Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

Xie, Wei; Cheng, Xing; Wang, Jie-Sheng; Guo, Shasha; Guo, M. W.; Zhu, Lei

doi:10.1109/access.2020.3014309

Cited by 19 publications

(9 citation statements)

References 84 publications

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“…It is worth mentioning that the comparison algorithms we use in this section are some advanced and hybrid algorithms, not the basic algorithm used above. These algorithms are FWHHO 39 , PPSO 40 , CLPPSO 40 , HHOHGSO 41 , DE 15 and CMA-ES 42 . The algorithms used for comparison and their parameter settings are based on the parameters used by the original author or the parameters widely used by various researchers.…”

Section: Experiments and Discussionmentioning

confidence: 99%

A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Dong

Zou

et al. 2022

Sci Rep

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This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents and maintain the population diversity. We test the presented hybrid algorithm on 30 well-known benchmark functions, CEC2019 benchmark functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to many statE−of-thE−art methods in terms of the quality of the resulting solution.

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Section: Experiments and Discussionmentioning

confidence: 99%

A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Dong

Zou

et al. 2022

Sci Rep

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“…Xie et al [180] tried to solve the HHO's shortcomings by proposing a hybrid algorithm that combined the HHO and Henary Gas Solubility Optimization (HGSO) algorithm. The authors used two benchmark functions to verify their algorithm: CEC 2005 and CEC2017.…”

Section: Hybrid Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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“…The method not only fails to find the appropriate value but also wastes a lot of time in adjusting parameters. Due to the optimization ability of HGSO being better than other intelligent algorithms, HGSO is chosen as a parameter adjuster to optimize the parameters of the PI controller to achieve high-precision trajectory tracking and planning [24,[27][28][29][30][31]. The specific method flow is shown in Figure 5.…”

Section: Hgso Parameter Regulator For Pi Controllermentioning

confidence: 99%

“…At the same time, the introduction of intelligent algorithms for parameter adjustment can automatically achieve efficient and accurate parameter selection. HGSO is based on the principle of gas solubility function to design the optimization algorithm [27][28][29][30][31]. The algorithm has not only a strong optimization ability but also groups the swarm particles to achieve multi-group parallel optimization.…”

Section: Introductionmentioning

confidence: 99%

“…HGSO was inspired by Henry's Gas Law, formulated by William Henry in 1803 [38]. The basic idea of Henry's law of gas solubility is described as [27][28][29]: at a constant temperature and pressure, the solubility of a gas in a given volume of liquid is proportional to the partial pressure of the gas in equilibrium. So, temperature and pressure are going to affect the gas solubility.…”

Section: Introduction and Description Of Hgsomentioning

confidence: 99%

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A PI Control Method with HGSO Parameter Regulator for Trajectory Planning of 9-DOF Redundant Manipulator

Liu

Zhu

et al. 2022

Sensors

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In order to solve the tracking accuracy problem of the redundant manipulator, a PI control method with Henry gas solubility optimization parameter regulator (PI-HGSO) is proposed in this paper. This method consists of the controller and the parameter regulator. The characteristic is that the position deviation of a manipulator is equivalent to a specific function; namely, the proportional-integral (PI) controller is used to adjust the deviation input. The error can be better corrected by the processing of the PI controller so that the inverse kinematics solution of the minimum error can be realized. At the same time, the parameter selection of PI controllers has always been a difficulty in controller design. To address the problem, Henry gas solubility optimization (HGSO) is selected as a parameter regulator to optimize the parameters and obtain the optimal controller, thereby achieving high-precision trajectory tracking. Experiments on 9-DOF redundant manipulator show that our method achieves competitive tracking accuracy in contrast with others. Meanwhile, the efficiency and accuracy of the PI controller are greatly guaranteed by using HGSO to automatically optimize controller parameters instead of making approximate adjustments through infinite manual trial and error. Therefore, the feasibility and competitive superiority of PI-HGSO is fully proved in trajectory planning of redundant manipulators.

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Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

Cited by 19 publications

References 84 publications

A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

A PI Control Method with HGSO Parameter Regulator for Trajectory Planning of 9-DOF Redundant Manipulator

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