A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction

Zarandi, M. H. Fazel; Zarinbal, Marzieh; Ghanbari, Niloufar; Türkşen, I.B.

doi:10.1016/j.ins.2012.08.002

Cited by 39 publications

(13 citation statements)

References 31 publications

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“…Thus MARS has been widely used in various fields such as sales prediction [52], credit evaluation [53][54][55], stock price forecasting [56][57][58], software reliability analysis [59][60][61] and predicting species distribution [62,63]. However, MARS has been rarely used in port throughput forecasting in the existing literature; therefore, this paper employs MARS to determine the final input vectors for RSVR and analyze significance degrees between different factors for further port throughput generation mechanism research.…”

Section: Introductionmentioning

confidence: 99%

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Geng

Dong

et al. 2015

Neurocomputing

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Section: Introductionmentioning

confidence: 99%

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Geng

Dong

et al. 2015

Neurocomputing

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“…While mass small deformations might be considered as local optimum, global solution constitutes an ideal crystal. Initially was simulated annealing applied primarily to operations research tasks (Heragu and Alfa, 1992;Koulamas et al 1994;Souilah, 1995;Van Breedam, 1995), but since then it has been intensively used in finance (Crama and Schyns, 2003;Luo et al 2014;Zarandi et al 2013), or medicine (Albrecht et al 2001;Jacob et al 2008). …”

Section: Figure 1 -Perfect Crystal With Minimum Energymentioning

confidence: 99%

Medical Staff Scheduling Using Simulated Annealing

2015

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Purpose: The efficiency of medical staff is a fundamental feature of healthcare facilities quality. Therefore the better implementation of their preferences into the scheduling problem might not only rise the work-life balance of doctors and nurses, but also may result into better patient care. This paper focuses on optimization of medical staff preferences considering the scheduling problem. Methodology/Approach:We propose a medical staff scheduling algorithm based on simulated annealing, a well-known method from statistical thermodynamics. We define hard constraints, which are linked to legal and working regulations, and minimize the violations of soft constraints, which are related to the quality of work, psychic, and work-life balance of staff.Findings: On a sample of 60 physicians and nurses from gynecology department we generated monthly schedules and optimized their preferences in terms of soft constraints. Our results indicate that the final value of objective function optimized by proposed algorithm is more than 18-times better in violations of soft constraints than initially generated random schedule that satisfied hard constraints.Research Limitation/implication: Even though the global optimality of final outcome is not guaranteed, desirable solutionwas obtained in reasonable time.Originality/Value of paper: We show that designed algorithm is able to successfully generate schedules regarding hard and soft constraints. Moreover, presented method is significantly faster than standard schedule generation and is able to effectively reschedule due to the local neighborhood search characteristics of simulated annealing.

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“…Fazel Zarandi et al [9] have used Imperialist Competitive Algorithm in their new hybrid clustering algorithm for model structure identification. In their paper a new fuzzy functions model is presented and its main parameters are optimized with simulated annealing approach.…”

Section: Related Workmentioning

confidence: 99%

The Proposed Center Initialization Based On Imperialist Competitive Algorithm (cib-ica)

Asfia¹,

Delavar²

2014

J. Math. Computer Sci.

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In this paper we will introduce an initial cluster centers method for k-means algorithm, which can achieve a significant impact on the convergence and will not fall local optimal solution trap .The proposed Center Initialization Based on Imperialist Competitive Algorithm (CIB-ICA) uses imperialist competitive algorithm and minimum spanning tree(MST) features to reduce clustering error percentage of K-means algorithm. The proposed method has been evaluated on some famous datasets and experimental results show that it is an efficient cluster center initialization method.

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A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction

Cited by 39 publications

References 31 publications

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Medical Staff Scheduling Using Simulated Annealing

The Proposed Center Initialization Based On Imperialist Competitive Algorithm (cib-ica)

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