An adaptive differential evolution with combined strategy for global numerical optimization

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for numerical optimization. However, the performance of DE significantly relies on its mutation operator and control parameters (scaling factor and crossover rate). In this paper, we propose a novel DE variant by introducing...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2020-05, Vol.24 (9), p.6277-6296
Main Authors: Sun, Gaoji, Yang, Bai, Yang, Zuqiao, Xu, Geni
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Crossovers
Engineering
Evolutionary algorithms
Evolutionary computation
Focus
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Mutation
Operators (mathematics)
Optimization
Periodic functions
Robotics
Scaling factors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Differential evolution (DE) is a simple yet powerful evolutionary algorithm for numerical optimization. However, the performance of DE significantly relies on its mutation operator and control parameters (scaling factor and crossover rate). In this paper, we propose a novel DE variant by introducing a series of combined strategies into DE, called CSDE. Specifically, in CSDE, to obtain a proper balance between global exploration ability and local exploitation ability, we adopt two mutation operators with different characteristics to produce the mutant vector, and provide a mechanism based on their own historical success rate to coordinate the two adopted mutation operators. Moreover, we combine a periodic function based on one modulo operation, an individual-independence macro-control function and an individual-dependence function based on individual’s fitness value information to adaptively produce scaling factor and crossover rate. To verify the effectiveness of the proposed CSDE, comparison experiments contained seven other state-of-the-art DE variants are tested on a suite of 30 benchmark functions and four real-world problems. The simulation results demonstrate that CSDE achieves the best overall performance among the eight DE variants.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-03934-3