Abstract
Shuffled Frog-Leaping Algorithm (SFLA) is a memetic meta-heuristic used for solving various combinatorial optimization problems. SFLA divides population into several memeplexes and then apply evolutionary process to update every memeplex. Like other evolutionary algorithm, it may also suffer from the problem of slow convergence. To improve the convergence and exploitation capability of SFLA, Binomial crossover is embedded. The proposed algorithm is named as, Binomial Crossover Embedded Shuffled Frog Leaping Algorithm (BC-SFLA). In the proposed BC-SFLA, the perturbation rate of the solutions are controlled by the binomial crossover. The proposed algorithm is tested over 15 benchmark test functions and compared with basic SFLA and two other nature inspired algorithms namely gravitational search algorithm (GSA) and differential evolution algorithm (DE). The results state that BC-SFLA is a competitive variant of SFLA.
Published Version
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