Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation
Connection Science - Evolutionary Learning and Optimisation
Locust swarms: a new multi-optima search technique
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Optimization in Fractal and Fractured Landscapes Using Locust Swarms
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Analyzing the role of "smart" start points in coarse search-greedy search
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Optimization in Fractal and Fractured Landscapes Using Locust Swarms
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Selection strategies for initial positions and initial velocities in multi-optima particle swarms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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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.