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

TL;DR

This paper showcases the effectiveness of the nature-inspired metaheuristic algorithm CSO-MA in solving diverse complex optimization problems across various scientific disciplines, demonstrating its versatility and superior performance.

Contribution

The paper applies the CSO-MA algorithm to multiple new statistical optimization problems, illustrating its broad applicability and efficiency in different contexts.

Findings

CSO-MA outperforms traditional optimization methods in several statistical tasks.

The algorithm effectively solves problems in bioinformatics, education, ecology, and industry.

Metaheuristics can sometimes surpass conventional algorithms in statistical optimization.

Abstract

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its 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 metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.