A modification of I-SOS: performance analysis to large scale functions

Abstract

SOS is a global optimization algorithm, based on nature, and is utilized to execute the various complex hard optimization problems. Be that as it may, some basic highlights of SOS, for example, pitfall among neighborhood optima and weaker convergence zone should be upgraded to discover better answers for progressively intricate, nonlinear, many optimum solution type problems. To diminish these deficiencies, as of late, numerous analysts increase the exhibition of the SOS by designing up a few changed form of the SOS. This paper suggests an improved form of the SOS to build up an increasingly steady balance between discovery and activity cores. This technique uses three unique procedures called adjusted benefit factor, altered parasitism stage, and random weighted number-based search. The technique is referred to as mISOS and tested in a popular series of twenty classic benchmarks. The dimension of these problems is considered to be hundred to monitor the impact of the suggested technique on the versatility of the test problems. Also, some real-life optimization problems are solved with the help of the proposed mISOS. The results investigated based on three different way and theses are statistical measures, convergence, and statistical analyses. The comparison of results of the mISOS with the standard SOS, SOS variants, and certain other cutting-edge algorithms shows its improved search performance.