Performance Evaluation of Cultural Artificial Bee Colony and Cultural Artificial Fish Swarm Algorithm

Volume 6 Issue 4 December - February 2019

Research Paper

Performance Evaluation of Cultural Artificial Bee Colony and Cultural Artificial Fish Swarm Algorithm

Busayo Hadir Adebiyi*, Ahmed Tijani Salawudeen**, Risikat Folashade O. Adebiyi***

*-***Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

Adebiyi, B.H., Salawudeen, A.T., Adebiyi,R.F.(2019) Performance Evaluation of Cultural Artificial Bee Colony and Cultural Artificial Fish Swarm Algorithm,i-manager's Journal on Computer Science, 6(4),43-50. https://doi.org/10.26634/jcom.6.4.15725

Abstract

The introduction of Computational Intelligence (CI) algorithms in the area of optimizations have been given significant attention in science and engineering allied disciplines. This is because they always find answers to a problem while maintaining perfect stability among its components. However, these algorithms sometimes suffer from premature convergence and fitness stagnation, which usually originates from the lack of explorative search capability of its perturbation operator. This paper presents a comparative performance of a Cultural Artificial Bee Colony (called the CABCA) algorithm and Cultural Artificial Fish Swarms Algorithm (called the mCAFAC). In both algorithms (CABCA and mCAFAC), the normative and situational knowledge is employed to guide the direction and step size of the population (ABC and AFSA). Four variants of each ABC and AFSA were developed using different configurations of cultural knowledge in Matlab/Simulink environment. A collection of twelve optimization benchmark functions was used to test the performance, and it was found that the modified algorithms (CABCA and mCAFAC) outperformed their respective standard (ABC and AFSA) algorithms.