![]() Genet Program Product Sched Evolut Learn Approach 127–153 Zhang F, Nguyen S, Mei Y, Zhang M, Zhang F, Nguyen S, Mei Y, Zhang M (2021) Search space reduction with feature selection. ACM Trans Knowl Discov Data (TKDD) 15(1):1–32 Huang Y, Ying JJ-C, Yu PS, Tseng VS (2020) Dynamic graph mining for multi-weight multi-destination route planning with deadlines constraints. Marouani H, Al-mutiri O (2022) Optimization of reliability-redundancy allocation problems: a review of the evolutionary algorithms. Liao Z, Zhu F, Mi X, Sun Y (2023) A neighborhood information-based adaptive differential evolution for solving complex nonlinear equation system model. In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp 496–503Ĭhang P, Bao X, Meng F, Lu R (2023) Multi-objective pigeon-inspired optimized feature enhancement soft-sensing model of wastewater treatment process. Huang Z, Mei Y, Zhang F, Zhang M (2022) A further investigation to improve linear genetic programming in dynamic job shop scheduling. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris’ hawks more diverse and improve the global search ability of the algorithm. Firstly, the pinhole imaging strategy is used to enable the Harris’ hawks to approach the optimal solution faster and accelerate convergence. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed Harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. The Harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years.
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