Adaptive search space for stochastic opposition-based learning in differential evolution

Differential evolution (DE) is a practical evolutionary algorithm (EA) widely employed for addressing continuous optimization problems. Opposition-based learning (OBL) emerges as a potent method among the techniques enhancing EA performance. The BetaCOBL variant represents a pinnacle in this domain. However, BetaCOBL's utilization of the promising regions of the search space remains partial, owing to its dependence on a non-adaptive framework. Consequently, its efficacy might dwindle as optimization progresses. We aimed to introduce an enhanced version of BetaCOBL, termed adaptive BetaCOBL (ABetaCOBL). ABetaCOBL commences by adapting the search space based on population distribution and subsequently identifying opposite solutions. We evaluated the efficacy of embedding ABetaCOBL into DE algorithms through experiments. Our experimental results substantiate that ABetaCOBL outperforms its precursor and resilient OBL variants (e.g., ABetaCOBL outperforms iBetaCOBL-eig in 19 out of 58 problems with NL-SHADE-LBC and in 22 out of 58 problems with NL-SHADE-RSP).