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.

应用竞争性带突变主体的自然启发元启发式算法（CSO-MA），在统计科学的各种优化问题中展示其灵活性和相对于竞争对手的出色表现。