Diverse forms of opposition are already existent virtually everywhere around us, and utilizing
opposite numbers to accelerate an optimization method is a new idea. Since 2005 …

Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the
opposite relationship among entities. In 2005, for the first time the concept of opposition was …

The impact of the opposition concept can be observed in many areas around us. This
concept has sometimes been called by different names, such as, opposite particles in …

In algorithms design, one of the important aspects is to consider efficiency. Many algorithm
design paradigms are existed and used in order to enhance algorithms' efficiency …

Abstract Opposition-Based Learning (OBL) is a research area that has been widely applied
in several algorithms for improving the search process. In this work we present a revision of …

The concept of opposition-based learning (OBL) was first introduced as a scheme for
machine intelligence. In a very short period of time, some other variants of opposite numbers …

Opposition-based learning (OBL) is an effective strategy that adjusts the population to
accelerate the convergence of the algorithm. However, OBL involves two phases …

Opposition-based learning as a new scheme for machine intelligence is introduced.
Estimates and counter-estimates, weights and opposite weights, and actions versus counter …

Opposition-based Learning (OBL) has been reported with an increased performance in
enhancing various optimization approaches. Instead of investigating the opposite point of a …

The concept of opposition-based learning using current optimum is proposed and combined
with differential evolution for function optimization. The distance between the opposite points …