SOLVING INTEGER PROGRAMMING PROBLEMS BY USING POPULATION-BASED BEETLE ANTENNAE SEARCH ALGORITHM

Ivona Brajević; Miodrag  Brzaković; Goran  Jocić

doi:10.5937/jpmnt9-34900

Ivona Brajević Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad
Miodrag Brzaković Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad
Goran Jocić Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad

DOI: https://doi.org/10.5937/jpmnt9-34900

Abstract

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems. This method employs the population's capability to search diverse regions of the search space to provide better guarantee for ﬁnding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.

References

Akay, B., & Karaboga, D. (2009). Solving Integer Programming Problems by Using Artificial Bee Colony Algorithm. In: Serra R., Cucchiara R. (eds) AI*IA 2009: Emergent Perspectives in Artificial Intelligence. AI*IA 2009. Lecture Notes in Computer Science, 5883 (pp. 355-364). Springer, Berlin, Heidelberg.

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. https://doi.org/10.1007/s10845-010-0393-4.

Brajević I. (2021) A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems. Mathematics, 9(11), 1211. https://doi.org/10.3390/math9111211.

Brajević, I., & Ignjatović, J. (2019). An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems. Journal of Intelligent Manufacturing, 30(6), 2545–2574. https://doi.org/10.1007/s10845-018-1419-6.

Brajević, I., & Stanimirović, P. S. (2018). An improved chaotic firefly algorithm for global numerical optimization. International Journal of Computational Intelligence Systems, 12(1), 131 – 148. https://doi.org/10.2991/ijcis.2018.25905187

Brajević, I., Stanimirović, P. S., Li, S., & Cao, X. (2020). A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm. International Journal of Computational Intelligence Systems, 13(1), 810 – 821. https://doi.org/10.2991/ijcis.d.200612.001.

Du, B., He, Y., & Zhang, Y. (2020). Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network. Electronics, 9, 1570. https://doi.org/10.3390/electronics9101570.

Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35. https://doi.org/10.1007/s00366-011-0241-y.

Guedria, N. B. (2016). Improved accelerated PSO algorithm for mechanical engineering optimization problems. Applied Soft Computing, 40, 455–467. https://doi.org/10.1016/j.asoc.2015.10.048.

Jiang, X., & Li, S. (2018). BAS: Beetle antennae search algorithm for optimization problems. International Journal of Robotics and Control, 1(1) 1–5. https://doi.org/10.5430/ijrc.v1n1p1.

Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department.

Kennedy, J., & Eberhart, R. (1995), Particle swarm optimization. In Proceedings of the 1995 IEEE international conference on neural networks (Perth, Australia) (pp. 1942–1948). Piscataway, NJ: IEEE Service Center.

Khan, A. T., Cao, X., Li, Z., & Li, S. (2021). Enhanced Beetle Antennae Search with Zeroing Neural Network for online solution of constrained optimization. Neurocomputing, 447, 294-306. https://doi.org/10.1016/j.neucom.2021.03.027.

Mafarja, M. M., & Mirjalili, S. (2017). Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing, 260, 302-312. https://doi.org/10.1016/j.neucom.2017.04.053.

Parsopoulos, K., & Vrahatis, M. (2005) Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems. In: Wang L., Chen K., Ong Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, 3612 (pp. 582–591). Springer, Berlin, Heidelberg.

Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026.

Tawhid, M.A., Ali, A.F., & Tawhid, M.A. (2019). Multidirectional harmony search algorithm for solving integer programming and minimax problems. International Journal of Bio-Inspired Computation, 13, 141–158. https://doi.org/10.1504/IJBIC.2019.099179.

Wang, P., Gao, Y., Wu, M., Zhang, F., & Li, G. (2020), In-Field Calibration of Triaxial Accelerometer Based on Beetle Swarm Antenna Search Algorithm. Sensors, 20, 947. https://doi.org/10.3390/s20030947.

Wang, Y., Gao, S., Zhou, M., & Yu, Y. (2021). A Multi-Layered Gravitational Search Algorithm for Function Optimization and Real-World Problems. IEEE/CAA Journal of Automatica Sinica, 8 (1), 94-109. https://doi.org/10.1109/JAS.2020.1003462.

Wu, Q., Ma, Z., Xu, G., Li, S., & Chen, D. (2019). A Novel Neural Network Classifier Using Beetle Antennae Search Algorithm for Pattern Classification. IEEE Access, 7, 64686-64696. https://doi.org/10.1109/ACCESS.2019.2917526.

Yang, X. S. (2008). Nature-Inspired Metaheuristic Algorithms, Luniver Press.

Zhang, Y., Li, S., & Xu, B. (2021). Convergence analysis of beetle antennae search algorithm and its applications. Soft Computing, 25, 10595–10608. https://doi.org/10.1007/s00500-021-05991-z.

Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217, 3166–3173. https://doi.org/10.1016/j.amc.2010.08.049.