Investigating the application of opposition concept to colonial competitive algorithm
Evolutionary algorithms (EAs) are well-known optimisation approaches to deal with non-linear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate colonial competitive algorithm (CCA). The proposed opposition-based CCA (OCCA) employs opposition-based learning (OBL) for population initialisation and also for generation jumping. In this work, opposite countries and colonies have been utilised to improve the convergence rate of CCA. A comprehensive set of 15 complex benchmark functions including a wide range of dimensions is employed for experimental verification. The influences of dimensionality and population size are also investigated. Experimental results confirm that the OCCA outperforms the original CCA in terms of convergence speed and robust.
colonial competitive algorithm obl cca opposition-based learning opposite countries opposite colonies