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

Busayo Hadir Adebiyi*, Gail**, Risikat Folashade O. Adebiyi***

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

Periodicity:December - February'2019
DOI : 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.

Keywords

ABC, CABCA, AFSA, mCAFAC, Test Functions, Optimization

How to Cite this Article?

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

References

[1]. Abachizadeh, M., Yazdi, M. R. H., & Yousefi-Koma, A. (2010). Optimal tuning of PID controllers using Artificial Bee Colony algorithm. In Advanced Intelligent Mechatronics (AIM), 2010 IEEE/ASME International Conference on (pp. 379-384). IEEE.

[2]. Adebiyi, B. H. (2017). Development of Cultural Algorithm Based Artificial Bee Colony for Improved Proportional Integral Derivative Parameter Tuning (M.Sc Dissertation. Ahmadu Bello University Zaria).

[3]. Adebiyi, B. H., Tekanyi, A. M. S., & Salawudeen, T. A. (2017). An Improved Artificial Bee Colony using Cultural Algorithm for optimization problem. International Journal of Computer Applications, 160(8). 14-18.

[4]. Akay, B., & Karaboga, D. (2012). Artificial Bee Colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23(4), 1001-1014.

[5]. Alobaidi, A. T. S., & Hussein, S. A. (2017). An improved Artificial Fish Swarm Algorithm to solve flexible job shop. In New Trends in Information & Communications Technology Applications (NTICT), 2017 Annual Conference on (pp. 7-12). IEEE.

[6]. Baba, Y., Ugweje, O. C., & Koyunlu, G. (2017). Development and analysis of a modified Artificial Fish Swarm Algorithm. In Electronics, Computer and Computation (ICECCO), 2017 13^th International Conference on (pp. 1-6). IEEE.

[7]. Binitha, S., & Sathya, S. S. (2012). A survey of bio inspired optimization algorithms. International Journal of Soft Computing and Engineering, 2(2), 137-151.

[8]. Chen, L., & Zhao, X. (2016). An improved power control AFSA for minimum interference to primary users in cognitive radio networks. Wireless Personal Communications, 87(1), 293-311.

[9]. Cheng, Z., & Hong, X. (2012). PID controller parameters optimization based on artificial fish swarm algorithm. In Intelligent Computation Technology and Automation (ICICTA), 2012 Fifth International Conference on (pp. 265-268). IEEE.

[10]. Dorigo, M., & Socha, K. (2006). An introduction to Ant Colony Optimization. Handbook of Approximation Algorithms and Metaheuristics, 26-1.

[11]. Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.

[12]. El-Telbany, M. E. (2013). Tuning PID controller for DC motor: An artificial bees optimization approach. International Journal of Computer Applications, 77(15).

[13]. Kaliappan, V., & Thathan, M. (2015). Enhanced ABC based PID controller for nonlinear control systems. Indian Journal of Science and Technology, 8, 48-56.

[14]. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06 (pp. 1-10). Erciyes University, Engineering Faculty, Department of Computer Engineering.

[15]. Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), 108-132.

[16]. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471.

[17]. Karaboga, D., & Basturk, B. (2008). On the performance of Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697.

[18]. Li, X. L. (2002). An optimizing method based on autonomous animats: Fish-swarm algorithm. Systems Engineering-Theory & Practice, 22(11), 32-38.

[19]. Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. N. (2014). Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), 965-997.

[20]. Reynolds, R. G. (1994). An introduction to cultural algorithms. In Proceedings of the Third Annual Conference on Evolutionary Programming (pp. 131-139). River Edge, NJ: World Scientific.

[21]. Reynolds, R. G., & Peng, B. (2004). Cultural algorithms: Modeling of how cultures learn to solve problems. In Tools with Artificial Intelligence, 2004. ICTAI 2004. 16^th IEEE International Conference on (pp. 166- 172). IEEE.

[22]. Reynolds, R. G., & Peng, B. (2005). Cultural algorithms: computational modeling of how cultures learn to solve problems: An engineering example. Cybernetics and Systems: An International Journal, 36(8), 753-771.

[23]. Salawudeen, A. T. (2015). Development of an Improved Cultural Artificial Fish Swarm Algorithm with Crossover. Department of Electrical and Computer Engineering, Ahmadu Bello University, Zaria, Nigeria,154.

[24]. Salawudeen, A. T., Abdulrahman, A. O., Sadiq, B. O., & Mukhtar, Z. A. (2016). An optimized Wireless Sensor Network Deployment using weighted Artificial Fish Swarm (wAFSA) Optimization Algorithm. International Conference on Information and Communication Technology and its Applications, 203-207.

[25]. Shapla, S. S., Haque, H. M., & Alam, M. S. (2015). Explorative Artificial Bee Colony algorithm: A novel Swarm Intelligence based Algorithm for continuous function optimization. International Journal of Science and Research (IJSR), 4(7), 1339-1344.

[26]. Tijani, S. A., & Mua'zu, M. B. (2015). Stabilization of inverted pendulum system using intelligent Linear Quadratic Regulator controller. In Computational Intelligence (IJCCI), 2015 7^th International Joint Conference on (Vol. 1, pp. 325-333). IEEE.

[27]. Varma, P., & Kumar, B. A. (2013). Control of DC motor using Artificial Bee Colony based PID controller. Int. J. Digital Appl. Contemp. Res., 2, 1-9.

[28]. Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In International Symposium on Stochastic Algorithms (pp. 169-178). Springer, Berlin, Heidelberg.

[29]. Yazdani, D., Sepas-Moghaddam, A., Dehban, A., & Horta, N. (2016). A novel approach for optimization in dynamic environments based on modified artificial fish swarm algorithm. International Journal of Computational Intelligence and Applications, 15(02), 1650010.

[30]. Yu, L., & Li, C. (2014). A global artificial fish swarm algorithm for structural damage detection. Advances in Structural Engineering, 17(3), 331-346.

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

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