Fitness function evaluations: A fair stopping condition?
Fitness function evaluations: A fair stopping condition?
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DOI：
10.1109/sis.2014.7011793
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
A. Engelbrecht
中科院分区：
文献类型：
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作者：
A. Engelbrecht
在比较基于群体的优化算法时，无论候选解的初始数量如何，仅使用对适应度函数评估(FE)的数量的限制作为停止条件已成为可接受的做法。这种做法在许多比较基于种群的算法的性能的比赛中得到了倡导，并被许多包含算法经验比较的文章所使用。本文主张这种做法不会导致公平的比较，并提供了大量的经验证据来支持这一论断。将标准的全局最优粒子群优化(PSO)算法应用于一个大型基准测试套件，在相同的有限元计算极限下，得到了不同群大小的PSO算法的实验结果。
It has become acceptable practice to use only a limit on the number of fitness function evaluations (FEs) as a stopping condition when comparing population-based optimization algorithms, irrespective of the initial number of candidate solutions. This practice has been advocated in a number of competitions to compare the performance of population-based algorithms, and has been used in many articles that contain empirical comparisons of algorithms. This paper advocates the opinion that this practice does not result in fair comparisons, and provides an abundance of empirical evidence to support this claim. Empirical results are obtained from application of a standard global best particle swarm optimization (PSO) algorithm with different swarm sizes under the same FE computational limit, on a large benchmark suite.