A simple strategy to maintain diversity and reduce crowding in particle swarm optimization
Each particle in a swarm maintains its current position and its personal best position. It is useful to think of these personal best positions as a population of attractors -- updates to current positions are based on attractions to these personal best positions. If the population of attractors has high diversity, it will encourage a broad exploration of the search space with particles being drawn in many different directions. However, the population of attractors can converge quickly --...[Show more]
|Collections||ANU Research Publications|
|Source:||Lecture Notes in Computer Science (LNCS)|
|Chen_Simple2011.pdf||232.94 kB||Adobe PDF|
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