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Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm

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NONLINEAR DYNAMICS 2014年第1-2期77卷 61-71页

作者： Li, Xiangtao Yin, Minghao NE Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China

This paper is concerned with the parameter estimation of nonlinear chaotic system, which could be essentially formulated as a multi-dimensional optimization problem. In this paper, a hybrid algorithm by combining differential evolution with artificial bee colony is implemented to solve parameter estimation for chaotic systems. Hybrid algorithm combines the exploration of differential evolution with the exploitation of the artificial bee colony effectively. Experiments have been conducted on Lorenz system and Chen system. The proposed algorithm is applied to estimate the parameters of two chaotic systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to differential evolution, artificial bee colony, particle swarm optimization, and genetic algorithm from literature when considering the quality of the solutions obtained.

关键词： differential evolution algorithm Artificial bee colony Exploration Exploitation Chaotic system Parameter estimation

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Design of time modulated concentric circular and concentric hexagonal antenna array using hybrid enhanced particle swarm optimisation and differential evolution algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2014年第9期8卷 657-665页

作者： Mangoud, Mohab A. Elragal, Hassan M. Alshara, Mohamed T. Univ Bahrain Dept Elect & Elect Engn Isa Town Bahrain Univ Alexandria Fac Engn Dept Elect Engn Alexandria Egypt

Design of circular arrays (CAs) and hexagonal arrays (HAs) with low sidelobe level (SLL) and high directivity is usually achieved by increasing the number of array elements, which leads to a high undesired mutual coupling. Therefore this study presents an efficient optimisation method and a framework to show how to design multi-ring concentric CAs (CCA) and concentric HAs (CHAs) configurations using a hybrid enhanced particle swarm optimisation and differential evolution (hybrid EPSO/DE) optimisation technique. The presented optimum CCA and CHA have perfect invariant SLL and high directivity with low mutual coupling by keeping the inter-element spacing not less than half a wavelength which is not possible to be achieved in CA and HA arrangements. Different configurations with two-rings, three-rings and four-rings are presented. The rotation angle of outer rings and the complex excitations of array elements are first optimised. Then, this design is further optimised using a time modulation technique by controlling the switch-on times and the phases of elements excitations of the best CCA and CHA array designs. The presented time modulated (TM) concentric CA and TM concentric HA designs attain ultra-low SLL, reduced sideband level and maximised directivity besides reducing the dynamic range of the array excitations.

关键词： antenna arrays particle swarm optimisation phase modulation time modulated concentric circular antenna array concentric hexagonal antenna array differential evolution algorithm SLL sidelobe level mutual coupling multiring concentric CA CCA concentric HA CHA hybrid enhanced particle swarm optimisation hybrid EPSO-DE optimisation technique interelement spacing TM

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USING AN IMPROVED BEE MEMORY differential evolution algorithm FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

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JOURNAL OF BIOLOGICAL SYSTEMS 2014年第1期22卷 101-121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 Utm Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Biosci & Med Engn Dept Biol Sci Utm Skudai 81310 Johor Malaysia

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder-Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

关键词： Parameter Estimation differential evolution algorithm Kalman Filter Artificial Bee Colony algorithm Memory Feature

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Optimal coordination of directional over-current relays using informative differential evolution algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2014年第2期5卷 269-276页

作者： Singh, Manohar Panigrahi, B. K. Abhyankar, A. R. Das, Swagatam Indian Inst Technol Dept Elect Engn Delhi 110016 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India

Growing interconnection in distribution system creates new problem for protection engineers. Particularly the design of overcurrent relay coordination in such system is an independent area of research. With the availability of new artificial based optimization algorithm relay coordination research gain a new momentum. Well established artificial based optimization algorithm such as genetic and particle swam optimization are successfully applied for such applications. This paper discusses the application of informative differential evolution algorithm with self adaptive re-clustering technique for selection of TDS and PSM for optimal coordination of directional overcurrent relays. Both continuous as well as discrete version of informative differential evolution algorithm are used for optimization of relay setting. Proper combination of backup relays for each primary relay are identified by using LINKNET graph theory approach. Coordination of directional overcurrent is developed for 9 bus and IEEE 30 bus distribution systems. The aim of problem is to minimize the total operating time of primary relays and eliminate the miscoordination among the primary and backup relay pairs. Discrete types of settings for electromechanical types of relay are also discussed in this paper. Moreover, the relay coordination problem is modified for providing optimal coordination time interval between 0.2 and 0.8 s among all primary and backup relays pairs. The results are compared with hybrid of genetic algorithm - nonlinear programming and sequential quadratic programming. Digsilient power factory software is used for verification of result. (C) 2013 Elsevier B.V. All rights reserved,

关键词： Over current relay Relay coordination differential evolution algorithm

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A Hybrid differential evolution algorithm Integrated with Particle Swarm Optimization

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Journal of Donghua University(English Edition) 2014年第2期31卷 197-200页

作者：范勤勤颜学峰 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization （PSODE） is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. differential evolution （ DE） operators are used to evolve the original population. And, particle swarm optimization （PSO） is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.

关键词： differential evolution algorithm particle swann optimization self-adaptive co-evolution

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A Novel Hybrid Hunger Games Search algorithm With differential evolution for Improving the Behaviors of Non-Cooperative Animals

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IEEE ACCESS 2021年 9卷 164188-164205页

作者： Li, Shaolang Li, Xiaobo Chen, Hui Zhao, Yuxin Dong, Junwei Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China Lishui Univ Coll Engn Lishui 323000 Peoples R China

Inspired by the behaviors of animals in the state of starvation, hunger games search algorithm (HGS) is proposed. HGS has shown competitive performance among other meta-heuristic (MH) algorithms. However, HGS tends to stagnate in local optimal for some complex optimization problems and remains premature convergence. Therefore, to solve these problems and enhance the diversity of the population, a modified HGS based on the operators of the differential evolution algorithm (DE), chaotic local search (CLS) strategies, and evolutionary population dynamics technique (EPD) is proposed (named DECEHGS). The proposed DECEHGS algorithm consists of two stages: in the first stage, based on the animals' behaviors, we use different evolutionary methods to update animals' positions;in the second stage, the CLS strategy and EPD technique are combined to prevent premature convergence and stagnation in a local optimum. The proposed algorithm was evaluated using IEEE CEC2014 and IEEE CEC2017 mathematical functions and four engineering problems. The experimental results demonstrate that DECEHGS has competitive performance in global optimization tasks and engineering problems compared with state-of-the-art algorithms.

关键词： Statistics Sociology Optimization Licenses Clustering algorithms Search problems Particle swarm optimization Hunger games search differential evolution algorithm chaotic local search evolutionary population dynamics optimization

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An Improved Cuckoo Search algorithm Utilizing Nonlinear Inertia Weight and differential evolution for Function Optimization Problem

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IEEE ACCESS 2021年 9卷 161352-161373页

作者： Zhang, Cheng-Xu Zhou, Kai-Qing Ye, Shao-Qiang Zain, Azlan Mohd Jishou Univ Coll Informat Sci & Engn Jishou 416000 Peoples R China Univ Teknol Malaysia Big Data Ctr Skudai 80310 Johor Malaysia

This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local search capabilities. Compared with other CS variants, two strategies are proposed in this paper to improve the properties of the WCSDE. On the one hand, a non-linearly decreasing inertia weight with the number of evolutionary iterations is employed in the WCSDE to improve the update method of the bird's nest position, enhance the balance between the exploration and development capabilities, and strengthen the local optimization capability. On the other hand, the mutation and cross-selection mechanisms of the differential evolution (DE) algorithm are introduced to make up for the lack of the mutual relationship between the populations, avoid the loss of practical information, and increase the convergence accuracy. In the experiment part, 13 classic benchmark functions are selected to execute the function optimization tasks among the standard CS, the WCSDE, and other four CS variants to verify the effectiveness of the proposed algorithm from two aspects. The results and corresponding statistical analysis reveal that the proposed algorithm has better global search ability and strengthener robustness.

关键词： Statistics Sociology Optimization Standards Convergence Benchmark testing Heuristic algorithms Cuckoo search algorithm differential evolution algorithm nonlinear inertia weight adaptive adjustment strategy function optimization

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Adaptive ε-Constraint Multi-Objective evolutionary algorithm Based on Decomposition and differential evolution

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IEEE ACCESS 2021年 9卷 17596-17609页

作者： Liu, Bing-Jie Bi, Xiao-Jun Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Minzu Univ China Dept Informat Engn Beijing 100081 Peoples R China

To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive epsilon-constraint multi-objective evolutionary algorithm based on decomposition and differential evolution (epsilon-MOEA/D-DE). First, an adaptive epsilon-constraint strategy based on both evolution generation and constraint violation is designed to make better use of excellent evolution individuals and improve population diversity. Then, an adaptive differential evolution (DE) mutation strategy with full utilization of infeasible individuals is proposed to increase search efficiency and avoid falling into the local optimum. Finally, a replacement mechanism is suggested to take advantage of the infeasible individuals in the population with better objective function values and constraint violation degree, and thus both diversity and convergence are well coordinated. A comparative experiment with four other excellent constrained multi-objective algorithms was implemented on standard constrained multi-objective optimization problems (CF series), and the results showed that the diversity and convergence of our algorithm were both improved.

关键词： Statistics Sociology Optimization Convergence Linear programming Search problems Diversity reception Constrained many-objective optimization epsilon-constrain handling techniques differential evolution algorithm MOEA/D

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A Study on Strategies and Mutant Factor in differential evolution algorithm for FIR Filter Design

A Study on Strategies and Mutant Factor in Differential Evol...

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1st International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Kotha, Srinivasa Reddy Vij, Saumya Sahoo, S. K. Birla Inst Technol & Sci Sch Elect & Elect Engn Pilani Rajasthan India

ISBN: (纸本)9781479928668

differential evolution (DE) is a population-based optimizer that obtains the solution by iteratively improving the given measure of quality. In this work, DE has been applied in FIR Filter design to optimize the frequency response and the signed-power-of-two (SPT) terms of the filter coefficients. DE is influenced mainly by three design parameters, Strategy S, Mutant factor F and Cross-over probability Cr. The focus of this paper is the mutation step of DE which involves F and S. Simulations with all possible combinations of F and S show that strategy 2 and 3 with F=0.7 give the desired frequency response till filter order N=50. However, with consideration of SPT terms, S=2 and F=0.7 gives the optimized solution.

关键词： FIR filter SPT Mutation Strategies Mutant Factor differential evolution algorithm

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Stability Enhancement in SVC based Controller Design using differential evolution algorithm

Stability Enhancement in SVC based Controller Design using D...

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International Conference on High Performance Computing and Applications (ICHPCA)

作者： Mohapatra, Sangram Keshori Dash, Bhabani Sankar CV Raman Coll Engn Dept Elect Engn Bhubaneswar Orissa India

ISBN: (纸本)9781479959587

This paper describes the power system stability enhancement in Static Var Compensator (SVC) based damping controller. Here Proportional Integral Derivative (PID) and Lead-Lag (LL) controllers with associated time delay are considered for the proposed study. To find out the optimal controller parameters, differential evolution algorithm is applied. The performance of the given controller is analysed under different disturbances for single machine infinite bus power system. To effectiveness and robustness of the proposed controllers, the various simulation results are compared under various disturbances and operating conditions.

关键词： static var compensator controller design power system stability differential evolution algorithm single machine infinite-bus power system

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