Improving the performance of particle swarms through dimension reductions — A case study with locust swarms

Abstract

A key challenge for many heuristic search techniques is scalability - techniques that work well on low-dimension problems may perform poorly on high-dimension problems. To the extent that some problems/problem domains are separable, this can lead to a benefit for search techniques that can exploit separability. The standard algorithm for particle swarm optimization does not provide opportunities to exploit separable problems. However, the design of locust swarms involves two phases (scouts and swarms), and “dimension reductions” can be easily implemented during the scouts phase. This ability to exploit separability in locust swarms leads to large performance improvements on separable problems. More interestingly, dimension reductions can also lead to significant performance improvements on non-separable problems. Results on the Black-Box Optimization Benchmarking (BBOB) problems show how dimension reductions can help locust swarms perform better than standard particle swarms - especially on high-dimension problems.