Multi-resource equilibrium optimization of scientific research projects based on pigeon colony algorithm

Balanced use of multiple resources has a significant impact on the quality of scientific research projects. Resources can be balanced by rationally arranging the implementation time of each project task. Traditional optimization problem solutions such as ‘fixed project cycle and resource balance’ include intelligent optimization algorithms such as genetic algorithms and particle swarm optimization. However, this paper innovatively applies the pigeon population algorithm to balancing and optimizing the use of multiple resources in scientific research projects. Firstly, the three aspects of cost, time difference and work importance are considered in order to establish a comprehensive evaluation system based on resource importance. A multi-resource equilibrium optimization mathematical model is then proposed in order to minimize the resource use variance. Finally, an example is tested to verify the effectiveness of the algorithm. Experiments show that the pigeon colony algorithm can effectively solve the optimal solution of multi-resource equilibrium optimization in scientific research. The work plan arrangement provided is more balanced than the initial solution and the suboptimal result chosen by project man-agers. Therefore, the pigeon colony algorithm has wide application prospects for scientific research projects and will have wide application prospects.