Using animal instincts to design efficient biomedical studies via particle swarm optimization

作者：Qiu Jiaheng; Chen Ray Bing; Wang Weichung; Wong Weng Kee^*

来源：Swarm and Evolutionary Computation, 2014, 18: 1-10.

DOI：10.1016/j.swevo.2014.06.003

• Approximate design
• c-optimal design
• D-optimal design
• Efficiency
• Metaheuristic algorithms
• Particle swarm optimization

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.

• 出版日期2014-10