Solving the IEEE-CEC 2014 expensive optimization test problems by using single-particle MVMO

Mean-Variance Mapping Optimization (MVMO) constitutes an emerging heuristic optimization algorithm, whose evolutionary mechanism adopts a single parent-offspring pair approach along with a normalized range of the search space for all optimization variables. Besides, MVMO is characterized by an archive of n-best solutions from which the unique mapping function defined by the mean and variance of the optimization variables is derived. The algorithm proceeds by projecting randomly selected variables onto the corresponding mapping function that guides the solution towards the best set achieved so far. Despite the orientation on the best solution the algorithm keeps on searching globally. This paper provides an evaluation of the performance of MVMO when applied for the solution of computationally expensive optimization problems. Experimental tests, conducted on the IEEE-CEC 2014 optimization test bed, highlight the capability of the MVMO to successfully tackle different complex problems within a reduced number of allowed function evaluations.