How important is a transfer function in discrete heuristic algorithms?
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Transfer functions are considered the simplest and cheapest operators in designing discrete heuristic algorithms. The main advantage of such operators is the maintenance of the structure and other continuous operators of a continuous algorithm. However, a transfer function may show different behaviour in various heuristic algorithms. This paper investigates the behaviour and importance of transfer functions in improving performance of heuristic algorithms. As case studies, two algorithms with different mechanisms of optimisation were chosen: Gravitational Search Algorithm and Particle Swarm Optimisation. Eight transfer functions were integrated in these two algorithms and compared on a set of test functions. The results show that transfer functions may show diverse behaviours and have different impacts on the performance of algorithms, which should be considered when designing a discrete algorithm. The results also demonstrate the significant role of the transfer function in terms of improved exploration and exploitation of a heuristic algorithm.
Neural Computing and Applications
© 2014 Springer London. This is an electronic version of an article published in Neural Computing and Applications, 2015, Volume 26, Issue 3, pp 625–640. Neural Computing and Applications is available online at: http://link.springer.com/ with the open URL of your article.
Analysis of Algorithms and Complexity