Civil Engineering Design in IoT: Leveraging Improved Swarm Intelligence Optimization

Nowadays, swarm intelligence algorithms are used to solve various problem in IoT environments because of their excellent performance, and the particle swarm algorithm(PSO) is a superior algorithm in swarm intelligence algorithms. The original PSO, however, exhibits certain limitations. For instance, the utilization of random initialization results in a less diverse population distribution for PSO. During the algorithm iteration process, excessively large or small weight factors can also detrimentally impact the performance of the algorithm. Moreover, when dealing with multi-modal problems, the PSO algorithm can quickly converge to local optimal because of its "precocious". In this paper, a Double-Population Particle Swarm Optimization(DP-PSO) algorithm is proposed. Firstly, the chaotic mechanism is used to initialize the population to make its distribution more uniform and obtain better convergence results. Secondly, the oscillation characteristic of the sine function is used to improve the updating method of weight by adding the sine factor, and the search area of the particle is expanded. Finally, the double-population variation mechanism is introduced to enhance the variation ability and improve the solving accuracy in the later iteration. The experimental results show that the DP-PSO is superior to other algorithms in performance and has obvious improvement in the ability to expand the search space of the population and jump out of the local optimal solution. When applied to cantilever beam design, DP-PSO also has good competitiveness in terms of stability, robustness, and scalability.