Micro-differential evolution with local search for high dimensional problems

Reduced population algorithms have proven to be efficient for solving optimization problems in the past. In this paper, we incorporate a local search procedure into a micro differential evolution algorithm (DE) with the aim of tackling high dimensional problems. Our main purpose is to find out if our proposal is more competitive in these problems than a canonical differential evolution algorithm. In relation to the state of the art techniques, the results our micro-DELS are comparable (or better) with the reference algorithms DECC-G and MLCC. This empirical analysis supports our conjecture that a reduced population DE hybridized with local search (our microDELS) is a key combination in dealing with functions having high dimensionality at a low computational cost.