An intuitive distance-based explanation of opposition-based sampling

► Opposition-based learning (OBL) has been utilized in different soft computing areas to enhance evolutionary algorithms (DE and PSO), ANNs. ► A proof that shows intuitively why considering the opposite of a candidate solution is more beneficial than another random solution. ► The proposed intuitive view is generalized to N-dimensional search spaces for black-box problems. ► We provide examples of symmetric and non-symmetric evaluation functions to give an idea of expected behavior. ► We now have in general that pR = 0.25, pX = 0.375 and pOX = 0.375.