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Nature-inspired metaheuristic algorithms are important components ofartificial intelligence, and are increasingly used across disciplines to tacklevarious types of challenging optimization problems. This paper demonstrates theusefulness of such algorithms for solving a variety of challenging optimizationproblems in statistics using a nature-inspired metaheuristic algorithm calledcompetitive swarm optimizer with mutated agents (CSO-MA). This algorithm wasproposed by one of the authors and its superior performance relative to many ofits competitors had been demonstrated in earlier work and again in this paper.The main goal of this paper is to show a typical nature-inspired metaheuristicalgorithmi, like CSO-MA, is efficient for tackling many different types ofoptimization problems in statistics. Our applications are new and includefinding maximum likelihood estimates of parameters in a single cell generalizedtrend model to study pseudotime in bioinformatics, estimating parameters in thecommonly used Rasch model in education research, finding M-estimates for a Coxregression in a Markov renewal model, performing matrix completion tasks toimpute missing data for a two compartment model, and selecting variablesoptimally in an ecology problem in China. To further demonstrate theflexibility of metaheuristics, we also find an optimal design for a carrefueling experiment in the auto industry using a logistic model with multipleinteracting factors. In addition, we show that metaheuristics can sometimesoutperform optimization algorithms commonly used in statistics.

Applications of Nature-Inspired Metaheuristic Algorithms for Tackling Optimization Problems Across Disciplines