Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm

Abstract

Aiming at the shortcomings of standard particle swarm optimization (PSO) algorithms that easily fall into local optimum, this paper proposes an optimization algorithm (LTQPSO) that improves quantum behavioral particle swarms. Aiming at the problem of premature convergence of the particle swarm algorithm, the evolution speed of individual particles and the population dispersion are used to dynamically adjust the inertia weights to make them adaptive and controllable, thereby avoiding premature convergence. At the same time, the natural selection method is introduced into the traditional position update formula to maintain the diversity of the population, strengthen the global search ability of the LTQPSO algorithm, and accelerate the convergence speed of the algorithm. The improved LTQPSO algorithm is applied to landscape trail path planning, and the research results prove the effectiveness and feasibility of the algorithm.

Online Incipient Fault Detection Method Based on Improved ℓ1 Trend Filtering and Support Vector Data Description

“…These methods can achieve IFD by constructing bearing performance degradation models. Zan's method can accurately predict the performance degradation trend and the remaining service life of rolling bearings for small samples; However, the conventional particle swarm algorithm that easily falls into local optimum [16]. Rai and Zhou's method is able to timely diagnose the incipient bearings failure, and Mao's method performs well on bearing online IFD.…”