FA-ABC: A Novel Combination of Firefly Optimization Algorithm and Artificial Bee Colony for Mathematical Test Functions and Real-World Problems

Subject Areas : optimization and simulation

Ali reza  Shafiee sarvestany ¹ (Department of Mechanical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran)
Mohammadjavad  Mahmoodabadi ² (Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran)

Received: 2021-09-09 Accepted : 2022-01-05 Published : 2022-06-01

Keywords: Artificial bee colony algorithm, Firefly Algorithm, CGAM problem, Hybrid optimization algorithm,

Abstract :

In this research study, an attempt is made to present a new optimization scheme by combination of the firefly algorithm and artificial bee colony (FA-ABC) to solve mathematical test functions and real-world problems as best as possible. In this regard, the main operators of the two meta-heuristic algorithms are employed and combined to utilize both advantages. The results are compared with those of five prominent well-known approaches on sixteen benchmark functions. Moreover, thermodynamic, economic and environmental modeling of a thermal power plant known as the CGAM problem is represented. The proposed FA-ABC algorithm is used to reduce the total cost and increase the efficiency of the system as shown in the Pareto front diagrams.

References:

• Mahmoodabadi, M. J., Rasekh, M., and Zohari, T., TGA: Team Game Algorithm, Future Computing and Informatics Journal, Vol. 3, No. 2, 2018, pp. 191-199.


• Piryonesi, S. M., Tavakolan, M., A Mathematical Programming Model for Solving Cost-Safety Optimization (CSO) Problems in The Maintenance of Structures, KSCE Journal of Civil Engineering, Vol. 21, No. 6, 2017, pp. 2226-2234.


• Piryonesi, S. M., Nasseri, M., and Ramezani, A., Resource Leveling in Construction Projects with Activity Splitting and Resource Constraints: A Simulated Annealing Optimization, Canadian Journal of Civil Engineering, Vol. 46, No. 2, 2019, pp. 81-86.


• Mahmoodabadi, M. J., Ostadzadeh, R., CTLBO: Converged Teaching–Learning–Based Optimization, Cogent Engineering, Vol. 6, No. 1, 2019, pp. 1654207.


• Holland, J. H., Genetic Algorithms, Scientific American, Vol. 267, No. 1, 1992, pp. 66-73.


• Eberhart, R., Kennedy, J., A New Optimizer Using Particle Swarm Theory, In MHS'95, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43.


• Dorigo, M., Maniezzo, V., and Colorni, A., Ant System: Optimization by A Colony of Cooperating Agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 26, No. 1, 1996, pp. 29-41.


• Mirjalili, S., Mirjalili, S. M., and Lewis, A., Grey Wolf Optimizer, Advances in Engineering Software, Vol. 69, 2014, pp. 46-61.


• Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., and Chen, H., Harris hawks optimization: Algorithm and applications. Future generation computer systems, Vol. 97, 2019, pp.849-872.


• Li, X., Qian, J., Studies on Artificial Fish Swarm Optimization Algorithm Based on Decomposition and Coordination Techniques, Journal of Circuits and Systems, Vol. 1, 2003, pp. 1-6.


• Chu, S. C., Tsai, P. W., and Pan, J. S., Cat Warm Optimization, In Pacific Rim International Conference on Artificial Intelligence, 2006, pp. 854-858.


• Atashpaz-Gargari, E., Lucas, C., Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition, in 2007 IEEE Congress On Evolutionary Computation, 2007, pp. 4661-4667.


• Tayarani-N, M. H., Akbarzadeh-T, M., Magnetic Optimization Algorithms a New Synthesis, In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008, pp. 2659-2664.


• Karaboga, D., Akay, B., A comparative Study of Artificial Bee Colony Algorithm, Applied Mathematics and Computation, Vol. 214, No. 1, 2009, pp. 108-132.


• -S. Yang, Firefly algorithm, Levy flights and global optimization, in Research and development in intelligent systems XXVI. 2010, Springer. p. 209-218.


• Yang, X. S., Gandomi, A. H., Bat Algorithm: A Novel Approach for Global Engineering Optimization, Engineering Computations, 2012.


• Rajakumar, B., The Lion's Algorithm: A New Nature-Inspired Search Algorithm, Procedia Technology, Vol. 6, 2012, pp. 126-135.


• Mirjalili, S., Lewis, A., The Whale Optimization Algorithm, Advances in Engineering Software, Vol. 95, 2016, pp. 51-67.


• Mirjalili, S., Dragonfly Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-Objective, Discrete, And Multi-Objective Problems, Neural Computing and Applications, Vol. 27, No. 4, 2016, pp. 1053-1073.


• Beniwal, S., Bacterial Foraging Optimization, 2018.


• Dong, N., Wu, C. H., Ip, W. H., Chen, Z. Q., Chan, C. Y., and Yung, K. L., An Opposition-Based Chaotic GA/PSO Hybrid Algorithm and Its Application in Circle Detection. Computers & Mathematics with Applications, Vol. 64, No. 6, pp.1886-1902.


• Mahmoodabadi, M. J., Mottaghi, Z. S., and Bagheri, A., HEPSO: High Exploration Particle Swarm Optimization, Information Sciences, Vol. 273, 2014, pp. 101-111.


• Yu, S., Wei, Y. M., and Wang, K., A PSO–GA Optimal Model to Estimate Primary Energy Demand of China, Energy Policy, Vol. 42, 2012, pp. 329–340.


• Kiran, M. S., Özceylan, E., Gündüz, M., and Paksoy, T., A Novel Hybrid Approach Based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey, Energy Convers, Manage, Vol. 53, 2012, pp. 75–83.


• Kiran, M. S., Özceylan, E., Gündüz, M., and Paksoy, T., Swarm Intelligence Approaches to Estimate Electricity Energy Demand in Turkey, Knowl-Based Syst, Vol. 36, 2012, pp. 93–103.


• Yu, S., Zhu, K. J., A Hybrid Procedure for Energy Demand Forecasting in China, Energy, Vol. 37, 2012, pp. 396–404.


• Piltan, M., Shiri, H., and Ghaderi, S. F., Energy Demand Forecasting in Iranian Metal Industry Using Linear and Nonlinear Models Based on Evolutionary Algorithms, Energy Convers. Manage, Vol. 58, 2012, pp. 1–9.


• Yang, X. S., Nature-Inspired Optimization Algorithms: Challenges and Open Problems, Journal of Computational Science, Vol. 46, 2020, pp. 101104.


• Karaboga, D., An Idea Based on Honey Bee Swarm for Numerical Optimization, Vol. 200, 2005, pp. 1-10.


• Tereshko, V., Reaction-Diffusion Model of a Honeybee Colony’s Foraging Behaviour, In International Conference on Parallel Problem Solving from Nature, 2000, pp. 807-816.


• Seyyedi, S. M., Ajam, H., and Farahat, S., A New Approach for Optimization of Thermal Power Plant Based on The Exergoeconomic Analysis and Structural Optimization Method: Application to the CGAM Problem. Energy Conversion and Management, Vol. 51, No. 11, 2010, pp. 2202-2211.


• Valero, A., Lozano, M. A., Serra, L., Tsatsaronis, G., Pisa, J., Frangopoulos, C., and Von Spakovsky, M. R., CGAM Problem: Definition and Conventional Solution. Energy, Vol. 19, No. 3, 1994, pp. 279-286.


• Schaffer, J. D., Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, Proceedings of the First International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum Associates, Inc., Publishers, 1985.


• Bejan, A., Tsatsaronis, G., and Michael, M. J., Thermal Design and Optimization, John Wiley & Sons, 1995.


• Gulder, O., Flame Temperature Estimation of Conventional and Future Jet Fuels, Journal of Engineering for Gas Turbines Power, 108, No. 2, 1986, pp. 376-380.


• Rizk, N. K., Mongia, H. C., Semianalytica Correlations for NOx,CO and UHC Emission, Journal of Engineering for Gas Turbines Power, 115, No. 3, 1993, pp. 612-619.


• Lefebvre, A. H., Gas Turbine Combustion, Edwards Brothers, Ann Arbor, MI, Philadelphia, USA 1998.