Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

粒子群优化 (PSO) 是一种越来越流行的元启发式算法，用于解决复杂的优化问题。它的流行是由于它在为许多应用学科中的问题找到最佳或接近最佳的解决方案方面一再成功。该算法不假设要优化的函数，对于像这里介绍的生物医学实验，PSO 通常在几秒钟的 CPU 时间内在普通笔记本电脑上找到最佳解决方案。我们应用 PSO 来为生物科学中的几个问题寻找各种类型的优化设计，并比较 PSO 相对于差分进化算法的性能，差分进化算法是工程文献中另一种流行的元启发式算法。