Brain storm optimization for test task scheduling problem

Abstract

The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications. It is one kind of combinatorial optimization problem with the property of multimodal. There are a lot of global optima and local optima in its huge solution space. Brain storm optimization algorithm (BSO) is a newly proposed swarm intelligence algorithm, which can utilize the evolutionary information of solution space. BSO has two main operations including convergent operation and divergent operation to balance the exploration and exploitation ability in the search process. In addition, the clustering strategy in BSO can explore different regions simultaneously regarding the multimodal property. The generation of new solutions depends on several ways, which greatly improve the possibility of finding better solutions. According to the characteristic of TTSP, BSO is more suitable for solving TTSP comparing to other metaheuristic algorithms. In this chapter, BSO is first applied to solving TTSP cooperating with a real number coding strategy. For single-objective TTSP, BSO divides the solution space into several clusters and produces the new solution based on various methods. Moreover, some modifications are proposed on BSO for solving multi-objective TTSP. It mainly includes the determination of centers and the reservation of better solutions. From the point of view of single objective and multi-objective problem, BSO has better performance comparing to recent scheduling algorithms for solving TTSP.