Adaptive Differential Evolution Based on New Mutation Strategy

2011 ◽  
Author(s):  
Shujun Bi ◽  
Jianjun Zhou
Keyword(s):  
Differential Evolution ◽  
New Mutation ◽  
Adaptive Differential Evolution ◽  
Mutation Strategy

scholarly journals A Global Multilevel Thresholding Using Differential Evolution Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Kanjana Charansiriphaisan ◽  
Sirapat Chiewchanwattana ◽  
Khamron Sunat
Keyword(s):  
Differential Evolution ◽  
Search Algorithm ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Threshold Values ◽  
New Mutation ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Otsu’S Function ◽  
Evolution Approach

Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.

scholarly journals lp Norm Smooth Inversion of Magnetic Anomaly Based on Improved Adaptive Differential Evolution

2021 ◽  
Vol 11 (3) ◽  
pp. 1072
Author(s):  
Wei Du ◽  
Lianzheng Cheng ◽  
Yuanfang Li
Keyword(s):  
Differential Evolution ◽  
Magnetic Anomaly ◽  
Ore Deposits ◽  
Parametric Estimation ◽  
Source Distribution ◽  
Average Technique ◽  
Lp Norm ◽  
Magnetic Inversion ◽  
Adaptive Differential Evolution ◽  
Mutation Strategy

Due to the approved applicability of differential evolution (DE) in geophysical problems, the algorithm has been widely concerned. The DE algorithms are mostly applied to solve the geophysical parametric estimation based on specific models, but they are rarely used in solving the physical property inverse problem of geophysical data. In this paper, an improved adaptive differential evolution is proposed to solve the lp norm magnetic inversion of 2D data, in which the perturbation direction in the mutation strategy is smoothed by using the moving average technique. Besides, a new way of updating the regularization coefficient is introduced to balance the effect of the model constraint adaptively. The inversion results of synthetic models demonstrate that the presented method can obtain a smoother solution and delineate the distributions of abnormal bodies better. In the field example of Zaohuoxi iron ore deposits in China, the reconstructed magnetic source distribution is in good agreement with the one inferred from drilling information. The result shows that the proposed method offers a valuable tool for magnetic anomaly inversion.

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 Adaptive Differential Evolution with Information Entropy-based Mutation Strategy

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Liujing Wang ◽  
Xiaogen Zhou ◽  
Tengyu Xie ◽  
Jun Liu ◽  
Guijun Zhang
Keyword(s):  
Differential Evolution ◽  
Information Entropy ◽  
Adaptive Differential Evolution ◽  
Mutation Strategy

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.

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