Optimisation inspiring from behaviour of raining in nature: droplet optimisation algorithm

Yasrebi, Milad; Baghban, Arash Eskandar; Parvïn, Hamïd; Mohammadpour, Majid

doi:10.1504/ijbic.2018.094616

Cited by 17 publications

(5 citation statements)

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“…The clustering ensemble approaches with homogenous clustering algorithms employ a same clustering algorithm during generation of the ensemble pool, that is, all partitions of the ensemble pool are generated by a same clustering algorithm. The partitions of the ensemble pool in homogenous clustering algorithms can be produced by one of the following subtypes: by employing different initializations of a given clustering algorithm , by employing different parameters (like different numbers of clusters) for data clustering using a same clustering algorithm , by employing different data projections for data clustering using a same clustering algorithm , by employing different subsets of dataset features for data clustering using a same clustering algorithm , by employing meta heuristic algorithms for data clustering , and by employing different datasets for data clustering using a same clustering algorithm. …”

Section: Related Workmentioning

confidence: 99%

Section: Ensemble Generation Problemmentioning

confidence: 99%

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Dependability‐based cluster weighting in clustering ensemble

Najafi

Parvïn

Mirzaie

et al. 2020

Statistical Analysis

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After observing the ensemble success in supervised learning (such as classification), it was extended into unsupervised learning. Therefore, cluster ensemble, which merges multiple basic data partitions or clusters (called as ensemble pool) into an ordinarily better clustering solution usually named as consensus partition, emerged. Any cluster ensemble method tries to optimize a particular criterion during extracting the consensus partition out of the ensemble pool. But traditional cluster ensembles consider all the pool members with the equal importance in making the consensus partition; that is to say that each basic partition or cluster participates in the cluster ensemble algorithm equivalently. Indeed, they ignore to consider any ensemble member according to its importance. But it is obvious that some clusters with more quality deserve more emphasis and some clusters with less quality deserve less emphasis during generating consensus partition. This paper proposes (a) a metric to evaluate quality of any arbitrary cluster, (b) a mechanism to project the computed quality of a cluster into a meaningful weight value, and (c) an approach to apply the weight values of the basic clusters in the cluster ensemble process. Experimental results conducted on a number of real‐world standard datasets indicate that the proposed method outperforms the state of the art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Ensemble Generation Problemmentioning

confidence: 99%

Dependability‐based cluster weighting in clustering ensemble

Najafi

Parvïn

Mirzaie

et al. 2020

Statistical Analysis

Self Cite

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“…In recent years, a large number of bio‐inspired optimization algorithms have been proposed, such as genetic algorithm (GA), bat algorithm (BA), differential evolution (DE), firefly algorithm (FA), and so on, which have been applied to various fields, including the malicious code detection, LEACH protocol optimization, privacy preservation, intrusion detection system, job scheduling problem, and so on . Hence, more scholars began to use them to decrease the error of the DVHop positioning algorithm because of their good global convergence and high robustness.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Wang

Huang

Cui

et al. 2019

Concurrency and Computation

167

140

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Distance vector-hop (DVHop), as a range-independent positioning algorithm, is a significant positioning method in wireless sensor networks (WSNs). It is composed of three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning method results in a larger positioning error. Therefore, to enhance the positioning precision, this paper investigates the characteristic of error distribution between the estimated and real distance in the DVHop algorithm and reveals that the error is subjecting to the Gaussian distribution, N∼(0,1/3CR). Furthermore, to improve positioning accuracy, we propose a Gaussian error correction multi-objective positioning model with non-dominated sorting (NSGA-II), which named GGAII-DVHop. Finally, this model is tested on three complex network topologies, and the results demonstrate that it is significantly superior to other four algorithms in both positioning precision and robustness. KEYWORDSGaussian error correction, GGAII-DVHop, multi-objective DVHop model, wireless sensor networks INTRODUCTIONRecently, the smaller and smarter sensors are deployed in WSNs 1-3 with the micro electromechanical systems technologies advanced. These seemingly tiny nodes are made up of devices such as memory, transceivers, power supplies, etc, which constitute inexpensive, independent, and low-powered WSNs. In addition, WSNs are widely applied to industry, 4 medical treatment, home care, machine learning, and so on. However, their communication radius (CR) is limited due to their low power consumption, which determines the nodes that communicate with each other by transmitting packets, that is multi-hop routing. The Internet of Things (IoT) 5-7 is composed of many such small and relatively independent WSNs.In these applications, the location of the sensor nodes is an unavoidable topic because many of the data will be meaningless without location information. For this reason, various localization algorithms were designed to sharpen the positioning accuracy of nodes, including the range-dependent and range-independent localization algorithms. Among them, the received signal strength indicator (RSSI), 8 time of arrival (TOA), 9 and Ad hoc positioning system (APS) 10 are typically range-dependent positioning algorithms; the distance vector hop (DVHop), 11 convex position estimation (CPE), 12 and multi-dimensional scaling (MDS) 13 are representatives of range-independent localization algorithms, such as Figure 1. Because of the limitation of the computer hardware and overall cost, the range-independent localization algorithms 14,15 are more popular with researchers.The DVHop is widely used due to its simple operation and low hardware requirements. It uses the multi-hop routing between sensor nodes, and the average distance of per hop between each anchor node to locate. The positioning process of DVHop can be divided into three parts, including connectivity detection, distance estimation, and position estimation. However, this simple positioning principle us...

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“…e algorithm is simple and convenient. rough the comparison and analysis with some of the latest optimization algorithms, it is found that the droplet optimization algorithm is better than some of the latest optimization algorithms [10]. Omidvar et al, based on the random behavior of natural phenomena and the behavior of chicks, constructed a See-See Partridge Chicks Optimization (SSPCO) algorithm and compared it with the latest optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Coordination and Coupling of Active and Passive Energy Optimization in Public Institutions: A Case Study of Hunnan District, Shenyang in China

Yang

Gao

2020

Mathematical Problems in Engineering

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Taking Hunnan District of Shenyang City as an example, this paper investigates 32 government agencies, 17 tertiary first-class hospitals, and 15 colleges and universities in Hunnan region and analyzes the energy consumption characteristics of three types of public institutions. Through the use of DeST energy consumption software to simulate the energy consumption of three types of public institutions, combined with the load density method to predict the total energy demand of hospitals, universities, and government agencies in Hunnan area and the MARKAL model coupling mechanism, the coupling model of active and passive energy suitable for public institutions is presented. According to the forecast results of energy demand, combined with the energy consumption structure and energy conversion efficiency, the energy supply of each subitem of the public institution was solved and the energy supply of the public institutions in Hunnan District was optimized in accordance with the relevant constraints. Through calculation, it is found that by 2020, the passive energy supply of public institutions in Hunnan District will account for 19.79% and active energy sources such as coal and petroleum will still be the main type of energy used by public institutions. Based on the research on the active and passive energy coupling supply of public institutions in Hunnan District, the active and passive energy coupling model and algorithm of public institutions constructed can provide a reference basis for the active and passive energy coupling utilization of public institutions in China.

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Optimisation inspiring from behaviour of raining in nature: droplet optimisation algorithm

Cited by 17 publications

References 0 publications

Dependability‐based cluster weighting in clustering ensemble

Dependability‐based cluster weighting in clustering ensemble

A Gaussian error correction multi‐objective positioning model with NSGA‐II

Coordination and Coupling of Active and Passive Energy Optimization in Public Institutions: A Case Study of Hunnan District, Shenyang in China

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