Brain Storm Optimization Algorithms: More Questions than Answers

Abstract

Brain storm optimization (BSO) algorithms is a framework that indicates algorithms using converging operation and diverging operation to locate the optima of optimization problems. Hundreds of articles on the BSO algorithms have been published in different journals and conference proceedings, even though there are more questions than answers. In this chapter, BSO algorithms are comprehensively surveyed and the future research directions are discussed from the perspective of model-driven and data-driven approaches. For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Many works have been conducted on the BSO algorithms, there are still massive questions on this algorithm need to be answered. These questions, which include the properties of algorithms, the connection between optimization algorithms and problems, and the real-world application should be studied for the BSO algorithms or, more broadly, for the swarm intelligence algorithms.