Chapter 6 - A comprehensive survey: Physics-based algorithms

Abstract

To solve computationally challenging optimization problems, metaheuristic algorithms can be used. Metaheuristic algorithms are inspired by natural processes and are used to solve complex optimization problems that cannot be solved with traditional optimization algorithms. They provide approximate solutions that are usually close to the optimal solution. The development of metaheuristics has been inspired by many natural and physical processes which, combined, have provided near-optimal or optimal solutions to several engineering problems. Specifically, this chapter discusses metaheuristic algorithms based on nonlinear physical phenomena with a concrete optimization paradigm, which have demonstrated remarkable exploration and exploitation capabilities for such problems. These metaheuristics have the ability to find the best solution from within a high-dimensional search space and can even find solutions to problems that are too complex for analytical methods. Additionally, they can efficiently explore a wide variety of possible solutions with minimal computational resources, making them ideal for engineering problems. In particular, this chapter describes a number of popular physics-based metaheuristics as well as the physical processes that underlie each of these algorithms.