Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. We apply a newly proposed nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA) and demonstrate its flexibility and out-performance relative to its competitors in a variety of optimization problems in the statistical sciences. In particular, we show the algorithm is efficient and can incorporate various cost structures or multiple user-specified nonlinear constraints. Our applications include (i) finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, (ii) estimating parameters in a commonly used Rasch model in education research, (iii) finding M-estimates for a Cox regression in a Markov renewal model and (iv) matrix completion to impute missing values in a two compartment model. In addition we discuss applications to (v) select variables optimally in an ecology problem and (vi) design a car refueling experiment for the auto industry using a logistic model with multiple interacting factors.

翻译：受自然启发的元启发式算法是人工智能的重要组成部分，并日益被跨学科用于解决各类具有挑战性的优化问题。我们应用一种新提出的受自然启发的元启发式算法——带变异智能体的竞争群优化器（CSO-MA），并展示了其在统计科学中多种优化问题中相对于同类算法的灵活性和优越性能。具体而言，我们证明了该算法的高效性，且能够纳入各种成本结构或满足多个用户指定的非线性约束。我们的应用包括：（i）在单细胞广义趋势模型中寻找参数的最大似然估计，以研究生物信息学中的伪时间；（ii）估计教育研究中常用的Rasch模型参数；（iii）在马尔可夫更新模型中找到Cox回归的M估计；（iv）通过矩阵补全对两室模型中的缺失值进行插补。此外，我们还讨论了其在以下方面的应用：（v）在生态学问题中优化变量选择，（vi）利用包含多个交互因子的逻辑模型为汽车行业设计加油实验。