Global best Harmony Search with a new pitch adjustment designed for Nurse Rostering

In this paper, the Harmony Search Algorithm (HSA) is proposed to tackle the Nurse Rostering Problem (NRP) using a dataset introduced in the First International Nurse Rostering Competition (INRC2010). NRP is a combinatorial optimization problem that is tackled by assigning a set of nurses with different skills and contracts to different types of shifts, over a predefined scheduling period. HSA is an approximation method which mimics the improvisation process that has been successfully applied for a wide range of optimization problems. It improvises the new harmony iteratively using three operators: memory consideration, random consideration, and pitch adjustment. Recently, HSA has been used for NRP, with promising results. This paper has made two major improvements to HSA for NRP: (i) replacing random selection with the Global-best selection of Particle Swarm Optimization in memory consideration operator to improve convergence speed. (ii) Establishing multi-pitch adjustment procedures to improve local exploitation. The result obtained by HSA is comparable with those produced by the five INRC2010 winners’ methods.