Search Optimization Opportunities of Modified Self-Organizing Migrating Algorithm in Multi-Extremal Tasks Environment

Abstract

The paper studies the search optimization task of multi-extremal objects, which are more complicated than mono-extremal. Paper postulates that to find extreme suitable values on complex test function the heuristic algorithm is one way. Self-Organizing Migrating Algorithm and devised approach applied to this task are considered. Conducted research established common test environment to compare multi-extremal test functions. Specific characteristics for problem solving of detection and identification of global and local extreme are included. Additional clustering mechanism are described. Obtained measurements and computing times of Self-Organizing Migrating Algorithm on a range of multi-extremal test functions are illustrated. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.