# Improved Brain Storm Optimization Algorithm Based on Flock Decision Mutation Strategy
## 1 Introduction
The introduction of agglomerative hierarchical clustering into BSO has been implemented, followed by an analysis of its effect on the efficacy of the creation operator. Although there is a marginal improvement in optimization accuracy compared to the original BSO, this modified algorithm still does not adequately address the issue of the BSO algorithm's propensity for getting stuck at the local optimum. A brain storm optimization algorithm enhanced with a reinforcement learning mechanism has been introduced [14]. Four distinct mutation tactics were devised to bolster the algorithm's searching ability at various phases. The results show that this algorithm surpasses additional improved BSO algorithms with regard to efficiency. However, the algorithm's performance is less competitive than other improved swarm intelligence algorithms. [PERSON] proposed a brain storm optimization algorithm with an adaptive learning strategy [15]. The BSO-AL, as proposed, creates new individuals through exploration, imitation, or adaptive learning. However, the algorithm tested fewer functions and lacked some reference values. An enhanced BSO algorithm that utilizes a difference-mutation technique and leverages the global-best individual has been introduced [16]. This approach substitutes the conventional mutation step in BSO with a difference step, markedly accelerating the convergence process. The algorithm adopts a global-best mutation strategy, which substantially enhances optimization performance. Nevertheless, the algorithm still struggles with the issue of local optimum entrapment, which hinders its effectiveness at tackling intricate multi-modal challenges. [PERSON] innovatively integrated the principles of chaos theory into the BSO algorithm by applying chaotic maps [17], resulting in an enhanced version of the BSO algorithm. This modified algorithm exhibits a marginal performance improvement compared to its predecessor, though the benefits are not particularly significant. Nevertheless, the incorporation of chaotic maps presents a novel approach to tackle the issue of algorithms that are prone to premature convergence. A chaotic local search-enhanced memetic brain storm optimization algorithm has been introduced [18]. This study presents a novel method that combines the BSO algorithm and chaotic local search, aiming to address the propensity of the BSO algorithm to become stuck at local maxima. Despite this integration, the algorithm showsonly marginal gains in optimization precision. An enhanced BSO algorithm incorporating an advanced discussion mechanism has been proposed [19]. It integrates a difference step approach while streamlining the BSO's selection methodology. This innovation aims to bolster global search capabilities during the initial phase and to refine local search activities in subsequent stages, thereby elevating the precision of the algorithm's optimization results. Additionally, the implementation of the difference step approach improved the convergence velocity of the algorithm. Despite these advancements, its performance for optimizing high-dimensional multi-modal problems does not meet anticipated benchmarks, and the algorithm remains prone to entrapment in local optimum. A proposed global-best brain storm optimization algorithm incorporates a discussion mechanism and a difference step [20]. This algorithm melts a suite of enhancement tactics, each with distinct characteristics, resulting in superior convergence rates and optimization precision relative to its predecessors. Nonetheless, the algorithm exhibits a propensity to become ensnared in local optima when dealing with intricate optimization challenges, indicating a necessity for additional refinements. In conclusion, the existing improved brain storm optimization algorithms suffer from several deficiencies, including sluggish rates of convergence, suboptimal optimization accuracy, and a significant tendency to become trapped in a local optimum.