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.

中文翻译：

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基于改进粒子群算法的智能城市景观设计

针对标准粒子群算法（PSO）容易陷入局部最优的缺点，提出了一种改进量子行为粒子群的优化算法（LTQPSO）。针对粒子群算法过早收敛的问题，利用单个粒子的演化速度和种群分散来动态调整惯性权重，使其具有自适应性和可控性，从而避免了过早收敛。同时，将自然选择方法引入传统的位置更新公式中，以保持种群的多样性，增强LTQPSO算法的全局搜索能力，并加快算法的收敛速度。改进的LTQPSO算法应用于景观步道规划，