CCEO: cultural cognitive evolution optimization algorithm

Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary p...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 23; no. 23; pp. 12561 - 12583
Main Authors: Zhou, Yongquan, Zhang, Shaoling, Luo, Qifang, Abdel-Baset, Mohamed
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019
Springer Nature B.V
Subjects:
Animal cognition
Artificial Intelligence
Behavior
Computational Intelligence
Control
Engineering
Evolutionary algorithms
Genetic algorithms
Knowledge
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Optimization
Optimization algorithms
Problem solving
Robotics
Stone Age
Benchmark functions
Cognitive behavior
Cultural cognitive evolution algorithm
Cultural evolution
Engineering optimization
ISSN:1432-7643, 1433-7479
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-03806-w