Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP

Sine Cosine Algorithm (SCA) has been proved to be superior to some existing traditional optimization algorithms owing to its unique optimization principle. However, there are still disadvantages such as low solution accuracy and poor global search ability. Aiming at the shortcomings of the sine cosi...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-10
Main Authors: Chen, Chien-Ming, Pan, Jeng-Shyang, Chu, Shu-Chuan, Yang, Qingyong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Evolution & development
Exploitation
Genetic algorithms
Intelligence
Optimization
Optimization algorithms
Population
Populations
Route planning
Scholars
Trigonometric functions
Vehicle routing
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:Sine Cosine Algorithm (SCA) has been proved to be superior to some existing traditional optimization algorithms owing to its unique optimization principle. However, there are still disadvantages such as low solution accuracy and poor global search ability. Aiming at the shortcomings of the sine cosine algorithm, a multigroup multistrategy SCA algorithm (MMSCA) is proposed in this paper. The algorithm executes multiple populations in parallel, and each population executes a different optimization strategy. Information is exchanged among populations through intergenerational communication. Using 19 different types of test functions, the optimization performance of the algorithm is tested. Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms. At last, it is applied to solving the capacitated vehicle routing problem (CVRP) in transportation. The algorithm can get better results, and the practicability and feasibility of the algorithm are also proved.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/8184254