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

ADMT Journal

دوره 15، شماره 2، شهریور 2022، صفحه 69-82   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30486/admt.2022.1940026.1314

نویسندگان

Ali reza Shafiee sarvestany^{* 1}؛ Mohammadjavad Mahmoodabadi²

¹Department of Mechanical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

²Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran

چکیده

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.

کلیدواژه‌ها

Artificial Bee Colony Algorithm؛ CGAM Problem؛ Firefly Algorithm؛ Hybrid Optimization Algorithm

مراجع

• 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.