Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

Jia, Heming; Lang, Chunbo; Oliva, Diego; Song, Wenlong; Peng, Xiaoxu

doi:10.3390/rs11121421

Cited by 165 publications

(85 citation statements)

References 56 publications

(55 reference statements)

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“…In this paper, we use the Harris Hawk optimization algorithm proposed by Mirjalili et al [34]. The Harris Hawk optimization algorithm has been successfully applied to many practical applications due to its complete attack strategy and has achieved good results [35][36][37], but there are still some disadvantages that make it subject to local problems. We used a nonlinear control parameter strategy to improve the linear convergence factor, to maintain a smooth transition of algorithm exploration and exploitation, increase population diversity, and to accelerate convergence.…”

Section: Improve the Constrained Dna-sequence Lower Bound's Methodsmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the long-term storage of large datasets. The DNA sequences used in storage need to consider relevant constraints to avoid nonspecific hybridization reactions, such as the No-runlength constraint, GC-content, and the Hamming distance. In this work, a new nonlinear control parameter strategy and a random opposition-based learning strategy were used to improve the Harris hawks optimization algorithm (for the improved algorithm NOL-HHO) in order to prevent it from falling into local optima. Experimental testing was performed on 23 widely used benchmark functions, and the proposed algorithm was used to obtain better coding lower bounds for DNA storage. The results show that our algorithm can better maintain a smooth transition between exploration and exploitation and has stronger global exploration capabilities as compared with other algorithms. At the same time, the improvement of the lower bound directly affects the storage capacity and code rate, which promotes the further development of DNA storage technology.

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Section: Improve the Constrained Dna-sequence Lower Bound's Methodsmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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“…Compute fitness values Find the best solution X b for i � 1: N do Use equation 9to update E if (|E| ≥ 1) then Compute new position for X i using equation 7 Compute the probability Pr i using equation (20) and r pr using equation (21) (10) if Pr i ≤ r pr then (11) Find the neighbor solution Y for the solution x i (12) Calculate the fitness value F(Y) for Y (13) if F(Y) < F(x i ) then (14) x i � Y (15) else (16) Compute the difference between the fitness value of x i and Y as δ � F(x i ) − F(Y) (17) if (Prob ≤ r 5 ) then (18) x i � Y (19) Update the value of temperature T using equation 6) (20else (21) Compute the energy E using equation 9) (22Update x i using operators of the HHO as in Algorithm 2 (23) t � t + 1 (24) Until t > t max (25) Return the best solution x b ALGORITHM 3: HHOSA scheduler for job scheduling in cloud computing.…”

Section: N) While (Terminal Condition Is Not Met) Domentioning

confidence: 99%

“…. , n, (19) where each value of x i belongs to an integer value in the interval [1, m] For more clarity, consider there are eight jobs and four machines, and the generated values for the current solution are given in x i as x i � 4 1 4 4 2 3 1 3 . In this representation, the first value in x i is 4, and this indicates that the first job will be allocated on the fourth machine.…”

Section: Initial Stagementioning

confidence: 99%

“…Local optima avoidance and smooth transition from exploration to exploitation are among the major advantages of the HHO algorithm. According to these behaviors, the HHO has been applied to several global optimization and real-world engineering problems, including image segmentation [18,19], feature selection [20], spatial assessment of landslide susceptibility [21], parameter estimation of photovoltaic cells [22], image denoising [23], and others [24][25][26][27][28][29]. However, the HHO suffers from some limitations that affect its performance such that its exploration ability is weaker than its exploitation ability, and this leads to degradation of the performance of convergence and the quality of the solution.…”

Section: Introductionmentioning

confidence: 99%

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Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Attiya

Elaziz

Xiong

2020

Computational Intelligence and Neuroscience

100

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In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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“…Since proposed [53], it has been used widely [54]- [56], such as solar energy [57]- [59], feature selection [60], drug design and discovery [61]. Furthermore, a large number of improved HHO variants have been presented, for example, hybrid HHO-based sine cosine mechanism [62], Nelder-mead driven HHO [63], generalized Gaussian distribution HHO [64], multi-objective HHO [65], mutation strategies-based HHO [66], diversification enriched HHO [58], Multi-population version [67] random forest model based-HHO [68]. In this study, the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer have been integrated to enhance and restore the search capability of the HHO.…”

Section: Proposed Sglhhomentioning

confidence: 99%

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

et al. 2020

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Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning. INDEX TERMS Di-2-Ethylhexyl phthalate; Hepatotoxicity; Support vector machine; Harris hawks optimization; Salp swarm algorithm; Grey wolf optimizer.

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Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

Cited by 165 publications

References 56 publications

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

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