HHO was applied to optimize the water network distribution of Homashahr city in Iran in. determined optimal wavelet coefficients by using the HHO. To denoise the satellite images, Golilarz et al. used the HHO in combination with the k-nearest neighbours and the support vector machines for chemical compound activities and descriptor selection, respectively. used HHO to optimize 29 benchmark functions and 6 engineering applications, and the results show that HHO has better competitiveness and application prospects than other SI algorithms. HHO has been widely applied to address the optimization of functions and engineering applications due to its gradient-free and powerful nature with high performance. in 2019 that mimics the way Harris eagles find and chase prey in nature, including global exploration, local besiege and pounce behaviour. HHO is a new swarm intelligence optimization algorithm proposed by Heidari et al. Still, the algorithms proposed recently or those that are not yet discovered have a wide range of application prospects. However, a single SI algorithm can not deal with all optimization problems.

All SI algorithms have two search phases: global exploration, which searches the whole space for a promising area, and local exploitation, which searches a chosen area that is promising to contain the best solution. To solve complex and large-scale problems, many swarm intelligence (SI) optimization algorithms that imitate swarm behaviour in natural phenomena, including Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Suffled Frog Leaping Algorithm (SFLA), Whale Optimization algorithm (WOA), Gravitational Search algorithm (GSA), Jaya and Harris Hawk Optimization (HHO) have been proposed. Therefore, it is difficult to achieve global optimization for multimodal functions and dynamically changing, strongly nonlinear problems using traditional algorithms. Traditional algorithms are based on derivative methods due to their mathematical complexity, which can only be used to deal with small-scale problems that must be continuous and derivable. The optimization issues in real-world problems have received increasing attention from researchers in the fields of artificial intelligence, computer vision, compressed sensing, decision-making and engineering for practical applications. 0041021003, WXZR201903).Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper.įunding: This work was partially supported by Natural Science research project of Universities in Anhui Province(NO.KJ2021A0953), Natural Science Key Scientific Research Project of West Anhui University (NO. Received: MaAccepted: JanuPublished: February 22, 2023Ĭopyright: © 2023 Yang et al. PLoS ONE 18(2):Įditor: Pijush Samui, NIT Patna: National Institute of Technology Patna, INDIA Experimental results show that the HHO-CS-OELM algorithm performs better than the state-of-the-art swarm intelligence optimization algorithms.Ĭitation: Yang T, Fang J, Jia C, Liu Z, Liu Y (2023) An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism.

The performance of the HHO-CS-OELM algorithm is verified by comparison with 14 optimization algorithms on 23 benchmark functions and an engineering problem. Meanwhile, it also overcomes the shortcoming that the exploration cannot be carried out at the late iteration in the HHO algorithm and balances the exploration and exploitation capabilities of the HHO algorithm. The chaotic sequence can improve the global search ability of the HHO algorithm due to enhancing the diversity of the population, and the opposite elite learning can enhance the local search ability of the HHO algorithm by maintaining the optimal individual. To overcome these shortcomings, a new HHO variant algorithm based on a chaotic sequence and an opposite elite learning mechanism (HHO-CS-OELM) is proposed in this paper. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitation capabilities. The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance.

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