Backcalculation of Pavement Moduli Using Bio-Inspired Hybrid Metaheuristics and Cooperative Strategies

Biologically inspired computing or natural computing is a field of research that takes inspiration from nature, biology, physical systems, and social behavior of natural systems for developing computational techniques to solve complex optimization problems. For instance, one of the most well-established nature-inspired heuristic techniques is the genetic algorithm (GA), which is based on the survival-of-thefittest notion espoused by Darwin’s theory of evolution. Similarly, the ant colony optimization (ACO) approach imitates the real-world foraging behavior shown by ants when they search for food, and particle swarm optimization (PSO) is inspired by social behavior of bird flocking or fish schooling. In recent years, such nature-inspired metaheuristics are emerging as successful alternatives to more classical approaches for solving optimization problems that contain uncertainty, stochasticity, and dynamic information in their mathematical formulation.