# Parallelization of the Bison Algorithm Applied to Data Classification
## 2 Related Work  
Parallelization techniques for nature-inspired algorithms are crucial for handling large datasets and computationally intensive tasks. MPI (Message Passing Interface) is one of the early frameworks used for this purpose. It provides a standard for communication between multiple nodes in a distributed system, enabling efficient parallel computations. Several studies have explored using MPI to parallelize nature-inspired algorithms. For instance, a parallel GA based on the MPI environment was developed, adapting the serial GA to a parallel implementation [7]. Another example is the MPI-OpenMP hybrid approach used to implement a multi-objective Particle Swarm Optimization (PSO) algorithm, combining distributed memory (MPI) and shared memory (OpenMP) parallelization [8]. Additionally, an MPI-based parallel GA was proposed for multiple geographical optimization problems, using a hybrid of fixed-position and sliding models. MapReduce, introduced by [PERSON] and [PERSON] [9], revolutionized the way large-scale data processing tasks are handled by abstracting the data processing into two fundamental operations: Map and Reduce. This framework has been effectively used to parallelize algorithms like Genetic Programming and Particle Swarm Optimization. For example, a MapReduce-based Particle Swarm Optimization algorithm was developed to handle large-scale clustering tasks, showing notable improvements in processing time and scalability [10]. Apache Spark, an advancement over MapReduce, offers in-memory computation, which significantly speeds up data processing tasks. For instance, [PERSON] and [PERSON] [11] compared the performance of Glow-worm Swarm Optimization (GSO) when implemented using Spark and MapReduce. Their findings indicated that Spark's in-memory computation capabilities provided faster and more efficient processing, making it more suitable for high-dimensional function optimization tasks. [PERSON] involved the implementation of a parallel fuzzy clustering algorithm using Spark. This study demonstrated that the Spark-based algorithm outperformed traditional clustering methods, providing better scalability and handling of real-time data streams [12]. This research highlights Spark's capability to efficiently process and analyze large datasets, making it a preferred choice for implementing nature-inspired algorithms in a parallel computing environment. [PERSON] implemented a scalable design of an artificial bee colony for big data classification using Apache Spark [13]. The performance results reveal that the proposed fitness algorithm can efficiently deal with unbalanced datasets and can scale very well, achieving excellent speedup and scaleup results. Similar implementations were introduced for the Differential Evolution (DE) [14] and the PSO algorithms [15]. GPU-based parallelization, although not specifically MPI, MapReduce, or Spark, is another common approach. [PERSON] conducted a survey focused on GPU-based parallel implementations of Swarm Intelligence algorithms, including GA and Differential Evolution [16]. Similarly, [PERSON] et al. presented a survey specifically on GPU-based parallel implementations of Particle Swarm Optimization (PSO) [17]. [PERSON] et al. surveyed parallel swarm optimization algorithms, covering CPU- and GPU-based implementations using frameworks like OpenMP, MPI, and R, though focused specifically on PSO [18]. A review of parallelization strategies for Swarm Intelligence algorithms identified particle-level parallelization as a common approach, easily implemented using OpenMP for multi-core CPUs. Overall, the integration of MPI, MapReduce, and Spark into the parallelization of nature-inspired algorithms has shown significant improvements in performance and scalability. These frameworks provide the necessary tools to handle the growing complexity and size of data in various computational intelligence applications. In this paper, we have parallelized the Bison Algorithm [6], which is another nature-inspired algorithm applied to optimization problems.