A simple strategy for maintaining diversity and reducing crowding in differential evolution
Differential evolution (DE) is a widely-effective population-based continuous optimiser that requires convergence to automatically scale its moves. However, once its population has begun to converge its ability to conduct global search is diminished, as the difference vectors used to generate new solutions are derived from the current population members' positions. In multi-modal search spaces DE may converge too rapidly, i.e., before adequately exploring the search space to identify the best...[Show more]
|Collections||ANU Research Publications|
|Source:||2012 IEEE Congress on Evolutionary Computation Proceedings|
|Montgomery_Simple2012.pdf||432.34 kB||Adobe PDF|
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