Estimation multivariate normal algorithm with threshold convergence

Estimation of Distribution Algorithms (EDAs) use a subset of solutions from the current population to build a distribution function from which the next generation of solutions is created. If there is poor diversity in the current population, then there is poor diversity in the subset of solutions selected from it and in the next generation that is created from it. Like many metaheuristics, EDAs can suffer from an autocatalytic process in which convergence begets more convergence. In Thresheld Convergence, convergence is "held" back by a threshold function, and this new technique has been successfully applied to other metaheuristics to prevent autocatalytic convergence from cascading into premature convergence. In this paper, Thresheld Convergence is applied to Estimation Multivariate Normal Algorithm with a key difference: convergence is controlled in the parameter space instead of the search space. Computational results show that significant improvements can be achieved across a broad range of multimodal functions.