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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Maciel, Leandro Gomide, Fernando Ballini, Rosangela Univ Estadual Campinas Sch Elect & Comp Engn Dept Comp Engn & Ind Automat Campinas SP Brazil Univ Estadual Campinas Inst Econ Dept Econ Theory Campinas Brazil

ISBN: (纸本)9781467318037

Modeling the term structure of government bond yields is of major interest to macroeconomists and financial market practitioners. It is crucial for bonds and derivatives pricing, risk management, revealing market expectations, and essential for monetary policy decisions. This paper proposes the use of differential evolution to estimate the term structure of government bond yields considering parsimonious modeling. differential evolution avoids non-convergence problems and the high instability of model parameter estimation caused by the need to choose initial values by the curve fitting algorithms of traditional optimization methods. Results show that differential evolution algorithm gives more accurate yield curves than nonlinear least squares and genetic algorithms, highlighting its potential as a tool for term structure of interest rates estimation.

关键词： differential evolution algorithms Government Nonlinear least squares Modeling evolutionary algorithm

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Self-adaptive differential evolution algorithm for constrained real-parameter optimization

Self-adaptive differential evolution algorithm for constrain...

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IEEE Congress on evolutionary Computation

作者： Huang, V. L. Qin, A. K. Suganthan, P. N. Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9780780394872

In this paper, we propose an extension of Self-adaptive differential evolution algorithm (SaDE) to solve optimization problems with constraints. In comparison with the original SaDE algorithm, the replacement criterion was modified for handling constraints. The performance of the proposed method is reported on the set of 24 benchmark problems provided by CEC2006 special session on constrained real parameter optimization.

关键词： differential evolution algorithms Parameter optimization self-adaptive evolutionary algorithm Benchmark testing Constrained optimization

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Self-adaptive differential evolution algorithm for numerical optimization

Self-adaptive differential evolution algorithm for numerical...

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IEEE Congress on evolutionary Computation

作者： Qin, AK Suganthan, PN Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)0780393635

In this paper, we propose a novel Self-adaptive differential evolution algorithm (SaDE), where the choice of learning strategy and the two control parameters F and CR are not required to be pre-specified. During evolution, the suitable learning strategy and parameter settings are gradually self-adapted according to the learning experience. The performance of the SaDE is reported on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization.

关键词： evolutionary computation learning (artificial intelligence) optimisation self-adjusting systems learning strategy numerical optimization parameter settings real parameter optimization self-adaptive differential evolution algorithm self-adjusting systems parameter setting evolutionary algorithm Parameter optimization differential evolution algorithms Learning Strategies self-adaptive

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differential evolution with adaptive parameter setting for multi-objective optimization

Differential evolution with adaptive parameter setting for m...

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IEEE Congress on evolutionary Computation

作者： Zielinski, Karin Laur, Rainer Univ Bremen ITEM D-2800 Bremen 33 Germany

ISBN: (纸本)9781424413393

Control parameter settings influence the convergence probability and convergence speed of evolutionary algorithms but it is often not obvious how to choose them. In this work an adaptive approach for setting the control parameters of a multi-objective differential evolution algorithm is presented. The adaptive approach is based on methods from Design of Experiments, so it is able to detect significant performance differences of individual parameters as well as interaction effects between parameters. It is evaluated based on 13 test functions and several performance measures.

关键词： parameter setting control parameter evolutionary algorithm Self tuning interaction effect Converge differential evolution algorithms

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Multi-Objective Optimization with Modified Pareto differential evolution

Multi-Objective Optimization with Modified Pareto Differenti...

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International Conference on Intelligent Computation Technology and Automation

作者： Chen Xiao-qing Hou Zhong-xi Liu Jian-Xia NUDT Coll Aerosp & Mat Engn Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9780769533575

In this paper, a modified Pareto differential evolution algorithm approach is introduced to solve multi-objective optimization problems. The modified approach including: improving the crowding mechanism by introducing the Niche theory, introducing a time variant scaling factor F, and using a simple but efficient mutation operator which combines information of the both better and worse solution in the population. The performance of the MDE is measured on the set of ZDT benchmark functions on multi-objective optimization.

关键词： crowding mechanism time varying multi-objective control Pareto optimisation Modified F Factor differential evolution algorithms Scale factor evolution evolution evolution evolution

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A research on short-term hydro-wind economic dispatch problem simulated with a modified differential evolution algorithm 2

A research on short-term hydro-wind economic dispatch proble...

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2nd Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Wang Xuemin Li Anqiang Zhang Rui Changjiang Inst Survey Planning Design & Res Wuhan 430010 Hubei Peoples R China

ISBN: (纸本)9781510840744

Due to the wide construction of wind power and the difficulty for it to join the power grid, a short-term hydro-wind economic dispatch (WHED) problem is proposed. WHED system contains several wind power units and hydropower plants, which are renewable and clean. Combined with hydropower plants, the wind power units can join the power grid stably. Then, a WHED system with four cascaded hydropower plants and two wind units is established, and a modified differential evolution (DE) algorithm with chaotic perturbation is proposed for optimizing. Finally, two cases are simulated and analysed, the dispatch results show that the presented model and algorithm are feasible and effective.

关键词： differential evolution algorithms Dispatching Wind energy Power grids WATER POWER hydropower Hydroelectric Power Plants evolutionary algorithm algorithms

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Multi-objective optimization based on self-adaptive differential evolution algorithm

Multi-objective optimization based on self-adaptive differen...

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IEEE Congress on evolutionary Computation

作者： Huang, V. L. Qin, A. K. Suganthan, P. N. Tasgetiren, M. F. Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore Fatih Univ Dept Management TR-34500 Istanbul Turkey

ISBN: (纸本)9781424413393

In this paper, our recently developed Self-adaptive differential evolution algorithm (SaDE) is extended to solve numerical optimization problems with multiple conflicting objectives. The performance of the proposed MOSaDE algorithm is evaluated on a suit of 19 benchmark problems provided for the CEC2007 special session (http://***/home/epnsugan/)on Performance Assessment of Multi-Objective Optimization algorithms.

关键词： differential evolution algorithms self-adaptive Optimization algorithms Benchmark testing evolutionary algorithm Performance assessment

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Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2019年第10期15卷

作者： Zhang, Yu Zhang, Jiawen Luo, Lin Gao, Xiaorong Southwest Jiaotong Univ Sch Phys Sci & Technol Chengdu 610031 Sichuan Peoples R China

It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg-Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg-Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg-Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg-Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

关键词： Wheel-set size prediction neural network model Levenberg-Marquardt algorithm differential evolution algorithms

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AMoDeBic: An adaptive Multi-objective differential evolution biclustering algorithm of microarray data using a biclustering binary mutation operator

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期238卷

作者： Charfaoui, Younes Houari, Amina Boufera, Fatma Univ Mustapha STAMBOULI Mascara Mascara Algeria

In bioinformatics, biclustering is a crucial optimization task that can reveal hidden patterns and identify groups of genes that behave similarly under certain conditions. This study aims to efficiently identify high-quality and cohesive biclusters that share common characteristics across two data dimensions. To achieve this, we propose the first biclustering approach that utilizes Multi-objective differential evolution (DE), which is a novel technique for gene group discovery. Additionally, we introduce the Biclustering Binary differential evolution (BBDE), a new mutation operator that combines node addition and deletion, guided by an adaptive factor F. We thoroughly tested our method's effectiveness taking into account biological relevance, noise, overlap resistance, and statistics. We compared our results to state of the art algorithms using both synthetic and real datasets like Yeast Cell Cycle, Saccharomyces cerevisiae, and Human B Cell. Our algorithms outperformed the comparisons and effectively identified significant biclusters.

关键词： differential evolution algorithms Multi-objective differential evolution Biclustering evolutionary algorithms Micro-array data Knowledge discovery

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Furnace Temperature Curve Optimization Model Based on differential evolution Algorithm

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Journal of Electronic Research and Application 2024年第4期8卷 64-80页

作者： Yiming Cheng Merchant Marine College Shanghai Maritime UniversityShanghai 201308China

When soldering electronic components onto circuit boards,the temperature curves of the reflow ovens across different zones and the conveyor belt speed significantly influence the product *** study focuses on optimizing the furnace temperature curve under varying settings of reflow oven zone temperatures and conveyor belt *** address this,the research sequentially develops a heat transfer model for reflow soldering,an optimization model for reflow furnace conditions using the differential evolution algorithm,and an evaluation and decision model combining the differential evolution algorithm with the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)*** approach aims to determine the optimal furnace temperature curve,zone temperatures of the reflow oven,and the conveyor belt speed.

关键词： Furnace temperature curve Difference equations differential evolution algorithms TOPSIS methods

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