Using Evolving Algorithm with Distance Indicator for Solving Different Non-linear Optimization Problems
DOI:
https://doi.org/10.23851/mjs.v33i3.1167Keywords:
Many Objective Problems, Bat Algorithm, Inverted Generational Distance.Abstract
In this paper, we have relied on the dominant control system as an important tool in building the group of leaders because it allows leaders to contain less dense areas, avoid local areas and produce a more compact and diverse Pareto front. Nine standard nonlinear functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. MATLAB was used to generate all the results of the proposed method and other methods in the same field of work.
References
Bosman, P. A., & Thierens, D. (2003). The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE transactions on evolutionary computation, 7(2), 174-188.
CrossRef DOI: https://doi.org/10.1109/TEVC.2003.810761
Yang, X. S. (2011). Bat algorithm for multi-objective optimization. International Journal of Bio-Inspired Computation, 3(5), 267-274.
CrossRef DOI: https://doi.org/10.1504/IJBIC.2011.042259
Laudis, L. L., Shyam, S., Jemila, C., & Suresh, V. (2018). MOBA: multi objective bat algorithm for combinatorial optimization in VLSI. Procedia Computer Science, 125, 840-846.
CrossRef DOI: https://doi.org/10.1016/j.procs.2017.12.107
Remha, S., Chettih, S., & Arif, S. (2018). A novel multi-objective bat algorithm for optimal placement and sizing of distributed generation in radial distributed systems. Advances in Electrical and Electronic Engineering, 15(5), 736-746.
CrossRef DOI: https://doi.org/10.15598/aeee.v15i5.2417
Talal, R. (2014). Comparative study between the (ba) algorithm and (pso) algorithm to train (rbf) network at data classification. International Journal of Computer Applications, 92(5), 16-22.
CrossRef DOI: https://doi.org/10.5120/16004-4998
Khan, K., & Sahai, A. (2012). A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. International Journal of Intelligent Systems and Applications, 4(7), 23.
CrossRef DOI: https://doi.org/10.5815/ijisa.2012.07.03
Sheah, R. H., & Abbas, I. T. (2021). Using multi-objective bat algorithm for solving multi-objective non-linear programming problem. Iraqi Journal of Science, 997-1015.
CrossRef DOI: https://doi.org/10.24996/ijs.2021.62.3.29
AlSattar, H. A., Zaidan, A. A., Zaidan, B. B., Abu Bakar, M. R., Mohammed, R. T., Albahri, O. S., ... & Albahri, A. S. (2020). MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems. Neural Computing and Applications, 32(8), 3101-3115.
CrossRef DOI: https://doi.org/10.1007/s00521-018-3808-3
Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119.
CrossRef DOI: https://doi.org/10.1016/j.eswa.2015.10.039
Qi, Y., Ma, X., Liu, F., Jiao, L., Sun, J., & Wu, J. (2014). MOEA/D with adaptive weight adjustment. Evolutionary computation, 22(2), 231-264. DOI: https://doi.org/10.1162/EVCO_a_00109
Li, H., Deb, K., Zhang, Q., Suganthan, P. N., & Chen, L. (2019). Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties. Swarm and Evolutionary Computation, 46, 104-117.
CrossRef DOI: https://doi.org/10.1016/j.swevo.2019.02.003
Li, K., Deb, K., Zhang, Q., & Kwong, S. (2014). An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE transactions on evolutionary computation, 19(5), 694-716.
CrossRef DOI: https://doi.org/10.1109/TEVC.2014.2373386
Fleischer, M. (2003, April). The measure of Pareto optima applications to multi-objective metaheuristics. In International conference on evolutionary multi-criterion optimization (pp. 519-533). Springer, Berlin, Heidelberg.
CrossRef DOI: https://doi.org/10.1007/3-540-36970-8_37
Moore, J., & Chapman, R. (1999). Application of Particle Swarm to Multiobjective Optimization: Dept. Comput. Sci. Software Eng., Auburn Univ.
Peng, G., Fang, Y. W., Peng, W. S., Chai, D., & Xu, Y. (2016). Multi-objective particle optimization algorithm based on sharing-learning and dynamic crowding distance. Optik, 127(12), 5013-5020.
CrossRef DOI: https://doi.org/10.1016/j.ijleo.2016.02.045
Hennequin, S., & Restrepo, L. M. R. (2016). Fuzzy model of a joint maintenance and production control under sustainability constraints. IFAC-PapersOnLine, 49(12), 1216-1221.
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