ABC-TDQL: An adaptive memetic algorithm

This paper provides a novel approach to design an adaptive memetic algorithm by utilizing the composite benefits of Artificial Bee Colony for global search and Q-learning for local refinement. Four variants of Differential Evolution including the currently best Self-adaptive Differential Evolution algorithm have been used here to study the relative performance of the proposed adaptive memetic algorithm with respect to runtime, cost function evaluation and accuracy (offset in cost function from the theoretical optimum after termination of the algorithm). Computer simulations undertaken on a well-known set of 25 benchmark functions reveals that incorporation of Q-learning in Artificial Bee Colony makes the corresponding algorithm more efficient in both runtime and accuracy. The performance of the proposed adaptive memetic algorithm has also been studied on a multi-robot path-planning problem.

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