Evaluating the Mean-Variance Mapping Optimization on the IEEE-CEC 2014 test suite
I. Erlich, Jose Luis Rueda, Sebastian Wildenhues, Fekadu Shewarega · 2014
This paper provides a survey on the performance of the hybrid variant of the Mean-Variance Mapping Optimization (MVMO-SH) when applied for solving the IEEE-CEC 2014 competition test suite on Single Objective RealParameter Numerical Optimization. MVMO-SH adopts a swarm intelligence scheme, where each particle is characterized by its own solution archive and mapping function. Besides, multi-parent crossover is incorporated into the offspring creation stage in order to force the particles with worst fitness to explore other sub-regions of the search space. In addition, MVMO-SH can be customized to perform with an embedded local search strategy. Experimental results demonstrate the search ability of MVMO-SH for effectively tackling a variety of problems with different dimensions and mathematical properties.