A study of crossover operators and reference set sizes for scatter search in unconstrained function optimization

This paper explores different crossover operators and how they affect the Scatter Search (SS) algorithm in unconstrained function optimization. It also explores how the size of the reference set affects convergence and robustness. An introduction to Scatter Search is given along with the typical template. It follows with a thorough explanation of the specific implementation that is used. Two types of tests are designed: (1) what is the best fitness value that it can reach within a maximum number of fitness evaluations and (2) how fast it can converge to goal fitness. These tests are performed using different crossover operators and reference set sizes. Results are also compared apples to apples with a basic implementation of Particle Swarm Optimization (PSO). The results show that although PSO is quicker at converging, SS shows more robustness with higher overall success rates.