A Hybrid Evolutionary Algorithm Based on ACO and PSO for Real Estate Early Warning System

Wang, Jianzhou; Liang, Jinzhao; Che, Jinxing; Sun, Donghuai

doi:10.1109/iccsit.2008.55

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…Detect the cluster centers of geometrical structure datasets Fun and Chen (2005) SOM and K-means Cluster analysis Chi and Yang (2006) PSO + K-means Document clustering Cui and Potok (2006) Ant cluster and GA Employed the evolutions of characteristics of a single ant of colonies of group Aranha and Iba (2006) Clustering algorithm and AIS Cluster analysis Li and Tan (2006) HEA (PSO & GA) Solving unconstrained and constrained optimization problems Yang et al (2007) PSO and GRASP Feature selection and clustering problem Marinakis et al (2007) SAKHMC Solving clustering problem Gü ngör and Ü nler (2007) Hybrid (2005) Constructing load-balanced clusters to ad hoc networks Ho and Ewe (2005) Topic discovery from document Yang et al (2005) Diagnostic system Kuo et al (2005) Kuo et al (2006) Network security model Srinov and Kurutach (2006) Highway layout planning Meng and Li (2007) Holonic manufacturing control Zhao et al (2007) Real estate early warning system Wang et al (2008) Grain distribution centers location Xiao and Zhang (2009) Intrusion alert analysis Siraj et al (2009) Medical risk profile recognition Ramos et al (2009) moving randomicity, multiple pheromone concepts, trailing pheromone, foraging pheromone, nearest neighborhood interpolation, entropy-based metrics, an ensemble of ant partitions, kernel functions, multi-ant colonies approach, method of cursory clustering, chaotic perturbation, bio-inspired spatial transition probabilities, device of memory bank, new picking and dropping probability functions, short-term memory within each agent etc. The disadvantage of the ant-based algorithm is that its result is influenced by various input data and parameters.…”

Section: Akpsomentioning

confidence: 99%

“…They have presented a task oriented clustering mechanism and a corresponding optimization algorithm to the holonic control. Wang et al (2008) developed a novel ACO-PSO-hybrid algorithm for real estate early warning system. They have presented a pre-warning system to monitor and provide prewarning to the decision makers in real estate market.…”

Section: Applicationsmentioning

confidence: 99%

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A survey: hybrid evolutionary algorithms for cluster analysis

Hasan¹,

Ramakrishnan²

2011

Artif Intell Rev

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Clustering is a popular data analysis and data mining technique. It is the unsupervised classification of patterns into groups. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as slowness of the convergence, sensitive to initial value and preset classed in large scale data set etc. and they still require much investigation to improve performance and efficiency. Over the last decade, clustering with ant-based and swarm-based algorithms are emerging as an alternative to more traditional clustering techniques. Many complex optimization problems still exist, and it is often very difficult to obtain the desired result with one of these algorithms alone. Thus, robust and flexible techniques of optimization are needed to generate good results for clustering data. Some algorithms that imitate certain natural principles, known as evolutionary algorithms have been used in a wide variety of real-world applications. Recently, much research has been proposed using hybrid evolutionary algorithms to solve the clustering problem. This paper provides a survey of hybrid evolutionary algorithms for cluster analysis.

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Section: Akpsomentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

A survey: hybrid evolutionary algorithms for cluster analysis

Hasan¹,

Ramakrishnan²

2011

Artif Intell Rev

View full text Add to dashboard Cite

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“…Regarding the issue of land management, to support decision making in urban planning and for the efficient management of landed properties and real estate management, one of the objectives is to determine the suitability of locations by also making use of the interpretation of relationships between real estate rental prices and the geographical locations of the housing units themselves [10,18]. The issues relating to the real estate portfolio's optimization also address problems inherent to the risk preferences or risk levels of investors, such as the proposition of a semi-variance model of a real estate investment portfolio based on the risk preference coefficient using the Artificial Bee Colony algorithm [42,43].…”

Section: Overview Of Nature-inspired Algorithms Applied To the Real E...mentioning

confidence: 99%

Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum Computing

De Paola,

Previtera,

Manganelli

et al. 2023

Buildings

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Current technology still does not allow the use of quantum computers for broader and individual uses; however, it is possible to simulate some of its potentialities through quantum computing. Quantum computing can be integrated with nature-inspired algorithms to innovatively analyze the dynamics of the real estate market or any other economic phenomenon. With this main aim, this study implements a multidisciplinary approach based on the integration of quantum computing and genetic algorithms to interpret housing prices. Starting from the principles of quantum programming, the work applies genetic algorithms for the marginal price determination of relevant real estate characteristics for a particular segment of Naples’ real estate market. These marginal prices constitute the quantum program inputs to provide, as results, the purchase probabilities corresponding to each real estate characteristic considered. The other main outcomes of this study consist of a comparison of the optimal quantities for each real estate characteristic as determined by the quantum program and the average amounts of the same characteristics but relative to the real estate data sampled, as well as the weights of the same characteristics obtained with the implementation of genetic algorithms. With respect to the current state of the art, this study is among the first regarding the application of quantum computing to interpretation of selling prices in local real estate markets.

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“…Some of the probable solutions a i are not zero, and the corresponding training samples are the support vectors on the classification line. The optimal classification function obtained by solving Equations (9) to (11) is shown in (12).…”

Section: Svm Theorymentioning

confidence: 99%

“…The study of early warning indicators began with research on early warnings in the macroeconomic field [11]. With the development of the field of study, the concepts and methods of early warning systems have been gradually promoted and applied in the fields of aviation research, seismic research, traffic accident research, and real estate research [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Wang¹,

Zeng²,

Zhang³

et al. 2020

IJCIS

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Based on a multi-class support vector machine, an urban real estate early warning model is constructed for the Beijing real estate market. The initial indicator system is established based on the historical development of Beijing's real estate market and the selection of real estate early warning indicators. Early warning index data for Beijing from 2000 to 2018 are selected, and the leading index is selected by a time difference correlation analysis as the warning index to be used for further implementation. The model is found to have a good early warning judgment performance, and demonstrates generalization ability. The model analyzes the real estate market from the aspects of land supply, credit scale, housing supply structure, restriction of speculation, and strengthening of transparency of real estate information. It predicts that the real estate market in Beijing will run smoothly in 2019. Based on the model's findings, the paper proposes policy recommendations to promote the healthy operation of China's real estate market.

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A Hybrid Evolutionary Algorithm Based on ACO and PSO for Real Estate Early Warning System

Cited by 4 publications

References 2 publications

A survey: hybrid evolutionary algorithms for cluster analysis

A survey: hybrid evolutionary algorithms for cluster analysis

Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum Computing

Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

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