Edinburgh Napier University

  The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (de) algorithms to adaptively manage the balance between the diversification and intensification phases, depending on current progress. The method—Similarity-based Neighbourhood Search (sns)—uses information derived from measuring Euclidean distances among solutions in the decision space to adaptively influence the choice of neighbours to be used in creating a new solution. sns is integrated into explorative and exploitative variants of jade, one of the most frequently used adaptive de approaches. Furthermore, shade, which is another state-of-the-art adaptive de variant, is also considered to assess the performance of the novel sns. A thorough experimental evaluation is conducted using a well-known set of large-scale continuous problems, revealing that incorporating sns allows the performance of both explorative and exploitative variants of de to be significantly improved for a wide range of the test-cases considered. The method is also shown to outperform variants of de that are hybridised with a recently proposed global search procedure, designed to speed up the convergence of that algorithm.