Skip to main content
SpringerLink
Log in

Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Harris Hawks Optimization (HHO) is a newly proposed metaheuristic algorithm, which primarily works based on the cooperative system and chasing behavior of Harris’ hawks. In this paper, an augmented modification called HHMV is proposed to alleviate the main shortcomings of the conventional HHO that converges tardily and slowly to the optimal solution. Further, it is easy to trap in the local optimum when solving multi-dimensional optimization problems. In the proposed method, the conventional HHO is hybridized with Multi-verse Optimizer to improve its convergence speed, the exploratory searching mechanism through the beginning steps, and the exploitative searching mechanism in the final steps. The effectiveness of the proposed HHMV is deeply analyzed and investigated by using classical and CEC2019 benchmark functions with several dimensions size. Moreover, to prove the ability of the proposed HHMV method in solving real-world problems, five engineering design problems are tested. The experimental results confirmed that the exploration and exploitation search mechanisms of conventional HHO and its convergence speed have been significantly augmented. The HHMV method proposed in this paper is a promising version of HHO, and it obtained better results compared to other state-of-the-art methods published in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Abasi, A. K., Khader, A. T., Al-Betar, M. A., Naim, S., Makhadmeh, S. N., & Alyasseri, Z. A. A. (2020). Link-based multi-verse optimizer for text documents clustering. Applied Soft Computing, 87, 106002.

    Article  Google Scholar 

  • Abd Elaziz, M., Elsheikh, A. H., Oliva, D., Abualigah, L., Lu, S., & Ewees, A. A. (2021). Advanced metaheuristic techniques for mechanical design problems. Archives of Computational Methods in Engineering, 29, 1–22.

    Google Scholar 

  • Abd Elaziz, M., Ewees, A. A., Neggaz, N., Ibrahim, R. A., Al-qaness, M. A., & Lu, S. (2021). Cooperative meta-heuristic algorithms for global optimization problems. Expert Systems with Applications, 176, 114788.

    Article  Google Scholar 

  • Abd Elaziz, M., Oliva, D., Ewees, A. A., & Xiong, S. (2019). Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer. Expert Systems with Applications, 125, 112–129.

    Article  Google Scholar 

  • Abd Elaziz, M., Oliva, D., & Xiong, S. (2017). An improved opposition-based sine cosine algorithm for global optimization. Expert Systems with Applications, 90, 484–500.

    Article  Google Scholar 

  • Abualigah, L. (2020). Group search optimizer: A nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Computing and Applications, 7, 1–24.

    Google Scholar 

  • Abualigah, L. (2020). Multi-verse optimizer algorithm: A comprehensive survey of its results, variants, and applications. Neural Computing and Applications, 32, 1–21.

    Google Scholar 

  • Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2021). Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, 116158.

    Article  Google Scholar 

  • Abualigah, L., & Alkhrabsheh, M. (2021). Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. The Journal of Supercomputing, 78, 1–26.

    Google Scholar 

  • Abualigah, L., & Diabat, A. (2021). Advances in sine cosine algorithm: A comprehensive survey. Artificial Intelligence Review, 54, 1–42.

    Article  Google Scholar 

  • Abualigah, L., Diabat, A., & Geem, Z. W. (2020). A comprehensive survey of the harmony search algorithm in clustering applications. Applied Sciences, 10(11), 3827.

    Article  Google Scholar 

  • Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. . H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.

    Article  Google Scholar 

  • Abualigah, L., & Dulaimi, A. J. (2021). A novel feature selection method for data mining tasks using hybrid sine cosine algorithm and genetic algorithm. Cluster Computing, 24, 1–16.

    Article  Google Scholar 

  • Abualigah, L., Shehab, M., Alshinwan, M., Mirjalili, S., & Abd Elaziz, M. (2021). Ant lion optimizer: A comprehensive survey of its variants and applications. Archives of Computational Methods in Engineering, 28, 1397–1416.

    Article  Google Scholar 

  • Abualigah, L., Shehab, M., Diabat, A., & Abraham, A. (2020). Selection scheme sensitivity for a hybrid salp swarm algorithm: Analysis and applications. Engineering with Computers, 1–27.

  • Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250.

    Article  Google Scholar 

  • Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2019). Modified krill herd algorithm for global numerical optimization problems. In S. Shandilya, S. Shandilya, & A. Nagar (Eds.), Advances in nature-inspired computing and applications (pp. 205–221). Springer.

  • Ali, E., El-Hameed, M., El-Fergany, A., & El-Arini, M. (2016). Parameter extraction of photovoltaic generating units using multi-verse optimizer. Sustainable Energy Technologies and Assessments, 17, 68–76.

    Article  Google Scholar 

  • Alsalibi, B., Abualigah, L., & Khader, A. T. (2020). A novel bat algorithm with dynamic membrane structure for optimization problems. Applied Intelligence, 51, 1–26.

    Google Scholar 

  • Alshinwan, M., Abualigah, L., Shehab, M., Abd Elaziz, M., Khasawneh, A. M., Alabool, H., & Al Hamad, H. (2021). Dragonfly algorithm: A comprehensive survey of its results, variants, and applications. Multimedia Tools and Applications, 80, 1–38.

    Article  Google Scholar 

  • Altabeeb, A. M., Mohsen, A. M., Abualigah, L., & Ghallab, A. (2021). Solving capacitated vehicle routing problem using cooperative firefly algorithm. Applied Soft Computing, 108, 107403.

    Article  Google Scholar 

  • Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23(3), 715–734.

    Article  Google Scholar 

  • Bao, X., Jia, H., & Lang, C. (2019). A novel hybrid Harris Hawks optimization for color image multilevel thresholding segmentation. IEEE Access, 7, 76529–76546.

    Article  Google Scholar 

  • Baykasoglu, A. (2012). Design optimization with chaos embedded great deluge algorithm. Applied Soft Computing, 12, 1055–1567.

    Article  Google Scholar 

  • Baykasoğlu, A., & Akpinar, Ş. (2015). Weighted superposition attraction (WSA): A swarm intelligence algorithm for optimization problems-part 2: Constrained optimization. Applied Soft Computing, 37, 396–415.

    Article  Google Scholar 

  • Baykasoğlu, A., & Ozsoydan, F. B. (2015). Adaptive firefly algorithm with chaos for mechanical design optimization problems. Applied Soft Computing, 36, 152–164.

    Article  Google Scholar 

  • Belegundu, A. . D., & Arora, J. . S. (1985). A study of mathematical programming methods for structural optimization. Part i: Theory. International Journal for Numerical Methods in Engineering, 21(9), 1583–1599.

    Article  Google Scholar 

  • Beyer, H.-G., & Schwefel, H.-P. (2002). Evolution strategies—A comprehensive introduction. Natural Computing, 1(1), 3–52.

    Article  Google Scholar 

  • Chen, H., Jiao, S., Wang, M., Heidari, A. A., & Zhao, X. (2020). Parameters identification of photovoltaic cells and modules using diversification-enriched Harris Hawks optimization with chaotic drifts. Journal of Cleaner Production, 244, 118778.

    Article  Google Scholar 

  • Chen, H., Wang, M., & Zhao, X. (2020). A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Applied Mathematics and Computation, 369, 124872.

    Article  Google Scholar 

  • Cheng, H., Zhang, Y., & Li, F. (2009). Improved genetic programming algorithm. In International Asia symposium on intelligent interaction and affective computing, ASIA’09 (pp. 168–171). IEEE. https://doi.org/10.1109/ASIA.2009.39

  • Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113–127.

    Article  Google Scholar 

  • Cuevas, E., Echavarría, A., & Ramírez-Ortegón, M. A. (2014). An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Applied intelligence, 40(2), 256–272.

    Article  Google Scholar 

  • Cully, A., & Demiris, Y. (2017). Quality and diversity optimization: A unifying modular framework. IEEE Transactions on Evolutionary Computation, 22(2), 245–259.

    Article  Google Scholar 

  • Czerniak, J. M., Zarzycki, H., & Ewald, D. (2017). AAO as a new strategy in modeling and simulation of constructional problems optimization. Simulation Modelling Practice and Theory, 76, 22–33.

    Article  Google Scholar 

  • de Melo, V. V., & Banzhaf, W. (2018). Drone squadron optimization: A novel self-adaptive algorithm for global numerical optimization. Neural Computing and Applications, 30(10), 3117–3144.

    Article  Google Scholar 

  • Deb, K. (1991). Optimal design of a welded beam via genetic algorithms. AIAA Journal, 29(11), 2013–2015.

    Article  Google Scholar 

  • Dhou, K. (2020). A new chain coding mechanism for compression stimulated by a virtual environment of a predator–prey ecosystem. Future Generation Computer Systems, 102, 650–669.

    Article  Google Scholar 

  • Dhou, K., & Cruzen, C. (2020). A new chain code for bi-level image compression using an agent-based model of echolocation in dolphins. In 2020 IEEE 6th international conference on dependability in sensor, cloud and big data systems and application (DependSys) (pp. 87–91). IEEE.

  • Dhou, K., & Cruzen, C. (2021). A highly efficient chain code for compression using an agent-based modeling simulation of territories in biological beavers. Future Generation Computer Systems, 118, 1–13.

    Article  Google Scholar 

  • Digalakis, J. G., & Margaritis, K. G. (2001). On benchmarking functions for genetic algorithms. International Journal of Computer Mathematics, 77(4), 481–506.

    Article  Google Scholar 

  • Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks (Vol. 4, Citeseer, pp. 1942–1948).

  • Eid, A., Kamel, S., & Abualigah, L. (2021). Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Computing and Applications, 33, 1–29.

    Article  Google Scholar 

  • El Aziz, M. A., Ewees, A. A., & Hassanien, A. E. (2016). Hybrid swarms optimization based image segmentation. In S. Bhattacharyya, P. Dutta, S. De, & G. Klepac (Eds.), Hybrid soft computing for image segmentation (pp. 1–21). Springer.

  • Elaziz, M. A., Oliva, D., & Xiong, S. (2017). An improved opposition-based sine cosine algorithm for global optimization. Expert Systems with Applications, 90, 484–500.

    Article  Google Scholar 

  • Essa, F., Abd Elaziz, M., & Elsheikh, A. . H. (2020). An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer. Applied Thermal Engineering, 170, 115020.

    Article  Google Scholar 

  • Ewees, A. A., Abd El Aziz, M., & Hassanien, A. E. (2019). Chaotic multi-verse optimizer-based feature selection. Neural Computing and Applications, 31(4), 991–1006.

    Article  Google Scholar 

  • Ewees, A. A., Elaziz, M. A., & Houssein, E. H. (2018). Improved grasshopper optimization algorithm using opposition-based learning. Expert Systems with Applications, 112, 156–172.

    Article  Google Scholar 

  • Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine predators algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377.

    Article  Google Scholar 

  • Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 191, 105190.

    Article  Google Scholar 

  • Faris, H., Hassonah, M. A., Ala’M, A.-Z., Mirjalili, S., & Aljarah, I. (2018). A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Computing and Applications, 30(8), 2355–2369.

    Article  Google Scholar 

  • Fathy, A., & Rezk, H. (2018). Multi-verse optimizer for identifying the optimal parameters of PEMFC model. Energy, 143, 634–644.

    Article  Google Scholar 

  • Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., & Alizadeh, Y. (2008). Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197(33–40), 3080–3091.

    Article  Google Scholar 

  • Gandomi, A. H., & Deb, K. (2020). Implicit constraints handling for efficient search of feasible solutions. Computer Methods in Applied Mechanics and Engineering, 363, 112917.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gandomi, A. H., Yang, X.-S., Alavi, A. H., & Talatahari, S. (2013). Bat algorithm for constrained optimization tasks. Neural Computing and Applications, 22(6), 1239–1255.

    Article  Google Scholar 

  • Golilarz, N. A., Gao, H., & Demirel, H. (2019). Satellite image de-noising with Harris Hawks meta heuristic optimization algorithm and improved adaptive generalized gaussian distribution threshold function. IEEE Access, 7, 57459–57468.

    Article  Google Scholar 

  • Guedria, N. B. (2016). Improved accelerated PSO algorithm for mechanical engineering optimization problems. Applied Soft Computing, 40, 455–467.

    Article  Google Scholar 

  • Gupta, S., Deep, K., Moayedi, H., Foong, L. K., & Assad, A. (2020). Sine cosine grey wolf optimizer to solve engineering design problems. Engineering with Computers, 37, 1–27.

    Google Scholar 

  • Han, S. -Y., Wan, X. -Y., Wang, L., Zhou, J., & Zhong, X. -F. (2016). Comparison between genetic algorithm and differential evolution algorithm applied to one dimensional bin-packing problem. In 2016 3rd International conference on informative and cybernetics for computational social systems (ICCSS) (pp. 52–55). IEEE.

  • Hassan, M. H., Kamel, S., Abualigah, L., & Eid, A. (2021). Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Systems with Applications, 182, 115205.

    Article  Google Scholar 

  • Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872.

    Article  Google Scholar 

  • He, Q., & Wang, L. (2007). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation, 186(2), 1407–1422.

    Article  Google Scholar 

  • He, Q., & Wang, L. (2007). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20(1), 89–99.

    Article  Google Scholar 

  • Houssein, E. H., Hosney, M. E., Oliva, D., Mohamed, W. M., & Hassaballah, M. (2020). A novel hybrid Harris Hawks optimization and support vector machines for drug design and discovery. Computers & Chemical Engineering, 133, 106656.

    Article  Google Scholar 

  • Huang, F.-Z., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation, 186(1), 340–356.

    Article  Google Scholar 

  • Hu, C., Li, Z., Zhou, T., Zhu, A., & Xu, C. (2016). A multi-verse optimizer with levy flights for numerical optimization and its application in test scheduling for network-on-chip. PLoS One, 11(12), e0167341.

    Article  Google Scholar 

  • Jiang, Y., Luo, Q., Wei, Y., Abualigah, L., et al. (2021). An efficient binary gradient-based optimizer for feature selection. Mathematical Biosciences and Engineering, 18(4), 3813–3854.

    Article  Google Scholar 

  • Kamboj, V. K., Nandi, A., Bhadoria, A., & Sehgal, S. (2020). An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Applied Soft Computing, 89, 106018.

    Article  Google Scholar 

  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Tech. Rep. 2, Technical Report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department.

  • Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: ray optimization. Computers & Structures, 112, 283–294.

    Article  Google Scholar 

  • Kaveh, A., & Talatahari, S. (2010). An improved ant colony optimization for constrained engineering design problems. Engineering Computations, 27(1), 155–182.

    Article  Google Scholar 

  • Koziel, S., Leifsson, L., & Yang, X.-S. (2014). Solving computationally expensive engineering problems: Methods and applications (Vol. 97). Springer.

  • Krishnanand, K., & Ghose, D. (2005). Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS 2005 (pp. 84–91). IEEE.

  • Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194(36–38), 3902–3933.

    Article  Google Scholar 

  • Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10(2), 629–640.

    Article  Google Scholar 

  • Long, W., Wu, T., Liang, X., & Xu, S. (2019). Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert Systems with Applications, 123, 108–126.

    Article  Google Scholar 

  • Mack, G. A., & Skillings, J. H. (1980). A Friedman-type rank test for main effects in a two-factor ANOVA. Journal of the American Statistical Association, 75(372), 947–951.

    Article  Google Scholar 

  • Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188(2), 1567–1579.

    Article  Google Scholar 

  • Mezura-Montes, E., & Coello, C. A. C. (2008). An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. International Journal of General Systems, 37(4), 443–473.

    Article  Google Scholar 

  • Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.

    Article  Google Scholar 

  • Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based systems, 89, 228–249.

    Article  Google Scholar 

  • Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based systems, 96, 120–133.

    Article  Google Scholar 

  • Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.

    Article  Google Scholar 

  • Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.

    Article  Google Scholar 

  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Article  Google Scholar 

  • Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513.

    Article  Google Scholar 

  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  • Moayedi, H., Gör, M., Lyu, Z., & Bui, D. T. (2020). Herding behaviors of grasshopper and Harris Hawk for hybridizing the neural network in predicting the soil compression coefficient. Measurement, 152, 107389.

    Article  Google Scholar 

  • Moayedi, H., Osouli, A., Nguyen, H., & Rashid, A. S. A. (2019). A novel Harris Hawks’ optimization and k-fold cross-validation predicting slope stability. Engineering with Computers. https://doi.org/10.1007/s00366-019-00828-8.

    Article  Google Scholar 

  • Mohammed, H., & Rashid, T. (2020). A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design. Neural Computing and Applications, 32, 1–18.

    Article  Google Scholar 

  • Pan, W.-T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74.

    Article  Google Scholar 

  • Pathak, V. K., & Srivastava, A. K. (2020). A novel upgraded bat algorithm based on Cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems. Engineering with Computers, 1–28.

  • Premkumar, M., Jangir, P., Kumar, B. S., Sowmya, R., Alhelou, H. H., Abualigah, L., Yildiz, A. R., & Mirjalili, S. (2021). A new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: Diversity analysis and validations, IEEE Access.

  • Ragsdell, K., & Phillips, D. (1976). Optimal design of a class of welded structures using geometric programming. Journal of Engineering for Industry, 98, 1021–1025.

    Article  Google Scholar 

  • Rahman, C. M., & Rashid, T. A. (2021). A new evolutionary algorithm: Learner performance based behavior algorithm. Egyptian Informatics Journal, 22, 213–223.

    Article  Google Scholar 

  • Rao, S. (2019). Engineering optimization: Theory and practice. Wiley.

  • Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.

    Article  Google Scholar 

  • Ray, T., & Saini, P. (2001). Engineering design optimization using a swarm with an intelligent information sharing among individuals. Engineering Optimization, 33(6), 735–748.

    Article  Google Scholar 

  • Ridha, H. M., Heidari, A. A., Wang, M., & Chen, H. (2020). Boosted mutation-based Harris Hawks optimizer for parameters identification of single-diode solar cell models. Energy Conversion and Management, 209, 112660.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sadollah, A., Sayyaadi, H., & Yadav, A. (2018). A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing, 71, 747–782.

    Article  Google Scholar 

  • Şahin, C. B., Dinler, Ö. B., & Abualigah, L. (2021). Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Applied Intelligence, 51, 1–17.

    Article  Google Scholar 

  • Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design, 112(2), 223–229.

    Article  Google Scholar 

  • Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software, 105, 30–47.

    Article  Google Scholar 

  • Sarker, R. A., Elsayed, S. M., & Ray, T. (2014). Differential evolution with dynamic parameters selection for optimization problems. IEEE Transactions on Evolutionary Computation, 18(5), 689–707.

    Article  Google Scholar 

  • Sattar, D., & Salim, R. (2020). A smart metaheuristic algorithm for solving engineering problems. Engineering with Computers, 37, 1–29.

    Google Scholar 

  • Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., & Khasawneh, A. . M. (2020). Moth–flame optimization algorithm: Variants and applications. Neural Computing and Applications, 32(14), 9859–9884.

    Article  Google Scholar 

  • Shehab, M., Alshawabkah, H., Abualigah, L., & Nagham, A.-M. (2020). Enhanced a hybrid moth-flame optimization algorithm using new selection schemes. Engineering with Computers, 37, 1–26.

    Google Scholar 

  • Shukri, S., Faris, H., Aljarah, I., Mirjalili, S., & Abraham, A. (2018). Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer. Engineering Applications of Artificial Intelligence, 72, 54–66.

    Article  Google Scholar 

  • Singh, N., Chiclana, F., Magnot, J.-P., et al. (2020). A new fusion of salp swarm with sine cosine for optimization of non-linear functions. Engineering with Computers, 36(1), 185–212.

    Article  Google Scholar 

  • Truong, K. H., Nallagownden, P., Baharudin, Z., & Vo, D. N. (2019). A quasi-oppositional-chaotic symbiotic organisms search algorithm for global optimization problems. Applied Soft Computing, 77, 567–583.

    Article  Google Scholar 

  • Tsai, J.-F. (2005). Global optimization of nonlinear fractional programming problems in engineering design. Engineering Optimization, 37(4), 399–409.

    Article  Google Scholar 

  • Wang, S., Liu, Q., Liu, Y., Jia, H., Abualigah, L., Zheng, R., & Wu, D. (2021). A hybrid SSA and SMA with mutation opposition-based learning for constrained engineering problems. Computational Intelligence and Neuroscience.

  • Wang, Z., Luo, Q., & Zhou, Y. (2021). Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Engineering with Computers, 37, 3665–3698.

  • Wang, X., Pan, J.-S., & Chu, S.-C. (2020). A parallel multi-verse optimizer for application in multilevel image segmentation. IEEE Access, 8, 32018–32030.

    Article  Google Scholar 

  • Xu, M., You, X., & Liu, S. (2017). A novel heuristic communication heterogeneous dual population ant colony optimization algorithm. IEEE Access, 5, 18506–18515.

    Article  Google Scholar 

  • Yang, X. .-S. (2008). Nature-inspired metaheuristic algorithms. Luniver Press.

  • Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary computation, 3(2), 82–102.

    Article  Google Scholar 

  • Yi, J., Huang, D., Fu, S., He, H., & Li, T. (2016). Multi-objective bacterial foraging optimization algorithm based on parallel cell entropy for aluminum electrolysis production process. IEEE Transactions on Industrial Electronics, 63(4), 2488–2500. https://doi.org/10.1109/TIE.2015.2510977.

    Article  Google Scholar 

  • Yousri, D., Abd Elaziz, M., Abualigah, L., Oliva, D., Al-Qaness, M. A., & Ewees, A. A. (2021). Covid-19 x-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Applied Soft Computing, 101, 107052.

    Article  Google Scholar 

  • Yousri, D., Allam, D., & Eteiba, M. B. (2020). Optimal photovoltaic array reconfiguration for alleviating the partial shading influence based on a modified Harris Hawks optimizer. Energy Conversion and Management, 206, 112470.

    Article  Google Scholar 

  • Zhang, M., Luo, W., & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178(15), 3043–3074.

    Article  Google Scholar 

  • Zheng, R., Jia, H., Abualigah, L., Liu, Q., & Wang, S. (2021). Deep ensemble of slime mold algorithm and arithmetic optimization algorithm for global optimization. Processes, 9(10), 1774.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  2. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

  3. Division of Engineering, New York University Abu Dhabi, 129188, Saadiyat Island, Abu Dhabi, UAE

    Ali Diabat

  4. Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA

    Ali Diabat

  5. Center for Cyber–Physical Systems, Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, UAE

    Davor Svetinovic

  6. Department of Information Systems and Operations Management, Vienna University of Economics and Business, Vienna, Austria

    Davor Svetinovic

  7. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Mohamed Abd Elaziz

  8. Artificial Intelligence Research Center (AIRC), Ajman University, 346, Ajman, UAE

    Mohamed Abd Elaziz

  9. Department of Artificial Intelligence Science and Engineering, Galala University, Suze, 435611, Egypt

    Mohamed Abd Elaziz

  10. School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, Russia, 634050

    Mohamed Abd Elaziz

Authors
  1. Laith Abualigah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Diabat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Davor Svetinovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Abd Elaziz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laith Abualigah.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abualigah, L., Diabat, A., Svetinovic, D. et al. Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems. J Intell Manuf 34, 2693–2728 (2023). https://doi.org/10.1007/s10845-022-01921-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-022-01921-4

Keywords

Search

Navigation