A<scp><i>Canis lupus</i></scp>inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

Nandi, Ayani; Kamboj, Vikram Kumar

doi:10.1002/nme.6573

Cited by 15 publications

(8 citation statements)

References 120 publications

(153 reference statements)

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“…Nandi and Kamboj [168] combined canis lupus and the Grey wolf optimizer to improve the HHO. The novel algorithm, which is called hHHO-GWO, was tested on CEC 2005, CEC-BC-2017, and 11 different engineering problems.…”

Section: Other Hho Variantsmentioning

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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Section: Other Hho Variantsmentioning

confidence: 99%

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

et al. 2022

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“…To verify the optimization performance of the PRN-SSA for basic test functions, the basic sparrow search algorithm (SSA) [40], improved sparrow search algorithm (GGSC-SSA) [41], particle swarm optimization algorithm (PSO) [42], grey wolf optimizer algorithm (GWO) [43], whale optimization algorithm (WOA) [44], and harris hawk optimization algorithm (HHO) [45] are compared with PRN-SSA. On the one hand, it aims to judge whether the improvement of this research can exceed the performance of other SSA and its variants, and on the other hand, it can reflect the optimization ability of the improved sparrow algorithm compared with other precision algorithms.…”

Section: Comparison Algorithms and Parameter Settingsmentioning

confidence: 99%

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

et al. 2022

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Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.

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“…Their extensive application to engineering problems has created the need for more efficient and problem‐specific developments. In swarm intelligence (SI), an important part of metaheuristics, algorithms are based on the mimicking of natural systems and cooperative populations and showed different degrees of success in particularly challenging optimization problems 3‐7 . Particle swarm optimization (PSO), first developed by Russell C. Eberhart and J. Kennedy 8,9 is one of the most popular SI algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improved particle swarm optimizer for problems with variable evaluation time: Application to asymptotic homogenization

Argilaga

Guo

2022

Numerical Meth Engineering

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This work presents a new particle swarm optimizer (PSO)-based metaheuristic algorithm designed to reduce overall computational cost without compromising optimization's precision in functions with variable evaluation time. The algorithm exploits the evaluation time gradient in addition to the convergence gradient attempting to reach the same convergence precision following a more economical path. The particle's newly incorporated time information is usually in contradiction to the past memories of best function evaluations thus degrading convergence. A technique is proposed in order to modulate the new cognitive input that consists of progressive reducing of its weight in order to confer the algorithm the appropriate time-convergence balance. Results show that the proposed algorithm not only provides computational economy, but also unexpectedly improves convergence per se due to a better exploration in the initial stages of optimization. Its application in asymptotic homogenization of a cracked poroelastic medium confirms its superior performance compared to a series of alternative optimization algorithms. The proposed algorithm improvement allows to extend the applicability of PSO and PSO-based algorithms to problems that were previously thought to be too computationally expensive for population-based approaches.

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ACanis lupusinspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

Cited by 15 publications

References 120 publications

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

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

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

Improved particle swarm optimizer for problems with variable evaluation time: Application to asymptotic homogenization

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