Partial opposition-based learning using current best candidate solution

Abstract

Opposition based learning (OBL) has been gaining significant attention in machine learning, specially, in metaheuristic optimization algorithms to take OBL's advantage for enhancing their performance. In OBL, all variables are changed to their opposites while some variables are currently holding proper values which are discarded and converted to worse values by performing opposite. The partial opposition scheme was developed to change randomly some variables to its opposites but they do not pay enough attention to identify and keep variables which have proper values. In this paper, we propose a novel partial opposition scheme, which is generated based on the current best candidate solution. It tries to generate new trial solutions by using the candidate solutions and their opposites such that some variables of a candidate solution are remain unchanged and other variables are changed to their opposites in the trial solution (i.e., gene/variable based optimization). Variables in the trial solution are identified as close or far, according to their Euclidean distance from the corresponding variables/genes in the current best candidate solution. The proposed scheme uses the opposite of variables, which are closer to the current best solution. Only the new trial solutions are included in the next generation which are closer to corresponding opposite solution. As a case study, we employ the proposed partial opposition scheme in the DE algorithm and the partial opposition-based DE is evaluated on CEC-2014 benchmark functions. Simulation results confirm that the partial opposition-based DE obtains a promising performance on the majority of the benchmark functions. The proposed algorithm is compared with the Opposition-based DE (ODE) and random partial opposition-based DE algorithm (DE-RPO); the results show that our new method is better than or at least comparable to other competitors.