An Improved Adaptive Spiral Dynamic Algorithm for Global Optimization
Main Article Content
Abstract
This paper proposes a new strategy to enhance the performance and accuracy of the Spiral dynamic algorithm (SDA) for use in solving real-world problems by hybridizing the SDA with the Bacterial Foraging optimization algorithm (BFA). The dynamic step size of SDA makes it a useful exploitation approach. However, it has limited exploration throughout the diversification phase, which results in getting trapped at local optima. The optimal initialization position for the SDA algorithm has been determined with the help of the chemotactic strategy of the BFA optimization algorithm, which has been utilized to improve the exploration approach of the SDA. The proposed Hybrid Adaptive Spiral Dynamic Bacterial Foraging (HASDBF) algorithm is designed so that the chemotaxis phase of bacteria represents the exploration part of the search operation. In contrast, the SDA represents the exploitation part.
Additionally, to improve search operation efficiency, the spiral model's radius and angular displacement are adaptively set according to a linear correlation concerning the fitness value. An additional phase, the elimination and dispersal phase, is obtained from BFA and added to the end of the SDA. This phase aims to improve the algorithm's final solution's accuracy by enhancing the algorithm's search strategy and performance. Simulation tests are run on unimodal and multimodal standard benchmark functions to verify the proposed algorithm. The proposed algorithm significantly outperforms SDA and Adaptive SDA (ASDA) algorithms regarding fitness value and accuracy.
Article received: 03/04/2023
Article accepted: 20/06/2023
Article published: 01/11/2023
Article Details
How to Cite
Publication Dates
References
Abbas, N. H. and Abdulsaheb, J. A., 2016. An adaptive multi-objective particle swarm optimization algorithm for multi-robot path planning, Journal of Engineering, 22(7), pp. 164–181. Doi:10.31026/j.eng.2016.07.10
Abdel-Rahman, Z., 2004. Studies on metaheuristics for continuous global optimization problems. Doctoral dissertation, Ph.D. thesis, Kyoto University, Japan.
Abraham, A., Biswas, A., Dasgupta, S. and Das, S., 2008, June. Analysis of reproduction operator in bacterial foraging optimization algorithm. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) (pp. 1476-1483). IEEE. Doi:10.1109/CEC.2008.4630988
Al-Araji, A.S., and Al-Zangana, S.J.E., 2019. Design of New hybrid neural controller for nonlinear CSTR system based on identification. Journal of Engineering, 25(4), pp. 70-89. Doi:10.31026/j.eng.2019.04.06
Biswas, A., Dasgupta, S., Das, S., and Abraham, A., 2007. Synergy of PSO and bacterial foraging optimization—a comparative study on numerical benchmarks. Innovations in hybrid intelligent systems, pp.255-263. Doi:10.1007/978-3-540-74972-1_34
Blondin, M.J., Sanchis, J., Sicard, P. and Herrero, J.M., 2018. New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm. Applied soft computing, 62, pp.216-229. Doi:10.1016/j.asoc.2017.10.007
Das, S., Biswas, A., Dasgupta, S. and Abraham, A., 2009. Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Foundations of computational intelligence volume 3: global optimization, pp.23-55. Doi:10.1007/978-3-642-01085-9_2
Dasgupta, S., Das, S., Abraham, A., and Biswas, A., 2009. Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Transactions on Evolutionary Computation, 13(4), pp. 919-941. Doi:10.1109/TEVC.2009.2021982
George, R.G., Hasanien, H.M., Badr, M.A. and Elgendy, M.A., 2018, September. A comparative study among different algorithms investigating optimum design of PID controller in automatic voltage regulator. In 2018 53rd International Universities Power Engineering Conference (UPEC) (pp. 1-6). IEEE. Doi:10.1109/UPEC.2018.8541870
Goher, K.M., Almeshal, A.M., Agouri, S.A., Nasir, A.N.K., Tokhi, M.O., Alenezi, M.R., Al Zanki, T., and Fadlallah, S.O., 2017. Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines. Robotics and biomimetics, 4(1), pp. 1-15. Doi:10.1186/s40638-017-0059-1
Jadon, S.S., Tiwari, R., Sharma, H., and Bansal, J.C., 2017. Hybrid artificial bee colony algorithm with differential evolution. Applied Soft Computing, 58, pp. 11-24. Doi:10.1016/j.asoc.2017.04.018
Jawad, M.M., and Hadi, E.A., 2019. A Comparative study of various intelligent algorithms based path planning for Mobile Robots. Journal of Engineering, 25(6), pp. 83-100. Doi:10.31026/j.eng.2019.06.07
Kasaiezadeh, A., Khajepour, A. and Waslander, S.L., 2014. Spiral bacterial foraging optimization method: Algorithm, evaluation and convergence analysis. Engineering Optimization, 46(4), pp.439-464. Doi:10.1080/0305215X.2013.776550
Kasruddin Nasir, A.N., Ahmad, M.A., and Tokhi, M.O., 2022. Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator. Journal of Low Frequency Noise, Vibration and Active Control, 41(1), pp. 340-358. Doi:10.1177/14613484211035646
Micev, M., Ćalasan, M., Ali, Z.M., Hasanien, H.M. and Aleem, S.H.A., 2021. Optimal design of automatic voltage regulation controller using hybrid simulated annealing–Manta ray foraging optimization algorithm. Ain Shams Engineering Journal, 12(1), pp.641-657. Doi:10.1016/j.asej.2020.07.010
Matajira-Rueda, D., Cruz-Duarte, J.M., Garcia-Perez, A., Avina-Cervantes, J.G., and Correa-Cely, C.R., 2018, November. A new improvement scheme of spiral algorithm (performance test). In 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pp. 1-6. Doi:10.1109/ROPEC.2018.8661438
Madinehi, N., Shaloudegi, K., Abedi, M. and Abyaneh, H.A., 2011, June. Optimum design of PID controller in AVR system using intelligent methods. In 2011 IEEE Trondheim PowerTech (pp. 1-6). IEEE. Doi:10.1109/PTC.2011.6019196
Matajira-Rueda, D., Cruz-Duarte, J.M., Garcia-Perez, A., Avina-Cervantes, J.G., and Correa-Cely, C.R., 2018, November. A new improvement scheme of spiral algorithm (performance test). In 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pp. 1-6. Doi:10.110ROPEC.2018.8661438
Madinehi, N., Shaloudegi, K., Abedi, M. and Abyaneh, H.A., 2011, June. Optimum design of PID controller in AVR system using intelligent methods. In 2011 IEEE Trondheim PowerTech (pp. 1-6). IEEE. Doi:10.1109/PTC.2011.6019196To’aima, F. M., Al-Aani, Y. N. and Salbi, H. A. A., 2015. Optimal location of static synchronous compensator (STATCOM) for IEEE 5-Bus standard system using genetic algorithm, Journal of Engineering, 21(7), pp. 72–84. Doi:10.31026/j.eng.2015.07.06
Nasir, A.N.K., and Tokhi, M.O., 2015a. An improved spiral dynamic optimization algorithm with engineering application. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(6), pp. 943-954. Doi:10.1109/TSMC.2014.2383995
Nasir, A.N.K., Tokhi, M.O., and Ghani, N.M.A., 2013a, September. Novel hybrid bacterial foraging and spiral dynamics algorithms. In 2013 13th UK Workshop on Computational Intelligence (UKCI), IEEE, pp. 199-205. Doi:10.1109/UKCI.2013.6651306
Nasir, A.N.K., Tokhi, M.O., Omar, M.E., and Ghani, N.M.A., 2014, January. An improved spiral dynamic algorithm and its application to fuzzy modelling of a twin rotor system. In 2014 world symposium on computer applications & research (WSCAR), IEEE, pp. 1-6). Doi:10.1109WSCAR.2014.6916774
Nasir, A.N.K., Tokhi, M.O., Sayidmarie, O., and Ismail, R.R., 2013b, September. A novel adaptive spiral dynamic algorithm for global optimization. 13th UK Workshop on Computational Intelligence (UKCI) IEEE, pp. 334-341. Doi:10.1109UKCI.2013.6651325
Nasir, A. N. K., Tokhi, M. O., Abd Ghani, N. M., and Raja Ismail, R. M. T., 2012a. Novel adaptive spiral dynamics algorithms for global optimization. 2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS), Limerick, Ireland, pp. 99–104. Doi:10.1109UKCI.2013.6651325
Nasir, A.N.K., Ismail, R.R., and Tokhi, M.O., 2016. Adaptive spiral dynamics metaheuristic algorithm for global optimisation with application to modelling of a flexible system. Applied Mathematical Modelling, 40(9-10), pp. 5442-5461. Doi:10.1016/j.apm.2016.01.002.
Nasir, A.N.K., Tokhi, M.O., Abd Ghani, N.M., and Ahmad, M.A., 2012b. A novel hybrid spiral-dynamics bacterial-foraging algorithm for global optimization with application to control design. In 2012 12th UK Workshop on Computational Intelligence (UKCI) IEEE, pp. 1-7. Doi:10.1109UKCI.2012.6335764
Nasir, A.N.K., and Tokhi, M.O., 2015b. Novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation. Applied Soft Computing, 27, pp. 357-375. Doi:10.1016/j.asoc.2014.11.030
Passino, K.M., 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), pp. 52-67. Doi:10.1109/MCS.2002.1004010
Simon, D., 2008. Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), pp. 702-713. Doi:10.1109/TEVC.2008.919004
Shah-Hosseini, H., 2011. Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. International Journal of Computational Science and Engineering, 6(1-2), pp. 132-140. Doi:10.1504/IJCSE.2011.041221
Sharma, S., Kumar, S., and Nayyar, A., 2019. Logarithmic spiral based local search in artificial bee colony algorithm. In Industrial Networks and Intelligent Systems: 14th EAI International Conference, INISCOM 2018, Da Nang, Vietnam, August 27–28, 2018, Proceedings, pp. 15-27. Doi:10.1007/978-3-030-05873-9_2
Stretch, D., and Adeyemo, J., 2018. Review of hybrid evolutionary algorithms for optimizing a reservoir. South African Journal of Chemical Engineering, 25(1), pp. 22-31.
Tamura, K., and Yasuda, K., 2011a. Primary study of spiral dynamics inspired optimization. IEEE, J Transactions on Electrical and Electronic Engineering, 6(S1), pp. S98-S100. Doi:10.1002/tee.20628
Tamura, K., and Yasuda, K., 2011b. Spiral multipoint search for global optimization. In 2011 10th International Conference on Machine Learning and Applications and Workshops, V. 1, IEEE, pp. 470-475. Doi:10.1109/ICMLA.2011.131.
Tamura, K., and Yasuda, K., 2011c. Spiral dynamics inspired optimization. Journal of Advanced Computational Intelligence and Intelligent Informatics, 15(8), pp. 1116-1122. Doi:10.20965/jaciii.2011.p1116
Yang, X.S., and Deb, S., 2009, December. Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC), IEEE, pp. 210-214.