ABSTRACT
Originally developed from the fundamentals of statistical physics, extremal optimization (EO) is based on the extremal dynamics of self-organized criticality, which describes a class of systems that have a critical point as an attractor. Their macroscopic behavior exhibits the spatial and temporal scale-invariance characteristics of the critical point of a phase transition. In fact, there are two motivations behind the proposed Multi-stage EO algorithm (MSEO) method. One is the effect of the control parameter t on the search dynamics of t-EO. Specifically, when t is small, the search is similar to a random walk. Conversely, it approaches to a deterministic local search, only updating those worst variables for very large values of t. The key idea behind MSEO is to perform optimization by using different values of the parameters in different search stages. Generally speaking, optimization starts with randomly collecting some local optima by multistart techniques.
