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Application of Differential Evolution algorithm for Optimization of Strategies Based on Financial Time Series

NUMERICAL ANALYSIS AND APPLICATIONS 2016年第2期9卷 150-158页

作者： Monakhov, O. G. Monakhova, E. A. Pant, M. Russian Acad Sci Inst Computat Math & Math Geophys Siberian Branch Pr Akad Lavrenteva 6 Novosibirsk 630090 Russia Dept Appl Sci & Engn New Technol BlockSaharanpur Campus IIT Roorkee 247667 Saharanpur India

An approach for optimization of trading strategies ( algorithms) based on indicators of financial markets and evolutionary computation is described. A new version of differential evolution algorithm for the search for optimal parameters of trading strategies for maximization of trading profit is used. The experimental results show that this approach can improve several times the profitability of the trading strategies.

关键词： trading strategy parallel genetic algorithm technical analysis financial indicator template evolutionary computation

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An asymmetric pinching damaged hysteresis model for glubam members: Parameter identification and model comparison

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COMPUTERS & STRUCTURES 2024年 305卷

作者： Shi, Da Demartino, Cristoforo Marano, Giuseppe Carlo Xu, Yongjia Politecn Torino DISEG Dept Struct Geotech & Bldg Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Roma Tre Univ Dept Architecture I-00153 Rome Italy Cent South Univ Sch Civil Engn Changsha 410075 Hunan Peoples R China

The performance of glue laminated bamboo (glubam) members is governed by the nonlinear response at their joints, where high deformation levels and stress concentrations are developed. Numerous phenomenological models are presently employed to describe the hysteresis behavior of these joints, while these models always have an excessive number of parameters, and the physical interpretation of these parameters is often challenging. Moreover, some hysteresis models cannot capture all hysteresis features such as asymmetry, pinching, and damage. Consequently, this paper introduces a novel phenomenological-based hysteretic model named Asymmetric Pinching Damaged (APD) model, and implemented it in Abaqus by combining connector and spring elements in series or parallel. This model encompasses asymmetry, pinching, and strength degradation for bamboo joint components, with parameters that possess clear physical meanings and are readily comprehensible. This study also presented a parameter identification framework coupling the parallel genetic algorithm (PGA) and Bayesian Neural Network (BNN). By merging the FE modeling and optimizing algorithms with the interactive application of ABAQUS and Python software platforms, the integrated identification framework is capable of performing multi-threaded parallel computation of finite element models considering the BNN-based uncertainty quantification, thus greatly improving the efficiency of parameter identification.

关键词： Glue laminated bamboo Asymmetric Pinching Damaged (APD) hysteretic models Parameter identification parallel genetic algorithm Bayesian neural network

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A novel microstrip dipole antenna with wideband and end-fire properties

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2007年第12期21卷 1679-1688页

作者： Chen, K. Chen, X. Huang, K. Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Peoples R China

A novel microstripdip ole antenna with wideband and end-fire properties is presented. It employs a teardrop dipole and an open sleeve structure for wide impedance bandwidth, and four printed directors for the end-fire radiation pattern and high end-fire gains. The antenna has been optimized in an automated design, making use of the genetic algorithm (GA) with full-wave EM simulation and cluster parallel computation. The resulting antenna exhibits encouraging properties, i.e., its S (11) < -10 dB impedance bandwidth surpasses 2.56:1 (from 1.32 GHz to 3.39 GHz), while has a maximum return loss -13.2 dB, maintains the end-fire radiation pattern and obtains end-fire gains from 5.2 dBi to 9.3 dBi over a frequency band from 1.5 GHz to 3.0 GHz. An antenna prototype has been fabricated and tested. Good agreement is obtained between numerical calculated and measured results.

关键词： parallel genetic algorithm YAGI ANTENNA DESIGN OPTIMIZATION BANDWIDTH ARRAY

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Structural polymorphism of the HIV-1 leader region explored by computational methods

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NUCLEIC ACIDS RESEARCH 2005年第22期33卷 7151-7163页

作者： Kasprzak, W Bindewald, E Shapiro, BA NCI Frederick SAIC Frederick Basic Res Program Frederick MD 21702 USA NCI Frederick Natl Canc Inst Ctr Canc Res Nanbiol Program Frederick MD 21702 USA

Experimental studies revealed that the elements of the human immunodeficiency virus type 1 (HIV-1) 5'-untranslated leader region (5'-UTR) can fold in vitro into two alternative conformations, branched (BMH) and 'linearized' (LDI) and switch between them to achieve different functionality. In this study we computationally explored in detail, with our massively parallel genetic algorithm (MPGAfold), the propensity of 13 HIV-1 5'-UTRs to fold into the BMH and the LDI conformation types. Besides the BMH conformations these results predict the existence of two functionally equivalent types of LDI conformations. One is similar to what has been shown in vitro to exist in HIV-1 LAI, the other is a novel conformation exemplified by HIV-1 MAL long-distance interactions. These novel MPGAfold results are further corroborated by a consensus probability matrix algorithm applied to a set of 155 HIV-1 sequences. We also have determined in detail the impact of various strain mutations, domain sizes and folds of elongating sequences simulating folding during transcription on HIV-1 RNA secondary structure folding dynamics.

关键词： HUMAN-IMMUNODEFICIENCY-VIRUS parallel genetic algorithm RNA SECONDARY STRUCTURE PRIMER ACTIVATION SIGNAL DIMERIZATION INITIATION SITE IN-VITRO EVIDENCE REVERSE TRANSCRIPTION GENOMIC-RNA STRUCTURE PREDICTION PACKAGING SIGNAL Transcription Initiation Site computational methods LDI Reverse Transcription HIV-1 HIV

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Optimal deploying IoT services on the fog computing: A metaheuristic-based multi-objective approach

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 10010-10027页

作者： Wu, Banghua Lv, Xuebin Shamsi, Wameed Deyah Dizicheh, Ebrahim Gholami Sichuan Univ Coll Cybersecur Chengdu 610041 Peoples R China Al Mustaqbal Univ Coll Informat Technol Unit Babylon 51001 Iraq Univ Tehran Sch ECE Tehran Iran Sichuan Univ Sch Comp Sci & Engn Chengdu 610064 PR Peoples R China

This study deals with solving the Internet of Things (IoT) Service Placement Problem (SPP) in fog computing environment using metaheuristic approaches. Basically, SPP is a non-deterministic polynomial-time hard (NP-hard) with huge discrete search spaces that is often processed by heuristic and metaheuristic approaches. We proposed an Improved parallel genetic algorithm (IPGA) to solve SPP and named it IPGA-SPP. Since the genetic algorithm may get stuck in local optima, we configure it in parallel with a shared memory along with several elitist operators. IPGA-SPP considers resource distribution for load balancing and prioritizes service execution to reduce latency. Also, IPGA-SPP solves the problem as a multi-objective problem by maintaining a set of Pareto solutions by making compromises between service latency, service cost, resource utilization and service time. Although many metaheuristic approaches have been developed for SPP, but satisfying the quality of service (QoS) and simultaneously guaranteeing security to overcome the constraints of fog computing has been less considered. In this regard, we equip IPGA-SPP with a two-way trust management mechanism so that clients and service providers can verify each other's trustworthiness. Therefore, the proposed scheme is a latency-aware, cost-aware and trustaware approach to improve the deployment process in fog computing. Through simulation on a synthetic fog environment, IPGA-SPP has shown an average of 8.4% better performance compared to state-of-theart methods such as CSA-FSPP, GA-PSO, EGA and WOA-FSP.

关键词： Internet of Things Fog Computing Services Placement Multi-objective problem parallel genetic algorithm Trust Management Mechanism

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Selection of a stroke risk model based on transcranial Doppler ultrasound velocity

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JOURNAL OF APPLIED STATISTICS 2012年第12期39卷 2699-2712页

作者： Mukhopadhyay, S. Das, I. Das, K. Indian Inst Technol Dept Math Bombay 400076 Maharashtra India AC Nielsen ORG MARG Pvt Ltd Nielsen Co Bombay 400033 Maharashtra India

Increased transcranial Doppler ultrasound (TCD) velocity is an indicator of cerebral infarction in children with sickle cell disease (SCD). In this article, the parallel genetic algorithm ( PGA) is used to select a stroke risk model with TCD velocity as the response variable. Development of such a stroke risk model leads to the identification of children with SCD who are at a higher risk of stroke and their treatment in the early stages. Using blood velocity data from SCD patients, it is shown that the PGA is an easy-to-use computationally variable selection tool. The results of the PGA are also compared with those obtained from the stochastic search variable selection method, the Dantzig selector and conventional techniques such as stepwise selection and best subset selection.

关键词： Dantzig selector Gibbs sampling optimization parallel genetic algorithm sickle cell disease stochastic search variable selection

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Adaptive Cellular Memetic algorithms

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EVOLUTIONARY COMPUTATION 2009年第2期17卷 231-256页

作者： Huy, Nguyen Quang Soon, Ong Yew Hiot, Lim Meng Krasnogor, Natalio Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals ill a population are Usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

关键词： Memetic algorithm parallel genetic algorithm cellular genetic algorithm cellular memetic algorithm

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FFcPsA: a fast finite conventional state using prefix pattern gene search algorithm for large sequence identification

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SOFT COMPUTING 2019年第8期23卷 2761-2771页

作者： Surendar, A. Arun, M. Basha, A. Mahabub Anna Univ Chennai Tamil Nadu India VIT Univ SENSE Vellore Tamil Nadu India KSR Coll Engn Dept ECE Tiruchengode India

Gnomic information continues to flood, and this trend comes in the wake of the life sciences' rapid development. The eventuality has been an increase in the demand for more scalable and faster searching techniques, with the demand also proving urgent. Whereas a faster algorithm could be used to search biomedical data, the process of making gene prediction remains challenging. Particularly, the searching of biomedical data has been affirmed to be a simple gradient base approach. Therefore, indexing has been investigated with the aim of achieving a fast finite conventional rate. With biomedical expressed datasheet at hand, data-based large sequence identification has been achieved via the prefix pattern gene search algorithm. Imperative to note is that real-value expression matrices can replace microarray experimental gene expression data. To ensure that the genomic dataset's querying exhibits reductions in the overall retrieval time and that the time used for pattern array building is sped up, parallel partitioned methods have gained application. Notably, the central merit accruing from the latter method is that the majority of unrelated sequences are skipped. Also, these methods ensure that the real search problems are only decomposed to establish original database fractions. To ensure that the establishment of the gene's hidden information and similar characteristics is enhanced, large genetic data patterns are required.

关键词： Gene search Pattern extraction Hash function Microarray Gene expression parallel genetic algorithm

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Overall optimization of distribution networks

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Journal of Harbin Institute of Technology(New Series) 2001年第4期8卷 371-374页

作者：刘莉陈学允郭志忠 Beijing XUJI Electric Corporation Limited Harbin Institute of Technology

Network reconfiguration and capacitor switching are important measures to reduce power loss and improve security and economy in automation of distribution. A new method based on parallel genetic algorithm is proposed to search the whole problem space for better solution. Multiple populations evolve independently and communicate periodically, which simulates parallel computing process to save computing time. The results show that the method is robust and has better benefit than the alterative iteration method. In addition, the effect of overall optimization is better than optimization alone. Power loss can be reduced and the level of voltage can be greatly improved.

关键词： network reconfiguration capacitor switching distribution networks parallel genetic algorithm

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Multi-Population genetic algorithm for Locating Multi-Optima in Noisy Complex Landscape

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2011年第16期40卷 3029-3048页

作者： Szeto, K. Y. Guo, Yunbo Hong Kong Univ Sci & Technol Dept Phys Hong Kong Hong Kong Peoples R China

A multi-population genetic algorithm (MPGA) is introduced to search for as many as possible of the local optima of a complex function in a noisy environment. By considering a multi-agent system consisting of sub-populations of agents that evolve simultaneously as a group of chromosomes in a genetic algorithm, we perform a spatial allocation of resources by the partitioning of the search space into many subspaces. A migration operator is used to control the exchange of chromosomes between different sub-populations. This spatial allocation of computational resources has the advantage of exhaustive search which avoids duplicate effort, and combines it with the parallel nature of the search for the solution in disjoint subspaces by genetic algorithms. The division of the solution space is performed intelligently using loci statistics of the chromosomes in past generations. The benchmark function used is a 2-D function with the x-y coordinates represented by a binary coded chromosome. Unlike traditional GAs, the exact differential df is used as the fitness for the chromosomes. For noisy environment, the derivatives used in calculating exact differentials are obtained through second order polynomial fitting. Two measurements are employed to evaluate the performance of this algorithm: precision (Pr), which is the average fitness of all the local optima at the end of the evolution, and cover degree (CD), which is the percentage of the local optima obtained at the end of evolution, among the total number of optima. The variance of the noise affects both CD and Pr, while the quality of the local optima decreases with increasing noise, the degree of coverage (CD) is rather insensitive to noise. The effects of the parameters of MPGA on performance are discussed, and applications to the mapping of complex landscape suggested.

关键词： Landscape mapping Multi-agent system parallel genetic algorithm Population dynamics

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