Performance up-gradation of Symbiotic Organisms Search by Backtracking Search Algorithm

Abstract

Symbiotic Organisms Search (SOS) algorithm is characterized based on the framework of relationships among the ecosystem species. Nevertheless, it is suffering from wasteful discovery, little productivity, and slack convergence rate. These deficiencies cause stagnation at the local optimum, which is hazardous in deciding the genuine optima of the optimization problem. Backtracking Search Algorithm (BSA) is likewise another streamlining method for comprehending the non-direct complex optimization problem. Consequently, in the current paper, an endeavor has been made toward the expulsion of the downsides from the traditional SOS by proposing a novel ensemble technique called e-SOSBSA to overhaul the degree of intensification and diversification. In e-SOSBSA, firstly, the mutation operator of BSA with the self-adaptive mutation rate is incorporated to produce a mutant of population and leap out from the local optima. Secondly, the crossover operator of BSA with the adaptive component of mixrate is incorporated to leverage the entire active search regions visited previously.The suggested e-SOSBSA has been tested with 20 classical benchmark functions, IEEE CEC2014, CEC2015, CEC2017, and the latest CEC 2020 test functions. Statistical analyses, convergence analysis, and diversity analysis are performed to show the stronger search capabilities of the proposed e-SOSBSA in contrast with the component algorithms and several state-of-the-art algorithms. Moreover, the proposed e-SOSBSA is applied to find the optimum value of the seven problems of engineering optimization. The numerical investigations and examinations show that the proposed e-SOSBSA can be profoundly viable in tackling real-world engineering optimization problems.

A Bio-Inspired Multi-Population-Based Adaptive Backtracking Search Algorithm

“…In this section, the proposed ImBSA is compared with some recently proposed variants of BSA, namely GWO-BSA [ 45 ], e-SOSBSA [ 46 ], BS-DE [ 47 ], LFBSA [ 11 ], IBS [ 10 ], and hDEBSA [ 48 ]. For this comparison, the CEC 2017 test functions with ten dimensions have been considered.…”

“…The proposed ImBSA is employed to determine the speed reducer design problem [ 46 ]. Various components of this problem are the number of teeth on pinion , the module of teeth , face width , the diameter of shaft 1 ( ), and diameter of shaft 2 ( ), length of shaft 1 between bearings ( ), and length of shaft 2 between bearings ( ) as shown seen in Fig.…”

“…The proposed ImBSA has been employed to solve the cantilever beam design with the same configuration as the reference [ 46 ]. The optimal output attained is shown in Table 22 , and results other than ImBSA are derived from [ 46 ].…”

“…The proposed ImBSA has been employed to solve the cantilever beam design with the same configuration as the reference [ 46 ]. The optimal output attained is shown in Table 22 , and results other than ImBSA are derived from [ 46 ]. In comparison with m-SCA, SCA, CS, GCA(I), GCA(II), MMA, CONLIN, and e-SOSBS, the optimal performance of the suggested ImBSA is better.…”

“…Cauchy-GWO, ARSM, improved ARSM, CS, GWO, and e-SOSBSA were solved I-beam design engineering optimization problems previously. The suggested ImBSA research is also employed to solve this significant optimization issue with the same parameter configuration used in reference [ 46 ]. The best outcome achieved by ImBSA is presented in Table 23 , and the results other than ImBSA are taken from [ 46 ].…”