# Parallelization of the Bison Algorithm Applied to Data Classification
## 1 Introduction  
Optimization is a fundamental mathematical discipline that deals with the process of making something as effective or functional as possible. The roots of optimization can be traced back to ancient times when scholars and engineers used basic principles to solve practical problems. In modern times, optimization plays a crucial role in various fields, including engineering, economics, and operations research, where the goal is often to maximize or minimize a certain objective function. Optimization problems can be broadly classified into different categories based on the nature of the objective function and the constraints. Linear programming, for example, deals with linear objective functions and linear constraints and has been extensively studied since the pioneering work of [PERSON] in the 1940s [1]. Nonlinear programming, on the other hand, involves nonlinear objective functions or constraints and presents additional challenges due to the complexity of the solution landscape. In recent years, the advent of advanced computational techniques and the availability of large datasets have further expanded the scope and application of optimization. Machine learning, for instance, relies heavily on optimization algorithms to train models and improve predictive accuracy. Metaheuristic algorithms, such as genetic algorithms and particle swarm optimization, have also gained popularity for solving complex, real-world optimization problems where traditional methods may fall short [2, 3]. Nature-inspired algorithms have garnered significant attention in the field of optimization due to their robustness and ability to find near-optimal solutions for complex problems. These algorithms are inspired by natural phenomena and processes, such as the behavior of biological species, the laws of physics, and the principles of natural selection. They offer an innovative approach to solving optimization problems, particularly thosethat are non-linear, non-differentiable, or multi-modal, where traditional methods often struggle [4]. One of the most well-known nature-inspired algorithms is the Genetic Algorithm (GA), introduced by [PERSON] in the 1970s. GAs mimic the process of natural selection by using operations such as selection, crossover, and mutation to evolve a population of candidate solutions [2]. These algorithms have been successfully applied to a wide range of optimization problems, from engineering design to machine learning and bioinformatics [2]. Ant Colony Optimization (ACO), proposed by [PERSON] in the early 1990s, is another influential nature-inspired algorithm. ACO is based on the foraging behavior of ants and their ability to find the shortest path between their colony and a food source by depositing pheromones. This algorithm has been particularly effective in solving combinatorial optimization problems, such as the traveling salesman problem and network routing [5]. Optimization algorithms are fundamental in machine learning, particularly in classification tasks where the objective is to accurately assign labels to instances. Particle Swarm Optimization (PSO) is a well-known method that has been widely used due to its simplicity and effectiveness. However, PSO and similar algorithms often encounter issues such as local minima and convergence inefficiencies. In many classification problems, regression-based approaches are employed to predict continuous values which are then thresholded to obtain discrete class labels. Traditional optimization algorithms like PSO are often used to optimize the weights in regression models. Despite their success, these algorithms can be improved by incorporating more sophisticated strategies for balancing exploration and exploitation. This paper uses the Bison Algorithm [6]. The algorithm is inspired by the collective behavior of bison herds, which exhibit efficient exploration and exploitation strategies during migration.