scholarly journals Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ching-Yun Kao ◽  
Shih-Lin Hung ◽  
Budy Setiawan
Keyword(s):  
Differential Evolution ◽  
Random Search ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Premature Convergence ◽  
Truss Structures ◽  
Adaptive Mutation ◽  
Exploration And Exploitation ◽  
De Algorithm ◽  
Mutation Strategies

The performance of differential evolution (DE) mostly depends on mutation operator. Inappropriate configurations of mutation strategies and control parameters can cause stagnation due to over exploration or premature convergence due to over exploitation. Balancing exploration and exploitation is crucial for an effective DE algorithm. This work presents an enhanced DE (EDE) for truss design that utilizes two new strategies, namely, integrated mutation and adaptive mutation factor strategies, to obtain a good balance between the exploration and exploitation of DE. Three mutation strategies (DE/rand/1, DE/best/2, and DE/rand-to-best/1) are combined in the integrated mutation strategy to increase the diversity of random search and avoid premature convergence to a local minimum. The adaptive mutation factor strategy systematically adapts the mutation factor from a large value to a small value to avoid premature convergence in the early searching period and to increase convergence to the global optimum solution in the later searching period. The outstanding performance of the proposed EDE is demonstrated through optimization of five truss structures.

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.

scholarly journals A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Lijin Wang ◽  
Yiwen Zhong ◽  
Yilong Yin ◽  
Wenting Zhao ◽  
Binqing Wang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Iteration Process ◽  
Global Optimum ◽  
Backtracking Search ◽  
De Algorithm ◽  
Search Optimization ◽  
Backtracking Search Optimization Algorithm ◽  
Mutation Strategies ◽  
Multimodal Problems

The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

A Hybrid Differential Evolution Algorithm for Binary CSPs

2010 ◽  
Vol 108-111 ◽  
pp. 328-334 ◽  
Author(s):  
Hong Jie Fu
Keyword(s):  
Differential Evolution ◽  
Mutation Rate ◽  
Differential Evolution Algorithm ◽  
Premature Convergence ◽  
De Algorithm ◽  
Wrong Direction ◽  
Adaptive Adjustment ◽  
Evolution Algorithm

A novel hybrid elements exchange/electromagnetism meta-heuristic differential evolution algorithm, named EEMDE, is proposed in this paper, avoiding the premature convergence of original DE algorithm. A metric to measure the Simplification of force exerted on a point is defined as the mutation rate F in the EEMDE, which is used to get an adaptive adjustment of F. EEMDE may produce slight disturbance on the original vector for enhancing the exploring capacity and avoid the DE to the "uphill" in the wrong direction forward. Experiments demonstrate that the convergence of EEMDE is faster than DE and simulations based on some CSPs express the effectiveness, efficiency and robustness of it.

scholarly journals Spacecraft Multiple-Impulse Trajectory Optimization Using Differential Evolution Algorithm with Combined Mutation Strategies and Boundary-Handling Schemes

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yuehe Zhu ◽  
Hua Wang ◽  
Jin Zhang
Keyword(s):  
Differential Evolution ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Global Optimum ◽  
Local Optimum ◽  
Local Optima ◽  
Trial Vector ◽  
Mutation Strategies

Since most spacecraft multiple-impulse trajectory optimization problems are complex multimodal problems with boundary constraint, finding the global optimal solution based on the traditional differential evolution (DE) algorithms becomes so difficult due to the deception of many local optima and the probable existence of a bias towards suboptimal solution. In order to overcome this issue and enhance the global searching ability, an improved DE algorithm with combined mutation strategies and boundary-handling schemes is proposed. In the first stage, multiple mutation strategies are utilized, and each strategy creates a mutant vector. In the second stage, multiple boundary-handling schemes are used to simultaneously address the same infeasible trial vector. Two typical spacecraft multiple-impulse trajectory optimization problems are studied and optimized using the proposed DE method. The experimental results demonstrate that the proposed DE method efficiently overcomes the problem created by the convergence to a local optimum and obtains the global optimum with a higher reliability and convergence rate compared with some other popular evolutionary methods.

scholarly journals An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Wan-li Xiang ◽  
Xue-lei Meng ◽  
Mei-qing An ◽  
Yin-zhen Li ◽  
Ming-xia Gao
Keyword(s):  
Differential Evolution ◽  
Linear Time ◽  
Differential Evolution Algorithm ◽  
Premature Convergence ◽  
Perturbation Scheme ◽  
Solution Quality ◽  
Multiple Mutation ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Mutation Strategies

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

scholarly journals Optimization of Wave Energy Converter Arrays by an Improved Differential Evolution Algorithm

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3522 ◽  
Author(s):  
Hong-Wei Fang ◽  
Yu-Zhu Feng ◽  
Guo-Ping Li
Keyword(s):  
Differential Evolution ◽  
Incident Wave ◽  
Wave Energy ◽  
Differential Evolution Algorithm ◽  
Wave Energy Converter ◽  
Wave Number ◽  
Adaptive Mutation ◽  
Energy Converter ◽  
De Algorithm ◽  
Evolution Algorithm

Since different incident waves will cause the same array to perform differently with respect to the wave energy converter (WEC), the parameters of the incident wave, including the incident angle and the incident wave number, are taken into account for optimizing the wave energy converter array. Then, the differential evolution (DE) algorithm, which has the advantages of simple operation procedures and a strong global search ability, is used to optimize the wave energy converter array. However, the traditional differential evolution algorithm cannot satisfy the convergence precision and speed simultaneously. In order to make the optimization results more accurate, the concept of an adaptive mutation operator is presented to improve the performance of differential evolution algorithm. It gives the improved algorithm a faster convergence and a higher precision ability. The three-float, five-float, and eight-float arrays were optimized, respectively. It can be concluded that the larger the size of the array is, the greater the interaction between the floats is. Hence, a higher efficiency of wave energy extraction for wave energy converter arrays is achieved by the layout optimization of the array of wave energy converters. The results also show that the optimal layout of the array system is inhomogeneously distributed and that the improved DE algorithm used on array optimization is superior to the traditional DE algorithm.

scholarly journals Differential evolution algorithm of soft island model based on K-means clustering

2020 ◽  
Vol 19 (3) ◽  
pp. 1548
Author(s):  
Xujie Tan ◽  
Seong-Yoon Shin
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Island Model ◽  
Benchmark Problems ◽  
De Algorithm ◽  
Mutation Strategies ◽  
The Individual ◽  
And Control

<p>Differential evolution (DE) is a highly effective evolutionary algorithm. However, the performance of DE depends on strategies and control parameters. The combination of many strategies helps balance the exploitation and exploration of DE. In this study, a multi-population based on<em> k</em>-means clustering is proposed to realize an ensemble of multiple strategies, thereby resulting in a new DE variant, namely KSDE, where similar individuals in the population implement the same mutation strategies, and dissimilar subpopulations migrate information through the soft island model (SIM). Firstly, the population is virtually divided into <em>k</em> subpopulations by the <em>k</em>-means clustering algorithm. Secondly, the individual specific mutation scheme is selected from a strategy pool by random method. Finally, the migration of subpopulation information is done using soft island model. The performance of the KSDE algorithm is evaluated on 13 benchmark problems. The experiments show that KSDE algorithm improves the performance of the DE algorithm.<strong></strong></p>

Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

2019 ◽  
Vol 13 (1) ◽  
pp. 36-61 ◽  
Author(s):  
Kangshun Li ◽  
Zhuozhi Liang ◽  
Shuling Yang ◽  
Zhangxing Chen ◽  
Hui Wang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Dynamic Properties ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Fitness Distance Correlation ◽  
And Performance

Dynamic fitness landscape analyses contain different metrics to attempt to analyze optimization problems. In this article, some of dynamic fitness landscape metrics are selected to discuss differential evolution (DE) algorithm properties and performance. Based on traditional differential evolution algorithm, benchmark functions and dynamic fitness landscape measures such as fitness distance correlation for calculating the distance to the nearest global optimum, ruggedness based on entropy, dynamic severity for estimating dynamic properties, a fitness cloud for getting a visual rendering of evolvability and a gradient for analyzing micro changes of benchmark functions in differential evolution algorithm, the authors obtain useful results and try to apply effective data, figures and graphs to analyze the performance differential evolution algorithm and make conclusions. Those metrics have great value and more details as DE performance.

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