Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

Abstract

The artificial bee colony (ABC) algorithm is a competitive stochastic population-based optimization algorithm. However, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to insufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estimation of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six benchmark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimental results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.