A Fast and efficient stochastic opposition-based learning for differential evolution in numerical optimization

A fast and efficient stochastic opposition-based learning (OBL) variant is proposed in this paper. OBL a machine concept to accelerate the convergence of soft computing algorithms, which consists simultaneously calculating an original solution its opposite. Recently, called BetaCOBL was proposed, capable controlling degree opposite solutions, preserving useful information held by preventing waste fitness evaluations. While it has shown outstanding performance compared several state-of-the-art variants, high computational cost may hinder from cost-sensitive optimization problems. Also, as assumes that decision variables given problem are independent, be ineffective for optimizing inseparable In paper, we propose improved mitigates all limitations. The algorithm iBetaCOBL reduces O(NP2 · D) O(NP (NP D stand population size dimension, respectively) using linear time diversity measure. preserves strongly dependent adjacent each other multiple exponential crossover. We used differential evolution (DE) variants evaluate algorithm. results evaluations on set 58 test functions show excellent ten including BetaCOBL.