Author(s)

Guoqing Wu ¹, Wenbo Li ², Rong Jiang ¹

Affiliation(s)

¹ Physical Science and Technology College, Yichun University, Yichun, Jiangxi, 336000, China
² Mathematics and Computer Science College, Yichun University, Yichun, Jiangxi, 336000, China

Corresponding Author

Wenbo Li

ABSTRACT

Particle Swarm Optimization (PSO) algorithm has the characteristics of simple principle, few parameters, high parallelism efficiency and easy implementation, it is widely used in various fields, and the PSO algorithm is an algorithm based on swarm intelligence search, which can be parallelized. Particle swarm optimization algorithm is an evolutionary computing technology based on swarm intelligence method, and it is a new branch in the field of evolutionary computing. Exploration of multiple feasible solutions that meet the conditions is more suitable for solving multi-objective optimization problems. Because the algorithm is easy to combine with other algorithms, it has broad application prospects in many complex combinatorial optimization fields. The purpose of this paper is to use the organic combination of the improved hybrid particle swarm multi-objective optimization algorithm and engineering project management to effectively solve the optimization problem of the entire project supply chain management, discuss and predict the possible problems and countermeasures in the theoretical research and application fields, and then propose improvement plans. Hybrid particle swarm multi-objective optimization algorithm. At the same time, an improved hybrid particle swarm multi-objective optimization algorithm is used to make material selection decisions in project management to achieve the goal of maximizing project benefits. The hybrid particle swarm multi-objective optimization algorithm has outstanding advantages in solving many difficult and complex combinatorial optimization problems. This paper first expounds the theory and important parameters of the hybrid particle swarm multi-objective optimization algorithm, and then analyzes its improvement and application in parameter optimization and intelligent fusion. Finally, it summarizes its application in engineering applications, such as: job scheduling problem, vehicle routing problem, image processing, power system optimization and other aspects of the application progress. Therefore, it can be concluded that the application of the improved optimization algorithm can avoid unfavorable factors in production in project management, seek to maximize benefits, and make production more convenient and efficient. Therefore, the research on hybrid particle swarm multi-objective optimization algorithm is used in engineering project management. It is of great significance. Intelligent decision system plays an important role in project management.

KEYWORDS

Hybrid particle swarm multi-objective optimization algorithm, engineering project management, functional optimization, artificial intelligence, early termination of tasks, market algorithm, Job Scheduling Problem

CITE THIS PAPER

Guoqing Wu, Wenbo Li, Rong Jiang, Application of Hybrid Particle Swarm Multi-objective Optimization Algorithm in Engineering Project Management. Industrial Engineering and Innovation Management (2024) Vol. 7: 17-27. DOI: http://dx.doi.org/10.23977/ieim.2024.070403.

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