Abstract
Shuffled Frog-Leaping Algorithm (SFLA) is a memetic meta-heuristic approach for solving complex optimization problems. Like other evolutionary algorithms, it may also suffer from the problem of slow convergence. To elevate the convergence and to improve the intensification and diversification capabilities of SFLA, elitism is embedded by calculating the mean of local best and second local best solutions while updating the position of worst solution in local best updating phase. Similarly, mean of global best and second global best solutions is used to improve the position of worst solution while updating the position of worst solution in global best updating phase. The proposed algorithm is named as Elitism based Shuffled Frog-Leaping Algorithm (ESFLA). The modified algorithm ESFLA is analysed over 15 distinct benchmark test problems and compared with conventional SFLA, its recent variant, namely Binomial Crossover Embedded Shuffled Frog-Leaping Algorithm (BC-SFLA) and two other nature inspired algorithms, namely Gravitational Search Algorithm (GSA) and Biogeography-Based Optimization Algorithm (BBO). The results manifest that ESFLA is an antagonist variant of SFLA.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call