Todays, various heuristic optimization methods have been developed. Many of these algorithms are inspired from physical processes or swarm behaviors in nature. gravitational search Algorithm (GSA) is an optimization algorithm based on the law of gravity and mass interactions. In the proposed algorithm, the search agents are a collection of masses. In this paper, mentioned algorithm is used to solve of the Frequency Assignment Problem (FAP). For ability test of the algorithm, CALMA benchmarks are used and results are good.

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.

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.

Because of photography limitations, it is sometimes impossible to achieve an image of high quality and sufficient clarity by taking just one picture from a scene. Therefore, various methods of image fusion have been proposed. On the other hand, random population-based algorithms have been used extensively for optimization. These algorithms are often inspired by the physical processes or the behavior of the living beings. Gravitation search algorithm (GSA) is an optimization algorithm that is based on the gravitation and mass concept and the search agents in this algorithm are masses. In this study, GSA is used to optimize the image fusion process when using images with different focuses. In this way, spatial frequency measure is used. The performance of proposed method is compared with two other methods: a peer approach when the particle swarm optimization (PSO) algorithm is used instead of GSA, and a pixel-based fuzzy approach. Experimental results show that the proposed method has a superior performance and this method can be applied to colored images, as well.

gravitational search algorithm (GSA) is one of the newest swarm based optimization algorithms, which has been inspired by the Newtonian laws of gravity and motion. GSA has empirically shown to be an efficient and robust stochastic search algorithm. Since introducing GSA a convergence analysis of this algorithm has not yet been developed. This paper introduces the first attempt to a formal convergence analysis of the standard gravitational search algorithm which involves with randomness and time varying parameters. In this analysis the behavior of GSA on the facet of mass interaction is considered. The paper provides a formal proof that each object converges to a stable point.