Random based inventive Algorithms are being widely used for optimization. An important category of these Algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed Algorithm for optimizing the orientation game is a series of optimization Algorithms that are formed with the idea of an old game and the Search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the referee's orders. The best position would be achieved by following the game laws. In this paper, the real version of the Algorithm is presented. The optimization results of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed method over the other well-known metaheuristic Algorithms.

A neuro-based approach is proposed for locating cloud-to-ground Lightning strokes. Due to insufficient experimental data, we have used the results of an electromagnetic simulator for training the developed artificial neural network. The simulator utilizes the well-known transmission line and is capable of predicting the electromagnetic field due to a return stroke channel (RSC) for various parameters associated with the shape of the channel base-current. The training process has been successfully done using the Levenberg-Marquard technique. The simulation results demonstrate that the RSC locations can be predicted with an absolute error not greater than 1 km for RSCs located within 80 km of a Lightning detection station.

This paper presents a compact neural architecture Search method for image classification using the Gravitational Search Algorithm (GSA). Deep learning, through multi-layer computational models, enables automatic feature extraction from raw data at various levels of abstraction, playing a key role in complex tasks such as image classification. Neural Architecture Search (NAS), which automatically discovers new architectures for Convolutional Neural Networks (CNNs), faces challenges such as high computational complexity and costs. To address these issues, a GSA-based approach has been developed, employing a bi-level variable-length optimization technique to design both micro and macro architectures of CNNs. This approach, leveraging a compact Search space and modified convolutional bottlenecks, demonstrates superior performance compared to state-of-the-art methods. Experimental results on CIFAR-10, CIFAR-100, and ImageNet datasets reveal that the proposed method achieves a classification accuracy of 98.48% with a Search cost of 1.05 GPU days, outperforming existing Algorithms in terms of accuracy, Search efficiency, and architectural complexity.

In this paper, a new Algorithm called Motion Coding Gravitational Search Algorithm (MGSA) is proposed to find a moving target using a unmanned aerial vehicles (UAVs). Using the laws of physics and the properties of the earth, each dimension has its own equation of motion based on the type of variable. Many traditional exploratory methods can not achieve the desired solution in high-dimensional spaces to Search for a moving target. The optimization process of the gravitational Search Algorithm, which is based on the gravitational interaction between particles, the dependence on the distance and the relationship between mass values, and the fit calculation, make this Algorithm unique. In this paper, the proposed MGSA Algorithm is proposed to solve the path complexity challenge problem in order to find the moving target through motion coding using UAVs. A set of particles in the path of Search for the target will reach a near-optimal solution through the gravity constant, weight factor, force and distance, which evolved with many Search scenarios in a GSA Algorithm. This coded method of motion makes it possible to preserve important particle properties, including the optimum global motion. The results of the existing simulation show that the proposed MGSA improves the detection performance by 12% and the time performance by 1. 71 times compared to APSO. It works better.