A novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of solutions in the search space
Tome Eftimov, Peter Korošec · Information Sciences · 2019
In this paper a novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of the solutions in the search space is introduced, known as extended Deep Statistical Comparison. This approach is an extension of the recently proposed Deep Statistical Comparison approach used for comparing meta-heuristic stochastic optimization algorithms according to the solutions values. Its main contribution is that the algorithms are compared not only according to obtained solutions values, but also according to the distribution of the obtained solutions in the search space. The information it provides can additionally help to identify exploitation and exploration powers of the compared algorithms. This is important when dealing with a multimodal search space, where there are a lot of local optima with similar values. The benchmark results show that our proposed approach gives promising results and can be used for a statistical comparison of meta-heuristic stochastic optimization algorithms according to solutions values and their distribution in the search space.