Nature-Inspired Algorithms: A Comprehensive Review

Houssein, Essam H.; Younan, Mina; Hassanien, Aboul Ella

doi:10.1201/9780429453427-1

Cited by 25 publications

(14 citation statements)

References 1 publication

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“…Henry gas solubility optimization (HGSO) algorithm is an up-to-date meta-heuristic method [16], which was invented by Hashim et al [17], [18] in 2019. It simulates the behavior of Henry's law and imitates the huddling behavior of gas to balance exploitation and exploration in the search space.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Cao

Liu

2020

IEEE Access

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Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry's law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cutoff conditions are met. Ten low-and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance.

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Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Cao

Liu

2020

IEEE Access

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“…Several metaheuristic optimization algorithms are proposed to solve a wide range of real-life problems. For example, nature-inspired algorithms mimic the biological, physical, or environmental processes [5,6,7]. The metaheuristic algorithms' versatility and gradient-free features consider black-box problems in addition to the theoretical developments and significant advantages.…”

Section: Introductionmentioning

confidence: 99%

“…For example, nature-inspired algorithms mimic the biological, physical, or environmental processes [5], [6]. Nature-inspired algorithms are designed to mimic the biological, physical, or environmental processes they are modeled after [7], [8]. The metaheuristic algorithms' versatility and gradient-free features consider black-box problems in addition to the theoretical developments and significant advantages.…”

Section: Introductionmentioning

confidence: 99%

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect, and other various test networks. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.INDEX TERMS Gradient-Based Optimizer (GBO); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristics; Optimization.

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“…Since the world is fast evolving, it makes technology employed in various fields of life. Optimization occupy an important role in solving complex real-world challenges [11] . Many scientific fields such as engineering design problems, economics and system management employ optimization for achieving desired targets.…”

Section: Introductionmentioning

confidence: 99%

Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm

Houssein

Mahdy

Eldin

et al. 2021

Journal of Advanced Research

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Nature-Inspired Algorithms: A Comprehensive Review

Cited by 25 publications

References 1 publication

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm

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