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Optimal approximation of linear systems by a differential evolution algorithm

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2001年第6期31卷 698-707页

作者： Cheng, SL Hwang, C Natl Cheng Kung Univ Dept Chem Engn Tainan 701 Taiwan Natl Chung Cheng Univ Dept Chem Engn Chiayi 621 Taiwan

In this paper, the problem of optimally approximating linear systems is solved by a differential evolution algorithm (DEA) incorporating a search-space expansion scheme. The optimal approximate rational model with/without a time delay for a system described by its rational or irrational transfer function is sought such that a frequency-domain L-2-error criterion is minimized. The distinct feature of the proposed model approximation approach is that the search-space expansion scheme can enhance the possibility of converging to a global optimum in the DE search. This feature and the chosen frequency-domain error criterion make the proposed approach quite efficacious for optimally approximating unstable and/or nonmimimum-phase linear systems. The simplicity and robustness of the modified DEA in terms of easy implementation and minimum assumptions on search space are demonstrated by two numerical examples.

关键词： differential evolution algorithm model reduction optimization methods

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Parameter Estimation by Using an Improved Bee Memory differential evolution algorithm (IBMDE) to Simulate Biochemical Pathways

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CURRENT BIOINFORMATICS 2014年第1期9卷 65-75页

作者： Chong, Chuii Khim Mohamad, Mohd Saberi Deris, Safaai Shamsir, Mohd Shahir Chai, Lian En Choon, Yee Wen Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Biosci & Med Engn Dept Biol Sci Skudai 81310 Johor Malaysia

Assessing and estimating essential parameters for a metabolic pathway by using a mathematical model is a significant step in Systems Biology. However, estimating process often faces numerous obstacles, for example when the number of unknown parameters escalates or data has noise, gets trapped in local minima and or having repeated exploration of poor solution during search process. Thus, this study proposes an improved Bee Memory differential evolution algorithm (IBMDE), which is a combination of the differential evolution algorithm (DE), the Kalman Filter (KF), the Artificial Bee Colony algorithm (ABC), and a memory feature to solve the aforementioned problems. The implemented metabolic pathways for this improved estimation algorithm were glycerol and pyruvate synthesis pathways. IBMDE was successful in generating the estimated optimal kinetic parameter values with noticeable reduction in errors (81.36% and 99.46% respectively) and faster convergence times (6.19% and 15.72% respectively) compared to DE, the Genetic algorithm (GA), the Nelder Mead (NM), and the Simulated Annealing (SA). The results indicated that, most importantly, the kinetic parameters produced by IBMDE had enhanced the production of desired metabolites than the other estimation algorithms. Besides that, the results also demonstrated the reliability of IBMDE as an estimation algorithm in terms of lower error.

关键词： Artificial bee colony algorithm differential evolution algorithm kalman filter memory feature parameter estimation

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Shape recognition based on neural networks trained by differential evolution algorithm

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NEUROCOMPUTING 2007年第4-6期70卷 896-903页

作者： Du, Ji-Xiang Huang, De-Shuang Wang, Xiao-Feng Gu, Xiao Chinese Acad Sci Hefei Inst Intelligent Machines Intelligent Comp Lab Hefei 230031 Peoples R China

In this paper a new method for recognition of 2D occluded shapes based on neural networks using generalized differential evolution training algorithm is proposed. Firstly, a generalization strategy of differential evolution algorithm is introduced. And this global optimization algorithm is applied to train the multilayer perceptron neural networks. The proposed algorithms are evaluated through a plant species identification task involving 25 plant species. For this practical problem, a multiscale Fourier descriptors (MFDs) method is applied to the plant images to extract shape features. Finally, the experimental results show that our proposed GDE training method is feasible and efficient for large-scale shape recognition problem. Moreover, the experimental results illustrated that the GDE training algorithm combined with gradient-based training algorithms will achieve better convergence performance. (c) 2006 Elsevier B.V. All rights reserved.

关键词： shape recognition generalization strategy differential evolution algorithm multiscale Fourier descriptors leaf image database

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Sequential quadratic programming based differential evolution algorithm for optimal power flow problem

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第11期5卷 1149-1154页

作者： Sivasubramani, S. Swarup, K. S. Indian Inst Technol Dept Elect Engn Chennai 600036 Tamil Nadu India

This study proposes a hybrid algorithm combining sequential quadratic programming (SQP) and differential evolution (DE) algorithm for solving the optimal power flow (OPF) problem. In this hybrid method, SQP is used to generate an individual, which is a member of an initial population, for DE algorithm. Having generated an individual by SQP, which will be nearer to the optimal solution, DE algorithm can reach the optimal solution more effectively than the classical evolutionary algorithms can. The proposed method has been used to solve the OPF problem on the standard IEEE 30- and IEEE 118-bus test systems to validate the effectiveness. Two different objectives, namely fuel cost considering valve-point effects and the transmission line losses, have been considered. The simulation results obtained from the proposed hybrid method reveal that this algorithm gives better solution for the problem having more non-convexity.

关键词： evolutionary computation differential evolution algorithm quadratic programming sequential quadratic programming DE algorithm Power transmission, distribution and supply load flow valve-point effect transmission line loss IEEE 118-bus test system OPF problem SQP IEEE 30-bus test system optimal power flow problem Optimisation techniques

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An improved differential evolution algorithm for multi-modal multiobjective optimization

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PEERJ COMPUTER SCIENCE 2024年 10卷 e1839页

作者： Qu, Dan Xiao, Hualin Chen, Huafei Li, Hongyi China West Normal Univ Coll Math Educ Nanchong Peoples R China Sichuan Univ Sci & Engn Coll Math & Stat Zigong Peoples R China

Multi -modal multi -objective problems (MMOPs) have gained much attention during the last decade. These problems have two or more global or local Pareto optimal sets (PSs), some of which map to the same Pareto front (PF). This article presents a new affinity propagation clustering (APC) method based on the Multi -modal multiobjective differential evolution (MMODE) algorithm, called MMODE_AP, for the suit of CEC'2020 benchmark functions. First, two adaptive mutation strategies are adopted to balance exploration and exploitation and improve the diversity in the evolution process. Then, the affinity propagation clustering method is adopted to define the crowding degree in decision space (DS) and objective space (OS). Meanwhile, the non -dominated sorting scheme incorporates a particular crowding distance to truncate the population during the environmental selection process, which can obtain welldistributed solutions in both DS and OS. Moreover, the local PF membership of the solution is defined, and a predefined parameter is introduced to maintain of the local PSs and solutions around the global PS. Finally, the proposed algorithm is implemented on the suit of CEC'2020 benchmark functions for comparison with some MMODE algorithms. According to the experimental study results, the proposed MMODE_AP algorithm has about 20 better performance results on benchmark functions compared to its competitors in terms of reciprocal of Pareto sets proximity (rPSP), inverted generational distances (IGD) in the decision (IGDX) and objective (IGDF). The proposed algorithm can efficiently achieve the two goals, i.e., the convergence to the true local and global Pareto fronts along with better distributed Pareto solutions on the Pareto fronts.

关键词： differential evolution algorithm Affinity propagation Multi-modal multi-objective optimization

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Optimizing electric vehicle routing with nonlinear charging and time windows using improved differential evolution algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 5423-5458页

作者： Deng, Jiawen Zhang, Jihui Yang, Shengxiang Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Shandong Key Lab Ind Control Technol Qingdao 266071 Peoples R China De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH Leics England

In real-life, green logistics is prevalent with the advent of pollution reduction;therefore, electric vehicle routing problem (EVRP) gains more focus. However, nonlinear charging technique has not obtained adequate attention for EVRP with time windows. In this study, an improved differential evolution (IDE) algorithm is introduced to address a variant of EVRP, which involves time windows and partial recharging policy with nonlinear charging. In IDE, a productive approach is developed to instruct the electric vehicle to charge in advance. A modified crossover operator is proposed to make populations more diverse. Five local neighborhood operators and a simulated annealing algorithm are embedded to enhance search quality. Further, we generate 55 instances based on Solomon benchmark and Analysis of Variance is leveraged to manifest the efficacy. Similarly, three algorithms are selected for comparison, i.e., genetic algorithm, artificial bee colony algorithm, and adaptive large neighborhood search. Experimental comparisons reveal that IDE outperforms others.

关键词： Electric vehicle Scheduling differential evolution algorithm Routing

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Self-adaptive differential evolution algorithm with discrete mutation control parameters

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第3期42卷 1551-1572页

作者： Fan, Qinqin Yan, Xuefeng E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： evolutionary computation differential evolution algorithm Discrete mutation parameters Control parameter adaptation Mutation strategy adaptation

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Model Optimization for an Industrial Fluid Catalytic Cracking Riser-regenerator Unit by differential evolution algorithm

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PETROLEUM SCIENCE AND TECHNOLOGY 2015年第13-14期33卷 1380-1387页

作者： Long, J. Mao, M. S. Zhao, G. Y. E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

A differential evolution (DE) algorithm for determining 16 advanced turning model parameters of a 1.2 million industrial fluid catalytic cracking (FCC) unit modeling by HYSYS 8.4 is presented. Industrial data from a Chinese petroleum refinery were used to develop, train and check the model. Due to FCC complexity, the proposed model is capable of predicting the yield of products based on main operating conditions. The optimized FCC model is used for further optimized analysis of the FCC unit operating conditions based on the maximum of economic benefits. Prediction of the economic benefit of FCC unit increases 917 yuan/h at the optimized operating conditions.

关键词： fluid catalytic cracking optimization unit parameters differential evolution algorithm economic benefit

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Identification of bilinear systems using differential evolution algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2011年第3期36卷 281-292页

作者： Ozer, Saban Zorlu, Hasan Erciyes Univ Dept Electir & Elect Engn TR-38039 Kayseri Turkey

In this work, a novel identification method based on differential evolution algorithm has been applied to bilinear systems and its performance has been compared to that of genetic algorithm. Box Jenkins system and different type bilinear systems have been identified using differential evolution and genetic algorithms. The simulation results have shown that bilinear systems can be successfully and efficiently identified using these algorithms.

关键词： System identification bilinear system differential evolution algorithm box-Jenkins system

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Feature Enhancement of Medical Images using Morphology-Based Homomorphic Filter and differential evolution algorithm

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2010年第4期8卷 857-861页

作者： Oh, Jinsung Hwang, Heesoo Halla Univ Dept Elect Eng Wonju 220712 Kangwon South Korea

In this paper, we present a new morphology-based homomorphic filtering technique for feature enhancement in medical images. The proposed method is based on decomposing an image into morphological subbands. The homomorphic filtering is performed using the morphological subbands. The differential evolution algorithm is applied to find an optimal gain and structuring element for each subband. Simulations show that the proposed filter improves the contrast of the features in medical images.

关键词： differential evolution algorithm homomorphic filter image enhancement morphological filter

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