# Combining Parallel Stochastic Methods and Mixed Termination Rules in Optimization
###### Abstract
This research demonstrates that the parallel PSO method performs exceptionally well on problems with evenly distributed loads, while in cases of uneven loads, the use of asynchronous approaches and larger particle populations proves more effective. Another study develops a parallel implementation of a stochastic Radial Basis Function (RBF) algorithm for global optimization, which does not require derivatives and is suitable for computationally intensive functions. The paper compares the performance of the proposed method with other parallel optimization methods, noting that the RBF method achieves good results with one, four, or eight processors across various optimization problems [48]. Parallel techniques are also applied in real-time image processing, which is critical for applications such as video surveillance, diagnostic medicine, and autonomous vehicles. A related article examines parallel architectures and algorithms, identifying effective applications and evaluating the challenges and limitations of their practical implementation, aiming to develop more efficient solutions [49]. Another study reviews parallel computing strategies for computational fluid dynamics (CFD), focusing on tools like OpenMP, MPI, and CUDA to reduce computational time [50]. Finally, a significant study explores parallel programming technologies for processing genetic sequences, presenting three main parallel computing models and analyzing applications such as sequence alignment, single nucleotide polymorphism calling, sequence preprocessing, and pattern detection [51]. Genetic Algorithms (GAs) are methods that can be easily parallelized, and several researchers have thoroughly examined them in the literature. For example, a universal parallel execution method for Genetic Algorithms, known as IIP (Parallel, Independent, and Identical Processing), is presented. This method achieves acceleration with the use of m processors. The technique calculates execution speed and compares results on small-size problems and the non-parametric Inverse Fractal Problem [52]. Similarly, another paper presents a distributed mechanism for improving resource protection in a digital ecosystem. This mechanism can be used not only for secure and reliable transactions but also to enhance collaboration among digital ecosystem community members to secure the environment, and also employs Public Key Infrastructure to provide strong protection for access workflows [53]. Genetic Algorithms have also been used as a strategy for optimizing large and complex problems, specifically in wavelength selection for multi-component analysis. The study examines the effectiveness of the genetic algorithm in finding acceptable solutions in a reasonable time and notes that the algorithm incorporates prior information to improve performance, based on mathematically grounded frameworks such as the schema theorem [54]. Additionally, another paper develops two genetic algorithm-based algorithms to improve the lifetime and energy consumption in mobile wireless sensor networks (MWSNs). The first algorithm is an improvement of the Unequal Clustering Genetic Algorithm, while the second algorithm combines the K-means Clustering Algorithm with Genetic Algorithms. These algorithms aim to better adapt to dynamic changes in network topology, thereby enhancing network lifetime and energy consumption [55]. Lastly, the use of Genetic Algorithms has promising applications across various medical specialties, including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. A related paper reviews and introduces applications of Genetic Algorithms in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, enabling physicians to envision potential applications of this metaheuristic method in their medical careers [56].