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 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.

Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

2019 ◽  
Vol 10 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Ali Khater Mohamed ◽  
Ehab Z. Elfeky ◽  
Mohamed Saleh
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Test Problems ◽  
New Mutation ◽  
De Algorithm ◽  
Engineering Problems ◽  
Evolution Algorithm ◽  
Mutation Scheme ◽  
Exploration Exploitation

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC'2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.

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.

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

scholarly journals Solving Multiobjective Game in Multiconflict Situation Based on Adaptive Differential Evolution Algorithm with Simulated Annealing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huimin Li ◽  
Shuwen Xiang ◽  
Wensheng Jia ◽  
Yanlong Yang ◽  
Shiguo Huang
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Payoff Function ◽  
Game Model ◽  
Entropy Weight ◽  
Strategy Space ◽  
De Algorithm ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

In this paper, we study the multiobjective game in a multiconflict situation. First, the feasible strategy set and synthetic strategy space are constructed in the multiconflict situation. Meanwhile, the value of payoff function under multiobjective is determined, and an integrated multiobjective game model is established in a multiconflict situation. Second, the multiobjective game model is transformed into the single-objective game model by the Entropy Weight Method. Then, in order to solve this multiobjective game, an adaptive differential evolution algorithm based on simulated annealing (ADESA) is proposed to solve this game, which is to improve the mutation factor and crossover operator of the differential evolution (DE) algorithm adaptively, and the Metropolis rule with probability mutation ability of the simulated annealing (SA) algorithm is used. Finally, the practicability and effectiveness of the algorithm are illustrated by a military example.

scholarly journals An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations

2020 ◽  
Vol 10 (1) ◽  
pp. 118-136
Author(s):  
Jeerayut Wetweerapong ◽  
Pikul Puphasuk
Keyword(s):  
Differential Evolution ◽  
Nonlinear Equations ◽  
Nonlinear Models ◽  
Differential Evolution Algorithm ◽  
Systems Of Nonlinear Equations ◽  
Test Problems ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

In this research, an improved differential evolution algorithm with a restart technique (DE-R) is designed for solutions of systems of nonlinear equations which often occurs in solving complex computational problems involving variables of nonlinear models. DE-R adds a new strategy for mutation operation and a restart technique to prevent premature convergence and stagnation during the evolutionary search to the basic DE algorithm. The proposed method is evaluated on various real world and synthetic problems and compared with the recently developed methods in the literature. Experiment results show that DE-R can successfully solve all the test problems with fast convergence speed and give high quality solutions. It also outperforms the compared methods.

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