title: Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, CA 90095, US.
authors.forename: Jiaheng
authors.initials: J
authors.lastname: Qiu
authors.affiliation: Department of Statistics, National Cheng-Kung University, Tainan 70101, Taiwan.
authors.forename: Ray-Bing
authors.initials: RB
authors.lastname: Chen
authors.affiliation: Department of Mathematics, National Taiwan University, Taipei, Taiwan.
authors.forename: Weichung
authors.initials: W
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, CA 90095, USA.
authors.forename: Weng Kee
authors.initials: WK
authors.lastname: Wong
keywords: Approximate design; D-optimal design; c-optimal design; efficiency; metaheuristic algorithms; particle swarm optimization