Product Search Algorithm Based on Improved Ant Colony Optimization in a Distributed Network

Authors

Zhishuo Liu, Beijing Jiaotong University
Fang Tian, Pepperdine University
Lida Li, Beijing Jiaotong University
Zhuonan Han, Beijing Jiaotong University
Yuqing Li, Beijing Jiaotong University

Department(s)

Business Administration

Document Type

Article

Version Deposited

Published version

Publication Date

8-1-2022

Keywords

ant colony optimization, commodity information search, crowd intelligence, distributed network

Abstract

The crowd intelligence-based e-commerce transaction network (CIeTN) is a distributed and unstructured network structure. Smart individuals, such as buyers, sellers, and third-party organizations, can store information in local nodes and connect and share information via moments. The purpose of this study is to design a product search algorithm on the basis of ant colony optimization (ACO) to achieve an efficient and accurate search for the product demand of a node in the network. We introduce the improved ideas of maximum and minimum ants to design a set of heuristic search algorithms on the basis of ACO. To reduce search blindness, additional relevant heuristic factors are selected to define the heuristic calculation equation. The pheromone update mechanism integrating into the product matching factor and forwarding probability is used to design the network search rules among nodes in the search algorithm. Finally, the search algorithm is facilitated by Java language programming and PeerSim software. Experimental results show that the algorithm has significant advantages over the flooding method and the random walk method in terms of search success rate, search time, product matching, search network consumption, and scalability. The search algorithm introduces the idea of improving the maximum and minimum ant colony system and proposes new ideas in the design of heuristic factors in the heuristic equation and the pheromone update strategy. The search algorithm can search for product information effectively.

Publication Title

International Journal of Crowd Science

E-ISSN

23987294

Volume

6

Issue

3

First Page

128

Last Page

134

DOI

10.26599/IJCS.2022.9100016

Comments

Publication can be accessed at this link: https://doi.org/10.26599/IJCS.2022.9100016

Recommended Citation

Z. Liu, F. Tian, L. Li, Z. Han and Y. Li, "Product Search Algorithm Based on Improved Ant Colony Optimization in a Distributed Network," in International Journal of Crowd Science, vol. 6, no. 3, pp. 128-134, August 2022, doi: 10.26599/IJCS.2022.9100016.