scholarly journals Optimization of steel moment frames with panel-zone design using an adaptive differential evolution

2020 ◽  
Vol 14 (2) ◽  
pp. 65-75
Author(s):  
Viet-Hung Truong ◽  
Ha Manh Hung ◽  
Pham Hoang Anh ◽  
Tran Duc Hoc
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Adaptive Mutation ◽  
Moment Frames ◽  
Steel Moment Frames ◽  
Panel Zone ◽  
Local Searches ◽  
Direct Design ◽  
Adaptive Differential Evolution ◽  
Mutation Strategy

Optimization of steel moment frames has been widely studied in the literature without considering shear deformation of panel-zones which is well-known to decrease the load-carrying capacity and increase the drift of structures. In this paper, a robust method for optimizing steel moment frames is developed in which the panel-zone design is considered by using doubler plates. The objective function is the total cost of beams, columns, and panel-zone reinforcement. The strength and serviceability constraints are evaluated by using a direct design method to capture the nonlinear inelastic behaviors of the structure. An adaptive differential evolution algorithm is developed for this optimization problem. The new algorithm is featured by a self-adaptive mutation strategy based on the p-best method to enhance the balance between global and local searches. A five-bay five-story steel moment frame subjected to several load combinations is studied to demonstrate the efficiency of the proposed method. The numerical results also show that panel-zone design should be included in the optimization process to yield more reasonable optimum designs. Keywords: direct design; differential evolution; optimization; panel-zone; steel frame.

scholarly journals Optimum sizing of members of truss structures using direct design and a self-adaptive mutationdifferential evolution

2021 ◽  
Vol 63 (2) ◽  
pp. 39-44
Author(s):  
Manh-Hung Ha ◽  
◽  
Hoang-Anh Pham ◽  
Keyword(s):  
Structural Design ◽  
Computational Cost ◽  
Truss Structures ◽  
Adaptive Mutation ◽  
De Algorithm ◽  
Inelastic Analysis ◽  
Local Searches ◽  
Direct Design ◽  
Mutation Strategy ◽  
Self Adaptive

Direct design using nonlinear inelastic analysis has been recently enabled for structural design as this approach can directly predict the behaviour of a structure as a whole, which eliminates capacity checks for individual structural members. However, the use of direct design is often accompanied by excessive computational efforts, especially for complicated structural design problems such as optimization or reliability analysis. In this study, we introduce an efficient method for the sizing optimization of truss structures employing nonlinear inelastic analysis for the direct design of structures. The objective function is the total weight of the structure while the strength and serviceability constraints are evaluated with nonlinear inelastic analysis. To save computational cost, an improved differential evolution (DE) algorithm is employed. Compared to the conventional DE algorithm, the proposed method has two major improvements: (1) a self-adaptive mutation strategy based on the p-best method to enhance the balance between global and local searches and (2) use of the multi-comparison technique (MCT) to reduce redundant structural analyses. The numerical results of a 72-bar truss case study demonstrate that the performance of the proposed method has significant advantages over the traditional DE method.

An Adaptive Differential Evolution Algorithm for Flow Shop Scheduling to Minimize Makespan

2013 ◽  
Vol 411-414 ◽  
pp. 2089-2092
Author(s):  
Yan Ping Zhou
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Flow Shop Scheduling ◽  
Evolution Process ◽  
Adaptive Mutation ◽  
Shop Scheduling ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Searching Ability

This paper proposed an adaptive differential evolution algorithm. The algorithm has an adaptive mutation factor which can be nonlinear reduced along with evolution process. Mutation factor is declined slowly in the beginning of evolution process in order to improve the global searching ability of the algorithm, and declined rapidly in the later of evolution process. The proposed algorithm is applied to solve flow shop scheduling to minimize makespan, computational experiments on a typical scheduling benchmark shows that the algorithm has a good performance.

scholarly journals Asynchronous differential evolution with selfadaptive parameter control for global numerical optimization

2018 ◽  
Vol 189 ◽  
pp. 03020 ◽  
Author(s):  
Tae Jong Choi ◽  
Yeonju Lee
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Differential Evolution Algorithm ◽  
Convergence Speed ◽  
Benchmark Problems ◽  
Benchmark Problem ◽  
Adaptive Differential Evolution ◽  
Mutation Strategy ◽  
Asynchronous Method ◽  
Evolution Algorithm

In this paper, we propose an extended self-adaptive differential evolution algorithm, called A-jDE. A-jDE algorithm is based on jDE algorithm with the asynchronous method. jDE algorithm is one of the popular DE variants, which shows robust optimization performance on various problems. However, jDE algorithm uses a slow mutation strategy so that its convergence speed is slow compared to several state-of-the-art DE algorithms. The asynchronous method is one of the recently investigated approaches that if it finds a better solution, the solution is included in the current population immediately so it can be served as a donor individual. Therefore, it can improve the convergence speed significantly. We evaluated the optimization performance of A-jDE algorithm in 13 scalable benchmark problems on 30 and 100 dimensions. Our experiments prove that incorporating jDE algorithm with the asynchronous method can improve the optimization performance in not only a unimodal benchmark problem but also multimodal benchmark problem significantly.

scholarly journals Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine

2019 ◽  
Vol 27 (1(133)) ◽  
pp. 67-77 ◽  
Author(s):  
Zhiyu Zhou ◽  
Chao Wang ◽  
Xu Gao ◽  
Zefei Zhu ◽  
Xudong Hu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Dictionary Learning ◽  
Gabor Filter ◽  
Differential Evolution Algorithm ◽  
Adaptive Mutation ◽  
Multi Scale ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Learning Machine

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

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