 2016

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Short-Term Price Forecast is a key issue for operation of both regulated power systems and electricity markets. Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, nonstationary, and time variant behavior of electricity price time series. So, in this article, the forecast model includes wavelet transform, autoregressive integrated moving average, and radial basis function neural networks (RBFN) is presented. Also, an intelligent algorithm is applied to optimize the RBFN structure, which adapts it to the specified training set, reduce computational complexity and avoids over fitting. Effectiveness of the proposed method is applied for price forecasting of electricity market of mainland Spain and its results are compared with the results of several other price forecast methods. These comparisons confirm the validity of the developed approach.

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Using Particle Swarm Optimization Algorithm Based on Multi-Objective Function in Reconfigured System for Optimal Placement of Distributed Generation

Cited by 16 publications

References 0 publications

Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

Two-Stage Optimization Model of Agricultural Product Distribution in Remote Rural Areas

Day‐ahead price forecasting based on hybrid prediction model

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