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A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems

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APPLIED INTELLIGENCE 2015年第4期42卷 642-660页

作者： Yi, Wenchao Gao, Liang Li, Xinyu Zhou, Yinzhi Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The differential evolution (DE) algorithm is a notably powerful evolutionary algorithm that has been applied in many areas. Therefore, the question of how to improve the algorithm's performance has attracted considerable attention from researchers. The mutation operator largely impacts the performance of the DE algorithm The control parameters also have a significant influence on the performance. However, it is not an easy task to set a suitable control parameter for DE. One good method is to considering the mutation operator and control parameters simultaneously. Thus, this paper proposes a new DE algorithm with a hybrid mutation operator and self-adapting control parameters. To enhance the searching ability of the DE algorithm, the proposed method categorizes the population into two parts to process different types of mutation operators and self-adapting control parameters embedded in the proposed algorithm framework. Two famous benchmark sets (including 46 functions) are used to evaluate the performance of the proposed algorithm and comparisons with various other DE variants previously reported in the literature have also been conducted. Experimental results and statistical analysis indicate that the proposed algorithm has good performance on these functions.

关键词： Hybrid mutation operator Self-adapting parameter control strategy differential evolution algorithm Global optimization problems

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Translation control of an immersed tunnel element using a multi-objective differential evolution algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 158-165页

作者： Liao, Qing Fan, Qin-Qin Li, Jun-Jun Shanghai Maritime Univ Logist Res Ctr 1550 Haigang Ave Shanghai 201306 Peoples R China Shanghai Maritime Univ Merchant Marine Coll 1550 Haigang Ave Shanghai 201306 Peoples R China

The translation of an immersed tunnel element under a water current flow is a typical optimization problem, which has been solved by various optimization approaches. Also, this problem is often addressed as a single objective optimization in most previous studies. However, the translation control of the immersed tunnel element often involves at least two conflicting objectives in actual situation. Therefore, converting the translation control problem of the immersed tunnel element into a multi-objective optimization problem is necessary and vital. Subsequently, a recently proposed multi-objective differential evolution algorithm is employed to solve this optimization problem. Results indicate that the multi-objective differential evolution algorithm can produce the most promising result when compared with other competitors, and provide a set of non-dominated solutions to assist decision-makers in completing the translation of the immersed tunnel element based on different targets and changing environment. Namely, the current study can help decision-makers to achieve a good trade-off among different objectives such as transport efficiency, transport cost, and transport safety in the translation control of the immersed tunnel element.

关键词： Immersed tunnel element Multi-objective optimization differential evolution algorithm evolutionary computation

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DSTATCOM allocation in distribution networks considering reconfiguration using differential evolution algorithm

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ENERGY CONVERSION AND MANAGEMENT 2011年第7期52卷 2777-2783页

作者： Jazebi, S. Hosseinian, S. H. Vahidi, B. Amirkabir Univ Technol Dept Elect Engn Tehran 158754413 Iran

The main idea in distribution network reconfiguration is usually to reduce loss by changing the status of sectionalizing switches and determining appropriate tie switches. Recently Distribution FACTS (DFACTS) devices such as DSTATCOM also have been planned for loss reduction and voltage profile improvement in steady state conditions. This paper implements a combinatorial process based on reconfiguration and DSTATCOM allocation in order to mitigate losses and improve voltage profile in power distribution networks. The distribution system tie switches. DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. differential evolution algorithm (DEA) has been used to solve and overcome the complicity of this combinatorial nonlinear optimization problem. To validate the accuracy of results a comparison with particle swarm optimization (PSO) has been made. Simulations have been applied on 69 and 83 busses distribution test systems. All optimization results show the effectiveness of the combinatorial approach in loss reduction and voltage profile improvement. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Distribution network Power losses Reconfiguration differential evolution algorithm DFACTS DSTATCOM

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Design of digital FIR filters using differential evolution algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2006年第5期25卷 649-660页

作者： Karaboga, Nurhan Cetinkaya, Bahadir Erciyes Univ Fac Engn Dept Elect Engn TR-38039 Kayseri Turkey

The differential evolution (DE) algorithm is a new heuristic approach with three main advantages: it finds the true global minimum of a multimodal search space regardless of the initial parameter values, it has fast convergence, and it uses only a few control parameters. The DE algorithm, which has been proposed particularly for numeric optimization problems, is a population-based algorithm like the genetic algorithms and uses similar operators: crossover, mutation, and selection. In this work, the DE algorithm has been applied to the design of digital finite impulse response filters, and its performance has been compared to that of the genetic algorithm and least squares method.

关键词： FIR filter design differential evolution algorithm genetic algorithm

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Tunneling parameters optimization based on multi-objective differential evolution algorithm

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SOFT COMPUTING 2021年第5期25卷 3637-3656页

作者： Wang, Hongyuan Wang, Jingcheng Zhao, Yaqi Xu, Haotian Shanghai Jiao Tong Univ Minist Educ China Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

This paper focuses on the geological adaptive control of tunneling boring machine (TBM). To deal with the issue that the geological condition is uncertain, clustering analysis is used to identify geological types based on two important indices indicating the strength of rock. Based on the results from above, the principles of variation and range of tunneling parameters under different rock condition are determined. Furthermore, considering several vital performances of TBM during tunneling operation, a multi-objective optimization problem is proposed. In the light of non-dominated sorting and crowded distance evaluation concepts in non-dominated sorting genetic algorithm-II, the proposed multi-objective optimization problem is solved by using differential evolution algorithm. Based on the practical construction data, the simulation results show that the proposed method is effective in improving the performance of tunneling operation of TBM, compared with unoptimized operating actions from current systems.

关键词： Multi-objective optimization Clustering analysis differential evolution algorithm Tunnel boring machine Geological adaptive control

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Follow-the-leader motion strategy for multi-section continuum robots based on differential evolution algorithm

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2021年第4期48卷 589-601页

作者： Gao, GuoHua Wang, Pengyu Wang, Hao Beijing Univ Technol Coll Mech Engn & Appl Elect Technol Beijing Peoples R China Aarhus Univ Dept Mech & Prod Engn Aarhus Denmark

Purpose The purpose of this paper is to present a follow-the-leader motion strategy for multi-section continuum robots, which aims to make the robot have the motion ability in a confined environment and avoid a collision. Design/methodology/approach First, the mechanical design of a multi-section continuum robot is introduced and the forward kinematic model is built. After that, the follow-the-leader motion strategy is proposed and the differential evolution (DE) algorithm for calculating optimal posture parameters is presented. Then simulations and experiments are carried out on a series of predefined paths to analyze the performance of the follow-the-leader motion. Findings The follow-the-leader motion can be well performed on the continuum robots this study proposes in this research. The experimental results show that the deviation from the path is less than 9.7% and the tip error is no more than 15.6%. Research limitations/implications Currently, the follow-the-leader motion is affected by the following factors such as gravity and continuum robot design. Furthermore, the position error is not compensated under open-loop control. In future work, this paper will improve the accuracy of the robot and introduce a closed-loop control strategy to improve the motion accuracy. Originality/value The main contribution of this paper is to present an algorithm to generate follow-the-leader motion of the continuum robot based on DE. This method is suitable for solving new arrangements in the process of following a nonlinear path. Then, it is expected to promote the engineering application of the continuum robot.

关键词： Continuum robot differential evolution algorithm Follow-the-leader Motion strategy Multi-section

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Modeling and optimization of CNC milling of AISI 1050 steel by a regression based differential evolution algorithm (DEA)

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MATERIALS TESTING 2016年第7-8期58卷 632-639页

作者： Esme, Ugur Kulekci, Mustafa Kemal Ustun, Deniz Buldum, Baris Kazancoglu, Yigit Ocalir, Seref Mersin Univ Tarsus Fac Technol Dept Automot Engn Tarsus Mersin Turkey Mersin Univ Dept Mech Engn Tarsus Mersin Turkey Mersin Univ Adv Technol Educ Res & Applicat Ctr Tarsus Mersin Turkey Izmir Univ Econ Dept Business Adm Izmir Turkey Mersin Univ Grad Sch Nat & Appl Sci Tarsus Mersin Turkey

The present study is aimed at finding an optimization strategy for the CNC pocket milling process based on regression analysis including differential evolution algorithm (DEA). Milling parameters such as cutting speed, feed rate and depth of cut have been designed using rotatable central composite design (CCD). The AISI 1050 medium carbon steel has been machined by a high speed steel (HSS) flat end cutter tool with 8 mm diameter using the zig-zag cutting path strategy under air flow condition. The influence of milling parameters has been examined. The model for the surface roughness, as a function of milling parameters, has been obtained using the response surface methodology (RSM). Also, the power and adequacy of the quadratic mathematical model have been proved by analysis of variance (ANOVA) method. Finally, the process design parameters have been optimized based on surface roughness using bio-inspired optimization algorithm, called differential evolution algorithm (DEA). The enhanced method proposed in this study can be readily applied to different metal cutting processes with greater and faster reliability.

关键词： CNC milling response surface methodology differential evolution algorithm optimization

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Improved differential evolution algorithm for Multi-Target Response Inversion Detected by a Portable Transient Electromagnetic Sensor

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IEEE ACCESS 2020年 8卷 208107-208119页

作者： Chen, Shudong Zhang, Shuang Luan, Xinqun Liu, Zhaohe Jilin Univ Coll Elect Sci & Engn Changchun 130012 Peoples R China Sci & Technol Near Surface Detect Lab Wuxi 214035 Jiangsu Peoples R China

The superimposed response of multi-target causes difficulty in locating and characterizing each target when detecting unexploded ordnance with a portable transient electromagnetic sensor, constructed with a single-layer transmitting coil and five three-component receiving coils. differential evolution (DE) algorithm is improved here with Gram-Schmidt orthogonalization and position rearrangement for superimposed response inversion based on the multisource model, which represents the multi-target response with a set of magnetic dipoles distributed over the interrogated area. The Gram-Schmidt orthogonalization turns the coefficient matrix of each target into an orthonormal basis. Accordingly, the best magnetic polarization tensor can be directly extracted from the superimposed response without inverting large and potentially ill-conditioned matrices. The position rearrangement groups the positions of individuals in the contemporary population to maximize the likelihood that the positions in the same group belong to the same target. The convergence speed of multi-target inversion is accelerated with the crossover operation of DE algorithm performed within the groups. Simulated experiment results show that the error in estimated position and characteristic response for improved DE algorithm is only 10% of that of the conventional DE algorithm. Field experiment is also conducted, and its results show that the error in estimated position for improved DE algorithm is only 20% of that of the conventional DE algorithm. The improved DE algorithm can accurately estimate the position and characteristic response of each target from superimposed response.

关键词： Statistics Sociology Mathematical model Electromagnetics Transient analysis Electromagnetic interference Silicon Unexploded ordnance portable transient electromagnetic sensor differential evolution algorithm Gram– Schmidt orthogonalization multisource model

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Bernstein-Levy differential evolution algorithm for numerical function optimization

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 6603-6621页

作者： Civicioglu, Pinar Besdok, Erkan Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Fac Engn Dept Geomat Engn Kayseri Turkey

differential evolutionary (DE) algorithm is one of the most frequently used evolutionary computation method for the solution of non-differentiable, complex and discontinuous real value numerical problems. The analytical structure of the mutation and crossover operators used by DE and the initial values of the parameters of the relevant operators affect the problem-solving ability of DE. Unfortunately, there is no analytical method for selecting and initializing the best artificial genetic operators that DE can use to solve a problem. Therefore, there is a need to develop new evolutionary search methods that are parameter-free and insensitive to the artificial genetic operators they use. In this paper, the Bernstein-Levy differential evolution (BDE) algorithm, which has a unique elitist-mutation operator and a Bernstein polynomials-based stochastic parameter-free crossover operator, is introduced. The numerical problem-solving success of BDE is statistically evaluated by using 30 benchmark problems of CEC2014 in the numerical experiments presented. BDE's success in solving the related benchmark problems is statistically compared with six state-of-the-art comparison algorithms. In this paper, three real-world optimization problems are also solved by using the proposed algorithm, BDE. According to statistics generated from the experimental results, BDE is statistically better than comparison methods in solving the related real-world problems.

关键词： differential evolution algorithm Viewshed analysis Hyperspectral imaging Radio coverage maximization

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Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 2645-2650页

作者： Bastuerk, Alper Guenay, Enis Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey Erciyes Univ Dept Elect & Elect Engn TR-38039 Kayseri Turkey

A cellular neural network (CNN) based edge detector optimized by differential evolution (DE) algorithm is presented. Cloning template of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing edge detectors and offers Superior performance in edge detection in digital images. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Cellular neural networks Cloning template differential evolution algorithm Edge detection

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