Differential Evolution with Self-Adaptation

Many practical engineering applications can be formulated as a global optimization problem, in which objective function has many local minima, and derivatives of the objective function are unavailable. Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces (Storn & Price, 1997) (Liu & Lampinen, 2005) (Price, Storn & Lampinen, 2005) (Feoktistov, 2006). Nowadays it is used as a powerful global optimization method within a wide range of research areas. Recent researches indicate that self-adaptive DE algorithms are considerably better than the original DE algorithm. The necessity of changing control parameters during the optimization process is also confirmed based on the experiments in (Brest, Greiner, Boškovic, Mernik, Žumer, 2006a). DE with self-adaptive control parameters has already been presented in (Brest et al., 2006a). This chapter presents self-adaptive approaches that were recently proposed for control parameters in DE algorithm.

Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211014413 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110144

Author(s):

Qianqian Zhang ◽

Daqing Wang ◽

Lifu Gao

Keyword(s):

Differential Evolution ◽

Objective Function ◽

Inverse Kinematics ◽

Weight Coefficient ◽

Adaptive Mutation ◽

Feasible Region ◽

Serial Manipulators ◽

De Algorithm ◽

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.

Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism

Mathematical Problems in Engineering ◽

10.1155/2013/419372 ◽

2013 ◽

Vol 2013 ◽

pp. 1-14 ◽

Cited By ~ 10

Author(s):

Xu Wang ◽

Shuguang Zhao

Keyword(s):

Differential Evolution ◽

Control Parameters ◽

De Algorithm ◽

Wide Range ◽

Evolution Algorithm ◽

Mutation Strategies ◽

Nonparametric Statistical ◽

Nonparametric Statistical Procedures ◽

A differential evolution (DE) algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.

Differential Evolution: A Survey and Analysis

Applied Sciences ◽

10.3390/app8101945 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1945 ◽

Cited By ~ 13

Author(s):

Tarik Eltaeib ◽

Ausif Mahmood

Keyword(s):

Global Optimization ◽

Differential Evolution ◽

Optimization Problems ◽

State Of The Art ◽

Population Based ◽

Vital Role ◽

Fine Tuning ◽

Control Parameters ◽

Hybrid Techniques ◽

Global Optimizer

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization

International Journal of Digital Content Technology and its Applications ◽

10.4156/jdcta.vol6.issue8.40 ◽

2012 ◽

Vol 6 (8) ◽

pp. 343-350

Author(s):

Liyuan Jia ◽

Li Huang ◽

Lei Li

Keyword(s):

Global Optimization ◽

Adaptive Control ◽

Differential Evolution ◽

Control Parameter ◽

SACPDE: Self-Adaptive Control Parameters in Differential Evolution Algorithm for Notch Filter Design in UWB Antenna Applications

2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) ◽

10.1109/latincom.2018.8613199 ◽

2018 ◽

Author(s):

Danilo F. Poveda-Pulla ◽

Josue B. Benavides-Aucapina ◽

Rafael A. Lituma-Guartan ◽

Luis F. Guerrero-Vasquez ◽

Paul A. Chasi - Pesantez

Keyword(s):

Adaptive Control ◽

Differential Evolution ◽

Filter Design ◽

Notch Filter ◽

Control Parameters ◽

Uwb Antenna ◽

Evolution Algorithm ◽

Optimization Of Raw Material Inventory Costs In The Food Supply Chain Using Differential Evolution Algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/20187313016 ◽

2018 ◽

Vol 73 ◽

pp. 13016

Author(s):

Mara Huriga Priymasiwi ◽

Mustafid

Keyword(s):

Supply Chain ◽

Differential Evolution ◽

Food Industry ◽

Optimization Method ◽

Raw Material ◽

Inventory Cost ◽

De Algorithm ◽

Material Requirement ◽

Total Inventory

The management of raw material inventory is used to overcome the problems occuring especially in the food industry to achieve effectiveness, timeliness, and high service levels which are contrary to the problem of effectiveness and cost efficiency. The inventory control system is built to achieve the optimization of raw material inventory cost in the supply chain in food industry. This research represents Differential Evolution (DE) algorithm as optimization method by minimizing total inventory based on amount of raw material requirement, purchasing cost, saefty stock and reorder time. With the population size, the parameters of mutation control, crossover parameters and the number of iterations respectively 80, 0.8, 0.5, 200. With the amount of safety stock at the company 7213.95 obtained a total inventory cost decrease of 39.95%. Result indicate that the use of DE algorithm help providein efficient amount, time and cost.