Multidimensional Red Fox meta-heuristic for complex optimization

Abstract

Modern applications of computer science need efficient algorithms to solve complex optimization tasks. This necessity is especially well visible in multidimensional problems, where with growing number of optimization variables applied algorithms often loose precision or computing becomes very long. In this paper we present an improved Multidimensional Red Fox Optimization algorithm (MRFO). The initial RFO idea was ameliorated with new approach to local search, with even faster motion of the population toward regions of optimum. Secondly, in the reproduction phase additional mathematical operations addressing the problem of individuals crossing assumed optimization domain were formulated. Compared to RFO, the computational complexity of MRFO grows significantly slower with increasing dimensionality of an optimization task. Numerical results on the well-known COCO BBOB benchmark show that proposed modifications have merit and lead to higher efficacy of MRFO compared to the baseline model. The results are also competitive to DE-best — an efficient Differential Evolution implementation.