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Tunneling parameters optimization based on multi-objective differential evolution algorithm

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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A constrained differential evolution algorithm for calculation of two-phase equilibria at given volume, temperature and moles

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JOURNAL OF COMPUTATIONAL PHYSICS 2024年 518卷

作者： Zhan, Yaqian Hu, Zhongbo Kou, Jisheng Hong, Nan Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou 434000 Hubei Peoples R China Shaoxing Univ Sch Civil Engn Shaoxing 312000 Zhejiang Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan 432000 Hubei Peoples R China

A variety of strategies including two types of direct solution methods based on Newton's method and indirect solution methods based on thermodynamic principles have been developed, which can find satisfactory solutions for the phase equilibrium calculation problem at given volume, temperature and moles (NVT-flash). However, these methods are sensitive to the initial values and depend on the objective function's derivative during calculation. To resolve these deficiencies, a constrained differential evolution algorithm (abbreviated as CDE) for calculating two-phase equilibria at given volume, temperature, and moles is presented in this paper. The total Helmholtz free energy is regarded as the objective function, and the moles vector and volume of a certain phase are taken as the decision variables. This paper deals with the constraints of the NVT-flash problem by using direct search method and exterior point method. The results of the investigations on pure substance, binary and ternary mixtures are in agreement with the published data, which proves the effectiveness in solving the NVT-flash problem and the energy attenuation characteristic of the algorithm. The proposed algorithm represents the first successful attempt to apply the meta heuristic optimization algorithm to solve the phase equilibrium computation under NVT-flash, and it shows the promising application prospect of meta-heuristic optimization algorithms for solving the phase equilibrium calculation problems.

关键词： differential evolution algorithm Helmholtz free energy minimization NVT-flash Two-phase equilibrium

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A green energy model for resource allocation in computational grid using differential evolution algorithm

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WIRELESS NETWORKS 2021年第7期27卷 4773-4794页

作者： Rafie, Mahnaz Islamic Azad Univ Dept Comp Engn Ramhormoz Branch Ramhormoz Iran

Energy loss due to the complete equipping and overstatement of infrastructure networks has recently prompted internet service providers to reduce energy consumption. Increasing energy costs and environmental awareness have recently raised concerns about grid power consumption and the underlying structure of services;therefore, one of the top priorities for service providers is to reduce energy consumption. In this paper, a method based on the differential evolution optimization algorithm is introduced to reduce the energy consumption costs, network load balancing, and execution time. To increase the convergence speed in the optimization algorithm, two Mamdani fuzzy inference systems have been used which are used in the optimization objective function to determine the fitness amount of each allocation. To better understand proposed algorithm behavior, the results of the differential evolution algorithm are compared with the AGA and MPGA methods. Simulation results show that the proposed algorithm achieves more than 30% performance improvement in comparison with the previous algorithms.

关键词： differential evolution algorithm Green energy Grid Resource allocation

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Bionic Geomagnetic Navigation Method for AUV Based on differential evolution algorithm

Bionic Geomagnetic Navigation Method for AUV Based on Differ...

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OCEANS Hampton Roads Conference

作者： Zhou, Yuqi Niu, Yun Liu, Mingyong Northwestern Polytech Univ Sch Marine Engn Xian Peoples R China China State Shipbldg Corp Lab Sci & Technol Marine Nav & Control Beijing Peoples R China

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

Bionic navigation based on geomagnetic feature search does not require prior geomagnetic maps. It is one of the effective navigation methods for autonomous underwater vehicle (AUV) underwater. It has the characteristics of high concealment and low cost. The main problem is the low efficiency of search and navigation. This paper proposes a new bionic navigation algorithm based on differential evolution algorithm (DE), which can make rapid magneto tropic heading decisions based on geomagnetic trends. By improving the mutation mechanism of the evolution strategy, the algorithm obtains better individuals in the population, thereby improving the efficiency of search and navigation. Simulation experiment results show that the algorithm can enable AUV to complete navigation tasks, and is superior to evolutionary algorithms (EAs) in terms of navigation search distance, time-consuming, and navigation accuracy.

关键词： autonomous underwater vehicle (AUV) differential evolution algorithm geomagnetic bionic navigation

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On the use of the differential evolution algorithm for truss-type structures optimization

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APPLIED SOFT COMPUTING 2024年 161卷

作者： Contreras-Bejarano, Oscar Villalba-Morales, Jesus Daniel Pontificia Univ Catolica Chile Dept Struct & Geotech Engn Santiago Chile Pontificia Univ Javeriana Dept Civil Engn Bogota Colombia

In recent decades, bio-inspired numerical algorithms have emerged as an alternative for optimizing the structural design of trusses. The differential evolution algorithm (DEA) has demonstrated both good performance and ease of implementation. However, unlocking the full potential of DEA to address engineering problems poses a significant challenge, necessitating a strategic and informed definition of each component of the algorithm. This research systematically evaluates the influence of defining DEA components on improving the reliability of truss optimization. The algorithm structure of DEA was configured for five aspects: (I) the mutation operator, (II) inclusion of multi -modal techniques, (III) inclusion of parameter control techniques, (IV) definition of the initial population, and (V) local search heuristics. A comprehensive evaluation is conducted to assess the performance of 23 DEA configurations in optimizing eight planar and spatial trusses, varying in size from 10 to 163 elements. Assessment is based on key criteria such as optimal weight, robustness, and computational cost, providing a thorough basis for comparison. The results showed that no tested DEA configuration is the best for all trusses. Instead, the study revealed the presence of recommendable configurations, each tailored to the specific complexities and scales inherent in various truss structures. The integration of multimodal and local search techniques proves particularly advantageous for larger trusses, amplifying the algorithm's exploratory capabilities to effectively navigate and uncover optimal regions. In contrast, using parameter control technique was more effective in optimizing smaller trusses, capitalizing on the rapid exploration of potential optimal areas in a smaller search space. There was a mutation operator that produced the best results for large trusses and good results for smaller structures. This operator uses the target vector as the base vector and guides its movement f

关键词： differential evolution algorithm Problem knowledge heuristics Multi-modal optimization Parameter control Truss optimization

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Eigen-Structure Assignment-Based differential evolution algorithm for T-S Fuzzy Control Tuning Applied to Water-Turbine Governing System

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IEEE ACCESS 2021年 9卷 39322-39332页

作者： Zou, Yidong Qian, Jing Zeng, Yun Guo, Yaqing Wu, Mengyun Wang, Fangfang Mei, Hong Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650000 Yunnan Peoples R China Kunming Univ Sci & Technol City Coll Kunming 650093 Yunnan Peoples R China

In this paper, a novel Takagi-Sugeno (T-S) fuzzy controller based on eigenstructure assignment (EA) and optimized by differential evolution algorithm (DE) is proposed, and the application of this control strategy in the hydro-turbine governing system (HTGS) is studied. Based on the non-linear model of HTGS, the corresponding state-space equations (SSE) are obtained by linearization through multiple equilibrium points. Combining with the principle of T-S fuzzy control, a T-S fuzzy model of HTGS integrating multiple SSE with generator power angle as a prerequisite is established. This paper adopts the EA method to design the fitness function in the optimization process and uses DE to complete the optimization operation to improve the performance of T-S fuzzy control in HTGS. Which makes each Linear-Quadratic-Regulator (LQR) controller gain in the fuzzy control reach the optimal under the constraint conditions. The simulation results show that compared with the standard fitness functions IAE (integral absolute error), ITAE (integral time absolute error) and ISE (integral square error), the fitness function designed using the EA method can expand the angle between the left and right eigenvectors of the T-S closed-loop system, and push the closed-loop pole from the imaginary axis to the left. That will make the adjustment time of the system shorter and the robustness against external interferences enhanced. The results also show that the proposed control strategy has better dynamic performance during the non-linear motion of HTGS and is superior to the existing control strategy.

关键词： Licenses Takagi-Sugeno model Mathematical model Fuzzy control Tuning Optimization Indexes Hydro-turbine governing system fuzzy control linear-quadratic-regulator differential evolution algorithm controller optimization

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differential evolution algorithm for Ship Path Planning in Open Waters

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2021年第4期29卷 476-486页

作者： Kang, Yu-Tao Chen, Wei-Jiong Wang, Jin-Hui Shanghai Maritime Univ Coll Ocean Sci & Engn 1550 Hai Gang RdPudong New Area Shanghai Peoples R China

A basic problem in the design of a decision support system for ship collision avoidance is path optimization in a complex and dynamic navigational environment. This paper introduces a new path planning method based on the differential evolution (DE) algorithm to calculate a safe, optimal path for a ship. The algorithm was tested on a set of traffic scenarios typically encountered in open waters. The simulation test results prove the method's ability to solve a path planning problem for ships. We also discuss the optimality, consistency, and performance of the algorithm and provide a comparison of this algorithm with the particle swarm optimization (PSO) algorithm. The comparison results clearly show a significant advantage of the DE algorithm over the PSO algorithm in the areas of output optimality, algorithm consistency, and execution efficiency.

关键词： Collision avoidance Path planning differential evolution algorithm

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An adaptive hybrid differential evolution algorithm for continuous optimization and classification problems

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10841-10867页

作者： Rauf, Hafiz Tayyab Bangyal, Waqas Haider Khan Lali, M. Ikramullah Univ Gujrat Dept Comp Sci Gujrat Pakistan Univ Educ Dept Informat Sci Lahore Pakistan

differential evolution (DE) is recognized as a simplistic yet robust evolutionary algorithm: it has been utilized to tackle different challenging optimization problems in various science and engineering disciplines. DE has some disadvantages, such as premature convergence and slow convergence rate, leading to the worst DE execution arrangement. Two DE variations named adaptive parameter selection-based DE (APSDE) and chaotic map hybridization based on DE and PSO (CMHDE-PSO) have been proposed to tackle the issues mentioned above. The proposed variants contain three unique advantages for APSDE: (1) new population initialization scheme to keep up the decent variety of the population diversity;(2) controlled mutation factor technique followed by adaptive decreasing parameter selection procedure;(3) novel mutation strategy with a specific weighted pattern to determine the mutant vector for the mutation operation. Similarly, for CMHDE-PSO (1) novel distribution called Torus for the selection of initial population located in the search space;(2) new parameter adoption technique based on chaotic circle maps defined by chaos theory;(3) average pattern means of two different mutation strategies;(4) and lastly the hybridization of proposed improved DE with PSO to supports DE escaping local minima. Both APSDE and CMHDE-PSO are compared with several standard non-DE old-fashioned optimization algorithms and various advanced DE variants. We accomplish definite experiments behind the powerful searching technique by applying the APSDE and CMHDE-PSO-based mutation and parameter selection strategy for the function optimization and weight optimization of feed-forward neural networks (FFNN) on real-world data classification problems. For data classification performance evaluation, 10 data sets are utilized from the repository of UCI machine learning. Experimental results showed that APSDE and CMHDE-PSO extensively beat different EAs in all test functions and obtained higher accuracy wi

关键词： differential evolution algorithm Premature convergence Chaotic maps Adaptive parameter selection

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An adaptive differential evolution algorithm with population size reduction strategy for unconstrained optimization problem

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APPLIED SOFT COMPUTING 2023年 138卷

作者： Zhang, Xiaoyan Liu, Qianqian Qu, Yawei Xian Univ Sci & Technol Coll Comp Sci & Technol Xian 710054 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710000 Peoples R China Jinling Inst Technol Coll Intelligent Sci & Control Engn Nanjing 211169 Peoples R China

The differential evolution (DE) algorithm is a heuristic random search algorithm that optimizes the problem based on population evolution. It has been widely studied for its advantages of fewer control parameters, fast convergence speed, and strong robustness. Here, an Adaptive Guided differential evolution algorithm on Mutation, Parameter and Population (AGDE-MPP) is proposed, which has improved the DE algorithm by adopting a new mutation scheme, a parameter adaptation scheme, and a non-linear population size reduction strategy. The new mutation scheme uses two ordered difference vectors to perturb the mutation direction of the base vector. The parameter adaptation scheme generates the mutation factor by the Cauchy distribution with the mean of the Lehmer mean value from the historical successful mutation factor pool. The new non-linear population size reduction strategy adopts a hyperbolic tangent function curve to control the population size. This adaptive algorithm can balance the exploration and exploitation capabilities at different stages of evolution and obtain solutions with high accuracy and fast convergence speed. The performance of AGDE-MPP and several state-of-the-art algorithms were evaluated on CEC2013 and CEC2017 test suites for 10, 30, and 50 dimensions. The experimental results show that the performance of AGDE-MPP is superior to almost compared algorithm and has strong competitiveness.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： differential evolution algorithm Novel mutation scheme Parameter adaptation scheme Population size reduction

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