scholarly journals Differential Evolution with Linear Bias Reduction in Parameter Adaptation

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 283
Author(s):  
Vladimir Stanovov ◽  
Shakhnaz Akhmedova ◽  
Eugene Semenkin
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Bias Reduction ◽  
Benchmark Problems ◽  
Basic Algorithm ◽  
Optimization Stage ◽  
Speed Up ◽  
Comparison Of The Results ◽  
Lehmer Mean

In this study, a new parameter control scheme is proposed for the differential evolution algorithm. The developed linear bias reduction scheme controls the Lehmer mean parameter value depending on the optimization stage, allowing the algorithm to improve the exploration properties at the beginning of the search and speed up the exploitation at the end of the search. As a basic algorithm, the L-SHADE approach is considered, as well as its modifications, namely the jSO and DISH algorithms. The experiments are performed on the CEC 2017 and 2020 bound-constrained benchmark problems, and the performed statistical comparison of the results demonstrates that the linear bias reduction allows significant improvement of the differential evolution performance for various types of optimization problems.

A Cultivated Variant of Differential Evolution Algorithm for Global Optimization

2016 ◽  
pp. 1-19
Author(s):  
Pooja
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Benchmark Problems ◽  
De Algorithm ◽  
Molecular Potential ◽  
Acceleration Rate ◽  
Base Vector ◽  
Molecular Potential Energy

Differential Evolution (DE) algorithm is known as robust, effective and highly efficient for solving the global optimization problems. In this chapter, a modified variant of Differential Evolution (DE) is proposed, named Cultivated Differential Evolution (CuDE) which is different from basic DE in two ways: 1) the selection of the base vector for mutation operation, 2) population generation for the next generation. The performance of the proposed algorithm is validated on a set of eight benchmark problems taken from literature and a real time molecular potential energy problem. The numerical results show that the proposed approach helps in formulating a better trade-off between convergence rate and efficiency. Also, it can be seen that the performance of DE is improved in terms of number of function evaluations, acceleration rate and mean error.

scholarly journals An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

2020 ◽  
Vol 45 (2) ◽  
pp. 97-124
Author(s):  
Pikul Puphasuk ◽  
Jeerayut Wetweerapong
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Systems Of Nonlinear Equations ◽  
Wide Range ◽  
Evolution Algorithm

AbstractDesigning an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.

Differential Evolution Applied to the Design of a Three-Dimensional Vehicular Structure

2007 ◽  
Author(s):  
Felipe Antonio Chegury Viana ◽  
Fernando Ce´sar Gama de Oliveira ◽  
Jose Antonio Ferreria Borges ◽  
Valder Steffen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Differential Evolution Algorithm ◽  
Basic Algorithm ◽  
Present Contribution ◽  
Finite Element Software ◽  
Design Variables ◽  
Numerical Noise ◽  
Discrete Design Variables

The purpose of this paper is to demonstrate the application of Differential Evolution to a realistic design optimization test problem. The present contribution regards the improvements implemented to the original basic algorithm as well as the application of a new algorithm for dealing with the unique challenges associated with real world optimization problems. The selected example is a three-dimensional vehicular structure optimization problem modeled using the commercial Finite Element software ANSYS® that has a combination of continuous and discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the Differential Evolution algorithm is able to find the optimum design for the proposed problem. The algorithm is robust in the sense that it is capable of dealing with the numerical noise involved in the modeling of the system and to manipulate discrete design variables, accordingly.

scholarly journals Differential Evolution with Population and Strategy Parameter Adaptation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
V. Gonuguntla ◽  
R. Mallipeddi ◽  
Kalyana C. Veluvolu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Search Space ◽  
Benchmark Problems ◽  
Parameter Adaptation ◽  
De Algorithm ◽  
Probabilistic Technique ◽  
Number Of Individuals ◽  
Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

scholarly journals Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-36 ◽  
Author(s):  
Wei Li ◽  
Lei Wang ◽  
Quanzhu Yao ◽  
Qiaoyong Jiang ◽  
Lei Yu ◽  
...  
Keyword(s):  
Solid State ◽  
Differential Evolution ◽  
Liquid State ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Gaseous State ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Cloud Particles

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

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