Brain Storm Optimization (BSO) Algorithm Original Implementation

Brain Storm Optimization (BSO) is an intelligent optimization algorithm inspired by human brainstorming activities, designed to solve complex optimization problems. The algorithm simulates the process of generating, evaluating, and refining ideas during group discussions, progressively enhancing solution quality through multiple iterations. In code implementation, BSO typically initializes a population of candidate solutions and iteratively applies creativity-inspired operators to explore the search space efficiently.

The core algorithmic framework involves these key steps with corresponding programming considerations: Clustering and Grouping: Similar to subgroup discussions in brainstorming, the algorithm first partitions solutions into clusters using techniques like k-means clustering. This allows localized optimization within each cluster, implemented through distance-based grouping functions. New Solution Generation: Solutions within clusters may undergo random modifications (e.g., Gaussian mutation) or cross-cluster fusion (simulated via crossover operators), mimicking the generation of novel ideas during discussions. Code typically includes mutation probability parameters and crossover rate controls. Elite Preservation: High-fitness solutions are preserved through elitism mechanisms (e.g., storing top-k solutions per iteration) to maintain convergence toward optimal regions. Random Perturbation: Stochastic components (like random walk operators) are incorporated to prevent premature convergence to local optima, often implemented using randomization functions with controlled variance.

Due to its simplicity and flexibility, BSO shows significant application potential in function optimization, parameter tuning, and engineering design problems. The algorithm's modular structure allows straightforward adaptation to various problem domains through customizable fitness functions and termination criteria.