To address the shortcomings of basic Gray Wolf Optimization (GWO) algorithm in solving complex problems, such as relying on initial population, converging too early, and easily falling into local optimality, a chaotic reverse learning initialization strategy, nonlinear control parameter convergence dynamic position update strategy are introduced to develop multi-strategy fusion Improved (IGWO) ...

It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle (PSO), genetic algorithm (GA) ant colony (ACO) are selected be applied STE. After calculation, all can obtain le...

The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability learning method. Among them, the kernel function its parameters have great impact accuracy. In order to find optimal improve accuracy of SVM, this paper proposes multi-classification gray wolf algorithm optimized SVM(GWO-SV...

In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the perform...

In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new...

Mechanical properties are important indicators for evaluating the quality of strips. This paper proposes a mechanical performance prediction model based on Gray Wolf Optimization (GWO) algorithm and Extreme Learning Machine (ELM) algorithm. In modeling process, GWO is used to determine optimal weights deviations ELM experiments model’s key parameters. The effectively avoids manual intervention ...

Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce mortality, early diagnosis and treatment are essential in leading to a greater improvement survival length patients. In this paper, hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm gray wolf optimization (GWO) was proposed for handling high dimensional redundant datasets col...

In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the latest bioinspired optimization techniques, which simulate the hunting process of grey wolves in nature. The binary version introduced here is performed using two different approaches. In the first app...

The analysis of geomagnetic suitability is the basis and premise matching navigation positioning. A evaluation model using mixed sampling an improved back propagation neural network (BPNN) based on gray wolf optimization (GWO) algorithm by incorporating dimension learning-based hunting (DLH) search strategy was proposed in this paper to accurately assess suitability. Compared with traditional m...

A valve-controlled hydraulic cylinder system has the characteristics of uncertainty and time-variance, electro-hydraulic servo unit encounters shock, vibration, other external interference when working, which seriously affect stability system. Therefore, it is necessary to introduce an active disturbance rejection controller (ADRC) into control. However, there are many ADRC parameters, difficul...