Open AccessA New Hybrid Algorithm Based on ABC and PSO for Function Optimization
Author(s) -
Changfeng Chen,
Azlan Mohd Zain,
Li-Ping Mo,
Kailiang Zhou
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012065
Subject(s) - particle swarm optimization , mathematical optimization , algorithm , crossover , computer science , convergence (economics) , selection (genetic algorithm) , jump , trigonometric functions , local optimum , firefly algorithm , meta optimization , mathematics , artificial intelligence , physics , geometry , quantum mechanics , economics , economic growth
Artificial bee colony algorithm (ABC) and particle swarm optimization (PSO) are both famous optimization algorithms that have been successfully applied to various optimization problems, especially in function optimization. Those two algorithms have been attracting more and more research interest because of their efficiency and simplicity. However, PSO has poor exploration capabilities and thus is easy to fall into the local optimum; Likewise, ABC has low convergence speed. To address these shortcomings, firstly, we improved the ABC with the combination of greedy selection and crossover, secondly, a sine-cosine method will be used to help PSO jump into local optimal. Finally, a new hybrid algorithm based on improved ABC and PSO are proposed. Moreover, four functions are used to verify the effectiveness of the proposed algorithm, and the results show that, compared with other well-known algorithms, ABC-PSO is more efficient, faster and more robust in function optimization.