A unified matrix-based stochastic optimization algorithm

Abstract

Various metaheuristics have been proposed recently and each of them has its inherent evolutionary, physical-based, and/or swarm intelligent mechanisms. This paper does not focus on any subbranch, but on the metaheuristics research from a unified view. The population of decision vectors is looked on as an abstract matrix and three novel basic solution generation operations, E[p(i,j)], E[p(c·i)] and E[i, p(c·i+j)], are proposed in this paper. They are inspired by the elementary matrix transformations, all of which have none latent meanings. Experiments with real-coded genetic algorithm, particle swarm optimization and differential evolution illustrate its promising performance and potential.