ABSTRACT
This chapter presents principles of the EMO algorithm, variants of EMO algorithms, and their applications to various domains. The EMO method imitates the attraction–repulsion mechanism of the electromagnetism (EM) theory in order to solve unconstrained or bound-constrained global optimization problems. According to electromagnetic theory, each particle has a charge in an electromagnetic field and there is an electromagnetic force acting between two particles, which follow Coulomb's law. Guan proposed a revised EMO algorithm for flow path design of an automated guided vehicle (AGV) system where a variable neighborhood search strategy is employed as the local search technique. In order to apply the EMO algorithm to a discrete domain such as discrete permutation flow shop scheduling, Liu and Gao redefined the calculation of the charge of particles, forces acting on them, and their movement in discrete space. Cuevas proposed an opposition-based EMO (OBEMO) applying opposition-based numbers to optimization algorithms for better exploration of the search space and enhancing the convergence speed.
