Abstract
We discuss the use of scout particles, or scouts, to improve the performance of a new heterogeneous particle swarm optimization algorithm, called scouting predator-prey optimizer. Scout particles are proposed as a straightforward way of introducing new exploratory behaviors into the swarm, expending minimal extra resources and without performing global modifications to the algorithm. Scouts are used both as general mechanisms to globally improve the algorithm and also as a simple approach to taylor an algorithm to a problem by embodying specific knowledge. The role of each particle and the performance of the global algorithm is tested over a set of 10 benchmark functions and against two state-of-the-art evolutionary optimizers. The experimental results suggest that, with the addition of scout particles, the new optimizer can be competitive and even superior to the other algorithms, both in terms of performance and robustness.
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Silva, A., Neves, A., Gonçalves, T. (2013). Using Scout Particles to Improve a Predator-Prey Optimizer. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_14
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