Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
A line up evolutionary algorithm for solving nonlinear constrained optimization problems
Computers and Operations Research
Opposition versus randomness in soft computing techniques
Applied Soft Computing
Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Imperialist competitive algorithm for minimum bit error rate beamforming
International Journal of Bio-Inspired Computation
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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.