<p>Genetic algorithm, simulated annealing, and particle swarm optimization are well-regarded metaheuristics due to performing remarkably for solving diverse optimization problems. In this article, some genetic algorithm, simulated annealing, and particle swarm optimization oriented biasing parameter selection methods are proposed for a new two-parameter biased estimator defined for the Almon distributed lag model suffering from multicollinearity. Additional novel hybrid biasing parameter selection methods in which the genetic algorithm, simulated annealing, and particle swarm optimization are consolidated with conventional parameter selection are also introduced to determine biasing parameters of the proposed estimator. To display how the new metaheuristic optimization oriented biasing parameter selection methods perform in practice, relationship between air pollution and mortality in the United States is modeled by the Almon distributed lag model. More comprehensive results are accomplished with a Monte Carlo simulation experiment designed for the Almon distributed lag model and proposed metaheuristics. Empirical analyses provide outcomes promoting the proposed metaheuristic optimization oriented biasing parameter selection methods.</p>