Non-dominated Sorting Harris’s Hawk Multi-Objective Optimizer based on the Flush-and-Ambush Tactic

Shaymah Akram Yasear (1), Ku Ruhana Ku-Mahamud (2)
(1) School of Computing, Universiti Utara Malaysia, Sintok, Changlun, 06010, Malaysia
(2) School of Computing, Universiti Utara Malaysia, Sintok, Changlun, 06010, Malaysia
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Yasear, Shaymah Akram, and Ku Ruhana Ku-Mahamud. “Non-Dominated Sorting Harris’s Hawk Multi-Objective Optimizer Based on the Flush-and-Ambush Tactic”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, Dec. 2020, pp. 2311-9, doi:10.18517/ijaseit.10.6.11504.
In this paper, a new population update strategy is proposed to overcome the limitations of the non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm. In the NDSHHMO algorithm, the population of hawks is updated based on the average positions of the first three best solutions in the search space. This update strategy leads to the algorithm falling into local optima due to population diversity loss, which causes poor convergence toward the true Pareto front. The proposed population update strategy is inspired by the flush-and-ambush (FA) tactic employed by the Harris’s hawks in nature. The proposed algorithm is called non-dominated sorting Harris’s hawks’ multi-objective optimizer based on the flush-and-ambush tactic (FA-NDSHHMO). The population update strategy in the FA-NDSHHMO includes two main stages, namely, updating the position of hawks using proposed flush-and-ambush movement strategy and selecting the best hawks by using a non-dominated sorting approach to be used in the next generation. The proposed population update strategy aims to improve the search ability of the algorithm, in terms of the diversity of a non-dominated solution and convergence toward the Pareto front. To evaluate the performance of the FA-NDSHHMO algorithm, a set of 10 multi-objective optimization problems has been used. The obtained results show that the new population update strategy has improved the search ability of the FA-NDSHHMO. Furthermore, the results show superiority of the FA-NDSHHMO algorithm compared to the NDSHHMO, multi-objective grasshopper and grey wolf optimization algorithms.

J. Kennedy and R. Eberhar, "Particle swarm optimization," Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948, 1995.

S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: Theory and application," Advances in Engineering Software, vol. 105, pp. 30-47, 2017.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in Engineering Software, vol. 69, pp. 46-61, 2014.

M. Dorigo and G. D. Caro, "The ant colony optimization meta-heuristic," in New Ideas in Optimization, D. Corne et al., Eds.: McGraw-Hill Ltd., England, UK, 1999, pp. 11-32.

J. Del Ser et al., "Bio-inspired computation: Where we stand and what's next," Swarm and Evolutionary Computation, vol. 48, pp. 220-250, 2019.

A. Slowik and H. Kwasnicka, "Nature inspired methods and their industry applications—Swarm intelligence algorithms," IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 1004-1015, 2018.

T. Ganesan, I. Elamvazuthi, and P. Vasant, "Swarm intelligence for multiobjective optimization of extraction process," in Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics: IGI Global, 2016, pp. 516-544.

C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," Ieee Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256-279, 2004.

S. Z. Mirjalili, S. Mirjalili, S. Saremi, H. Faris, and I. Aljarah, "Grasshopper optimization algorithm for multi-objective optimization problems," Applied Intelligence, vol. 48, no. 4, pp. 805-820, 2018.

S. Mirjalili, S. Saremi, S. M. Mirjalili, and L. d. S. Coelho, "Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization," Expert Systems with Applications, vol. 47, pp. 106-119, 2016.

S. A. Yasear and K. R. Ku-Mahamud, "Non-dominated sorting Harris’s hawk multi-objective optimizer based on reference point approach " Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 3, 2019.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182-197, 2002.

M. Guo, J. Wang, L. Zhu, S. Guo, and W. Xie, "An improved grey wolf optimizer based on tracking and seeking modes to solve function optimization problems," IEEE Access, vol. 8, pp. 69861-69893, 2020.

W. Long, T. Wu, S. Cai, X. Liang, J. Jiao, and M. Xu, "A novel grey wolf optimizer algorithm with refraction learning," IEEE Access, vol. 7, pp. 57805-57819, 2019.

O. Niyomubyeyi, T. E. Sicuaio, J. I. D. Gonzí¡lez, P. Pilesjí¶, and A. Mansourian, "A comparative study of four metaheuristic algorithms, amosa, moabc, mspso, and nsga-ii for evacuation planning," Algorithms, vol. 13, no. 1, p. 16, 2020.

W. Abdou and C. Bloch, "Trade-off between diversity and convergence in multi-objective genetic algorithms," 2020, Cham: Springer International Publishing, pp. 37-50.

R. Akbari and K. Ziarati, "A rank based particle swarm optimization algorithm with dynamic adaptation," Journal of Computational and Applied Mathematics, vol. 235, no. 8, pp. 2694-2714, 2011.

G.-G. Wang and Y. Tan, "Improving metaheuristic algorithms with information feedback models," IEEE transactions on cybernetics, vol. 49, no. 2, pp. 542-555, 2017.

G. Beni and J. Wang, "Swarm intelligence in cellular robotic systems," in Robots and Biological Systems: Towards a New Bionics?, vol. 102, P. Dario, G. Sandini, and P. Aebischer, Eds. Berlin, Heidelberg: Springer, 1993, pp. 703-712.

J. C. Bednarz, "Cooperative hunting in Harris' hawks (Parabuteo unicinctus)," Science, vol. 239, no. 4847, p. 1525, 1988.

M. A. Al-Betar, M. A. Awadallah, H. Faris, I. Aljarah, and A. I. Hammouri, "Natural selection methods for grey wolf optimizer," Expert Systems with Applications, vol. 113, pp. 481-498, 2018.

A. Hussain and Y. S. Muhammad, "Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator," Complex & Intelligent Systems, pp. 1-14, 2019.

Harris hawk chase [Painting]. Available:

M. Dorigo and L. M. Gambardella, "A study of some properties of Ant-Q," in International Conference on Parallel Problem Solving from Nature, 1996: Springer, pp. 656-665.

S. Mirjalili and A. Lewis, "The whale optimization algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, 2016.

A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, "Harris hawks optimization: Algorithm and applications," Future Generation Computer Systems, vol. 97, pp. 849-872, 2019.

K. Deb and J. Sundar, "Reference point based multi-objective optimization using evolutionary algorithms," in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, 2006: ACM, pp. 635-642.

P. Jangir and N. Jangir, "A new non-dominated sorting grey wolf optimizer (NS-GWO) algorithm: Development and application to solve engineering designs and economic constrained emission dispatch problem with integration of wind power," Engineering Applications of Artificial Intelligence, vol. 72, pp. 449-467, 2018.

G. Chen, J. Qian, Z. Zhang, and Z. Sun, "Multi-objective improved bat algorithm for optimizing fuel cost, emission and active power loss in power system," IAENG International Journal of Computer Science, vol. 46, no. 1, pp. 118-133, 2019.

Y. Tian, H. Wang, X. Zhang, and Y. Jin, "Effectiveness and efficiency of non-dominated sorting for evolutionary multi-and many-objective optimization," Complex & Intelligent Systems, vol. 3, no. 4, pp. 247-263, 2017.

V. L. Vachhani, V. K. Dabhi, and H. B. Prajapati, "Improving NSGA-II for solving multi objective function optimization problems," in 2016 International Conference on Computer Communication and Informatics (ICCCI), 2016: IEEE, pp. 1-6.

M. Laumanns, L. Thiele, K. Deb, and E. Zitzler, "Combining convergence and diversity in evolutionary multiobjective optimization," Evolutionary computation, vol. 10, no. 3, pp. 263-282, 2002.

Q. Zhang, A. Zhou, S. Zhao, P. Suganthan, W. Liu, and S. Tiwari, "Multiobjective optimization test instances for the CEC 2009 special session and competition," 2008, Available:

Z. Sherinov and A. íœnveren, "Multi-objective imperialistic competitive algorithm with multiple non-dominated sets for the solution of global optimization problems," Soft Computing, vol. 22, no. 24, pp. 8273-8288, 2018/12/01 2018.

R. Liu, R. Wang, M. He, and X. Wang, "Improved artificial weed colonization based multi-objective optimization algorithm," 2017, Singapore: Springer Singapore, pp. 181-190.

S. Hinojosa, D. Oliva, E. Cuevas, G. Pajares, O. Avalos, and J. Gí¡lvez, "improving multi-criterion optimization with chaos: a novel multi-objective chaotic crow search algorithm," Neural Computing and Applications, vol. 29, no. 8, pp. 319-335, 2018.

J. Ning, B. Zhang, T. Liu, and C. Zhang, "An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem," Neural Computing and Applications, vol. 30, no. 9, pp. 2661-2671, 2018.

L.-X. Wei, X. Li, R. Fan, H. Sun, and Z.-Y. Hu, "A hybrid multiobjective particle swarm optimization algorithm based on R2 indicator," IEEE Access, vol. 6, pp. 14710-14721, 2018.

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