A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algori...

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Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2021; no. 1
Main Authors: Wang, Zhaocai, Bao, Xiaoguang, Wu, Tunhua
Format: Journal Article
Language:English
Published: New York Hindawi 2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Analysis
Chains
Combinatorial analysis
Complexity
Computer applications
Deoxyribonucleic acid
DNA
Efficiency
Experiments
Feasibility
Integer programming
Molecular biology
Optimization
Polynomials
Post offices
Resource allocation
Resource scheduling
Solution space
Storage capacity
Traveling salesman problem
China
ISSN:1687-5265, 1687-5273
Online Access:Get full text
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Summary:The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems.
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ISSN:1687-5265
1687-5273
DOI:10.1155/2021/8814947