ABC_DE_FP: A Novel Hybrid Algorithm for Complex Continuous Optimization Problems

Abstract

Artificial bee colony (ABC) algorithm evolved as one of the efficient swarm intelligence-based algorithm in solving various global optimisation problems. Though numerous variants of ABC are available, algorithm depicts poor convergence rate in many situations. Therefore, maintaining balance between intensification and diversification of an algorithm still needs attention. In this context, a novel hybrid ABC algorithm (ABC_DE_FP) has been developed by integrating FPA and DE in original ABC algorithm. To assess the efficacy of proposed hybrid algorithm, it is primarily compared with contemporary ABC variants such as GABC, IABC and AABC over simple benchmark problems. Thereafter, it is evaluated with respect to original ABC, FPA, hybrid ABC_FP, ABC_DE and ABC_SN over CEC2014 optimisation problems for up to 100 dimensions. Results reveal that proposed algorithm considerably outperforms its counterparts in terms of minimum error value attained and convergence speed for majority of global numerical optimisation functions.