Selecting Decision Variables for Artificial Bee Colony using a Self-adaptive Variable Matrix
Artificial Bee Colony (ABC) is a Swarm Intelligence optimization algorithm well-know for its versatility. The selection of decision variables to up-date is purely stochastic, incurring in several issues to the local search capability of the ABC. To address these issues, a self-adaptive decision variable selec-tion mechanism is proposed with the goal of balancing the degree of explorationand exploitation throughout the execution of the algorithm. This selection,named Adaptive Decision Variable Matrix (ADVM) represents both stochasticand deterministic parameter selection in a binary matrix and regulates the ex-tent of how much each selection is employed based on the estimation of thesparsity of the solutions in the search space. Influence of the proposed approachto performance and robustness of the original algorithm is validated by experi-menting on fifteen highly multimodal benchmark optimization problems. ADVMis integrated into the original ABC and variations in order to showcase the flex-ibility of the method. Numerical comparison is made against the ABC and theirvariants, as well as to other population-based algorithms (e.g., Particle SwarmOptimization and Differential Evolution). Results show an improvement of theperformance of the algorithms with the ADVM in the most difficult instances.
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