Discussing the article: "Brain Storm Optimization algorithm (Part I): Clustering"
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Check out the new article: Brain Storm Optimization algorithm (Part I): Clustering.
In this article, we will look at an innovative optimization method called BSO (Brain Storm Optimization) inspired by a natural phenomenon called "brainstorming". We will also discuss a new approach to solving multimodal optimization problems the BSO method applies. It allows finding multiple optimal solutions without the need to pre-determine the number of subpopulations. We will also consider the K-Means and K-Means++ clustering methods.
Brain Storm Optimization (BSO) is one of the exciting and innovative population optimization algorithms that is inspired by the natural phenomenon of brainstorming. This optimization method is an effective approach to solving complex problems using the principles of collective intelligence and collective behavior. BSO simulates the process of generating new ideas and solutions, similar to what happens in group discussions, which makes it a unique and promising tool for finding optimal solutions in various areas. In this article, we will look at the basic principles of BSO, its advantages and areas of application.
Population-based methods are an important tool for solving complex optimization problems. However, in the context of multimodal problems where multiple optimal solutions need to be found, existing approaches face limitations. This article presents a new optimization method called the brainstorming optimization method.
Existing approaches, such as niching and clustering methods, typically divide the population into subpopulations to search for multiple solutions. However, these methods suffer from the need to pre-determine the number of subpopulations, which can be challenging, especially when the number of optimal solutions is not known in advance. BSO compensates for this deficiency by transforming the target space into a space where individuals are clustered and updated based on their coordinates. Unlike existing methods that strive for one global optimum, the proposed BSO method directs the search process towards several "meaningful" solutions.
Author: Andrey Dik