Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems

Zhang, Qingke; Gao, Hao; Zhan, Zhi‐Hui; Li, Junqing; Zhang, Huaxiang

doi:10.1016/j.knosys.2022.110206

Cited by 90 publications

(30 citation statements)

References 76 publications

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“…Following [20], X is divided into three parts according to the parameters named P 1 = 5. The first part comprises the leader and the elites (varying from 2 to P 1 ).…”

Section: Growth Optimizermentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

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“…Following [20], X is divided into three parts according to the parameters named P 1 = 5. The first part comprises the leader and the elites (varying from 2 to P 1 ).…”

Section: Growth Optimizermentioning

confidence: 99%

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Fatani

Dahou

Elaziz

et al. 2023

Sensors

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“…The energy flow optimization model of the electric-gas-heat was established by introducing the state variables over-limit penalty variable and wind and light abandonment penalty variable, and the overall objective function is the weighted sum of IES operating cost, state variables over-limit penalty cost, and wind and light abandonment penalty cost. Based on the daily load demand data of different energy forms in typical scenarios and the active electric power output of wind turbines and photovoltaic arrays, the optimal energy flow was solved by the GO algorithm [6]. Based on this, the superiority and feasibility of the model and the optimization method are confirmed by the simulation example.…”

Section: Introductionmentioning

confidence: 99%

Energy flow optimization of an electric-gas-heat integrated energy system based on the energy circuit theory

Cheng,

Zhang,

Gao

2024

J. Phys.: Conf. Ser.

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The energy flow optimization of the integrated energy system (IES) can not only decrease the operating cost of the IES but also maximize the utilization rate of renewable energy. To deal with the problem that the optimization results fall into the local optimal solution due to the over-limit of IES state variables, based on the IES energy circuit theory, this paper established an energy flow optimization model of the electric-gas-heat integrated energy system by taking the weighted sum of the operating cost, state variables over-limit penalty cost, and wind and light abandonment penalty cost as the optimization objective. The growth optimizer (GO) algorithm can avoid the local convergence of the optimization results and accelerate the solving speed of the model. Therefore, the effectiveness and feasibility of the model and the optimization method are confirmed by the simulation example.

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“…A variety of renowned metaheuristic algorithms, such as Genetic algorithms (GA) [8], Particle Swarm Optimization (PSO) [9], Gravity Search Algorithm (GSA) [10], Ant Colony algorithm (ACA) [11], Gray Wolf Optimizer (GWO) [12], Gorilla Troops Optimizer (GTO) [13], Ant Lion Optimizer (ALO) [14], Firefly algorithm [15], Cuckoo Search (CS) [16], Stochastic Fractal Search (SFS) [17], Growth Optimizer [18], and Differential Evolution (DEoptim) [19], have proven their effectiveness in numerous applications. These applications include parametric identification for various systems, such as n-link inverted pendulum [20], induction motors, photovoltaic cell models, robot manipulators, lithium-ion batteries , and power systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient Identification of Multi-Link Inverted Pendulums via Global Optimization

Ozana,

Slanina,

Deb

et al. 2024

Preprint

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Multi-link inverted pendulum systems pose intricate challenges in control theory and robotics, requiring precise dynamic parameter identification to achieve stability and robustness in control strategy design. We present a novel and efficient experimental identification procedure formulated as an optimization problem based on simple short-term datasets and metaheuristic global optimizers. We use a training dataset for identification and validation dataset to evaluate and analyze the obtained results. The study incorporates three distinct global optimization techniques, namely Stochastic Fractal Search (SFS), Growth Optimizer, and Differential Evolution (DEoptim), selected as candidates to handle the identification of multi-link pendulums and similar extremely demanding optimization jobs to be used when controlling modern mechatronic systems. We illustrate that DEoptim dominates over other global optimizers in several aspects. The proposed identification procedure is innovative, adaptable, and simple, relying solely on selected measurable signals sans further signal processing. Its versatility makes it a valuable tool for parameter identification in diverse domains. The results are supported by experiments with the laboratory triple pendulum setup and simulation experiments on a virtual quadruple inverted pendulum.

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Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems

Cited by 90 publications

References 76 publications

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Energy flow optimization of an electric-gas-heat integrated energy system based on the energy circuit theory

Efficient Identification of Multi-Link Inverted Pendulums via Global Optimization

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