A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques
It provides an overview for researchers and practitioners interested in optimization methods, but it is incremental as it summarizes existing techniques rather than introducing new ones.
This chapter reviews physics-inspired metaheuristic optimization techniques, which are designed to solve challenging optimization problems by modeling non-linear physical phenomena, and highlights their effectiveness in providing near-optimal or optimal solutions for engineering tasks.
Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate meta-heuristics that have successfully provided near-optimal or optimal solutions to several engineering tasks. This chapter focuses on meta-heuristic algorithms modelled upon non-linear physical phenomena having a concrete optimization paradigm, having shown formidable exploration and exploitation abilities for such optimization problems. Specifically, this chapter focuses on several popular physics-based metaheuristics as well as describing the underlying unique physical processes associated with each algorithm.