scholarly journals Some Aspects of the Influence of Population Diversity on the Performance of Differential Evolution

2021 ◽  
Author(s):  
Joao Claudio Chamma Carvalho ◽  
Kalef Levy de Lima Pinto ◽  
Roberto Celio Limão Oliveira
Keyword(s):  
Differential Evolution ◽  
Population Diversity ◽  
Optimization Process ◽  
Performance Results

This paper presents a study about some aspects of the influence of population diversity on the performance of the Differential Evolution technique. In order to accomplish this, the referred algorithm is tested with different benchmark functions widely used in the literature, and the performance results are analyzed and discussed by associating changes in the population diversity with changes in the evolution of the best solution over the generations. The objective of this work is to investigate the pattern of diversity behavior throughout the optimization process through graphs results and, then, evaluate how sensitive is the technique performance when associated with the population diversity behavior. This work can assist the implementation of new operators and strategies, which will permit the Differential Evolution technique to have a better performance.

A population diversity-controlled differential evolution for parameter estimation of solar photovoltaic models

2022 ◽  
Vol 51 ◽  
pp. 101938
Author(s):  
Yang Yu ◽  
Kaiyu Wang ◽  
Tengfei Zhang ◽  
Yirui Wang ◽  
Chen Peng ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Population Diversity ◽  
Solar Photovoltaic

scholarly journals Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1565 ◽  
Author(s):  
Xingping Sun ◽  
Linsheng Jiang ◽  
Yong Shen ◽  
Hongwei Kang ◽  
Qingyi Chen
Keyword(s):  
Differential Evolution ◽  
Clustering Analysis ◽  
Numerical Optimization ◽  
Optimization Algorithms ◽  
Search Space ◽  
Population Diversity ◽  
Convergence Of Algorithms ◽  
Adaptive Differential Evolution ◽  
Complex Methods ◽  
Single Objective

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.

On the Population Diversity for the Chaotic Differential Evolution

2018 ◽  
Author(s):  
Roman Senkerik ◽  
Adam Viktorin ◽  
Michal Pluhacek ◽  
Tomas Kadavy
Keyword(s):  
Differential Evolution ◽  
Population Diversity

scholarly journals Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Jianzhong Huang ◽  
Yuwan Cen ◽  
Nenggang Xie ◽  
Xiaohua Ye
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Measuring Point ◽  
Mapping Model ◽  
Gravitational Search ◽  
Set Up

For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural network. Due to the contradiction between population diversity and convergence rate in the optimization of complex neural networks by using differential evolution, a gravitational search algorithm and differential evolution is proposed to accelerate the convergence rate of differential evolution population driven by gravity. Gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population. Compared with the standard differential evolution, the convergence speed and accuracy of gravitational search algorithm and differential evolution are significantly improved, which has better optimization stability. The calculation results show that the output accuracy of this gravitational and differential evolution neural network can meet the calculation requirements of the positioning control of demolition robot’s manipulator. The optimization using gravitational search algorithm and differential evolution is done with the connection weights of a neural network in this article, and as similar techniques can be applied to the other hyperparameter optimization problem. Moreover, such an inverse calculation method can provide a reference for the autonomous positioning of large hydraulic series manipulator, so as to improve the robotization level of construction machinery.

Global and Local Surrogate-Model-Assisted Differential Evolution for Waterflooding Production Optimization

SPE Journal ◽  
2019 ◽  
Vol 25 (01) ◽  
pp. 105-118 ◽  
Author(s):  
Guodong Chen ◽  
Kai Zhang ◽  
Liming Zhang ◽  
Xiaoming Xue ◽  
Dezhuang Ji ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Surrogate Model ◽  
Net Present Value ◽  
Channel Model ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Production Optimization ◽  
Optimization Process ◽  
Promising Area ◽  
Global And Local

Summary Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm called global and local surrogate-model-assisted differential evolution (GLSADE) is introduced for waterflooding production-optimization problems. The proposed method consists of two parts: (1) a global surrogate-model-assisted differential-evolution (DE) part, in which DE is used to generate multiple offspring, and (2) a local surrogate-model-assisted DE part, in which DE is used to search for the optimum of the surrogate. The cooperation between global optimization and local search helps the production-optimization process become more efficient and more effective. Compared with the conventional one-shot surrogate-based approach, the developed method iteratively selects data points to enhance the accuracy of the promising area of the surrogate model, which can substantially improve the optimization process. To the best of our knowledge, the proposed method uses a state-of-the-art surrogate framework for production-optimization problems. The approach is tested on two 100-dimensional benchmark functions, a three-channel model, and the egg model. The results show that the proposed method can achieve higher net present value (NPV) and better convergence speed in comparison with the traditional evolutionary algorithm and other surrogate-assisted optimization methods for production-optimization problems.

Drive optimization of a pulsatile total artificial heart

2014 ◽  
Vol 33 (3) ◽  
pp. 941-952
Author(s):  
André Pohlmann ◽  
Kay Hameyer
Keyword(s):  
Cardiovascular Diseases ◽  
Differential Evolution ◽  
Cost Function ◽  
Human Body ◽  
Differential Evolution Algorithm ◽  
Optimization Methods ◽  
Parameter Variation ◽  
Optimization Process ◽  
Total Artificial Heart ◽  
Content Type

Purpose – Total artificial hearts (TAHs) are required for the treatment of cardiovascular diseases. In order to replace the native heart a TAH must provide a sufficient perfusion of the human body, prevent blood damage and meet the implantation constraints. Until today there is no TAH on the market which meets all constraints. So the purpose of this paper is to design a drive in such a way that the operated TAH meets all predefined constraints. Design/methodology/approach – The drive is designed in terms of weight and electric losses. In setting up a cost function containing those constraints, the drive design can be included in a optimization process. When reaching the global minimum of the cost function the optimum drive design is found. In this paper the optimization methods manual parameter variation and differential evolution are applied. Findings – At the end of the optimization process the drive's weight amounts to 460 g and its mean losses sum up to 10 W. This design meets all predefined constraints. Further it is proposed to start the optimization process with a parameter variation to reduce the amount of optimization parameters for the time consuming differential evolution algorithm. Practical implications – This TAH has the potential to provide a therapy for all patients suffering from cardiovascular diseases as it is independent of donor organs. Originality/value – The optimization-based design process yields an optimum drive for a TAH in terms of weight and electrical losses. In this way a TAH is developed which meets all implantation constraints and provides sufficient perfusion of the human body at the same time.

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