A two-stage memory powered Great Deluge algorithm for global optimization

Abstract

A two-stage memory architecture and search operators exploiting the accumulated experience in memory are maintained within the framework of a Great DeLuge algorithm for real-valued global optimization. The level-based acceptance criterion of the Great DeLuge algorithm is applied for each best solution extracted in a particular iteration. The use of memory-based search supported by effective move operators results in a powerful optimization algorithm. The success of the presented approach is illustrated using three sets of well-known benchmark functions including problems of varying sizes and difficulties. Performance of the presented approach is evaluated and in comparison to well-known algorithms and their published results. Except for a few large-scale optimization problems, experimental evaluations demonstrated that the presented approach performs at least as good as its competitors.