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 o...
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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 managers. Therefore, the pigeon colony algorithm has wide application prospects for scientific research projects and will have wide application prospects.
In this paper, a new pigeon colony algorithm (PCA) based on the features of a pigeoncolony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process,...
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In this paper, a new pigeon colony algorithm (PCA) based on the features of a pigeoncolony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution;the flying process is designed to search for the local and global optimum and improve the global worst solution;and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.
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