Abstract
Locust Swarms are a recently-developed multi-optima particle swarm. To test the potential of the new technique, they have been applied to the 1000-dimension optimization problems used in the recent CEC2008 Large Scale Global Optimization competition. The results for Locust Swarms are competitive on these problems, and in particular, much better than other particle swarm-based techniques. An analysis of these results leads to a simple guideline for parameter selection in Locust Swarms that has a broad range of effective performance. Further analysis also demonstrates that “dimension reductions” during the search process are the single largest factor in the performance of Locust Swarms and potentially a key factor in the performance of other search techniques.
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Chen, S. (2009). An Analysis of Locust Swarms on Large Scale Global Optimization Problems. In: Korb, K., Randall, M., Hendtlass, T. (eds) Artificial Life: Borrowing from Biology. ACAL 2009. Lecture Notes in Computer Science(), vol 5865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10427-5_21
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DOI: https://doi.org/10.1007/978-3-642-10427-5_21
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