Polarized Laser Backscattering of Atmospheric Cloud Distribution Based on Simulated Annealing Algorithm

Ren, Shenhe; Gao, Ming; Wang, Mingjun; Li, Yan

doi:10.1155/2021/6635828

Cited by 1 publication

(1 citation statement)

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“…Enterprise resource integration and scheduling are NP-complete problems [ 6 , 7 ]. Particle swarm algorithms [ 8 – 10 ], ant colony algorithms [ 11 – 13 ], simulated annealing algorithms [ 14 – 16 ], and genetic algorithms [ 17 – 19 ] are currently available that can solve NP-complete problems. The PSO algorithm has the advantages of fast convergence speed, easy operation, and the algorithm is convenient for global optimization, but it also has disadvantages such as easy to fall into local optimum and low search accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Method for Enterprise Resource Integration Based on Improved Particle Swarm Optimization

Guo¹,

Zhu²,

Chang³

2022

Computational Intelligence and Neuroscience

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An enterprise’s development and growth are inextricably linked to rational and efficient resource integration and optimization. This study focuses on the reorganization and integration of industrial elements inside the firm from the standpoint of resource integration. The ideal resource integration strategy is investigated by integrating the industrial parts of a certain enterprise in order to increase the efficiency of project completion and lower enterprise expenses. The enterprise’s internal material and human resources are limited, but it is frequently necessary to execute numerous activities at the same time, and each activity must meet multiple goals. This research investigates how to properly integrate and schedule resources while attaining different goals. This research proposes using an enhanced particle swarm optimization technique (IPSO) to combine firms’ internal resources. In order to address the issue of uneven particle dispersion caused by random population initialization, IPSO incorporates chaos theory into particle population initialization. The logistic mapping sequence generates a huge number of particles, and the particles with the highest quality are chosen for initialization. This can increase particle quality, allowing particles to be spread equally during setup. In the late stage, the classic particle swarm optimization algorithm (PSO) has a slow convergence rate, causing the algorithm to readily slip into a local optimal solution. This research proposes a dynamic inertia weight update approach based on fitness value. In the later stages of the algorithm, this strategy can improve the convergence speed and quality of the global optimal solution, allowing the particles to do a global search and eventually identify the population’s ideal solution. Furthermore, IPSO creates a fitness function depending on task completion time. IPSO is used to test the performance of an enterprise’s resource integration case. Experiments show that the method utilized can swiftly locate the ideal solution, complete the integration, and optimization of enterprise resources in the shortest job completion time, and for the least amount of money.

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