A new metaheuristic bat-inspired algorithm. (English) Zbl 1197.90348

Yang, Xin-She

González, Juan R. (ed.) et al., Nature inspired cooperative strategies for optimization (NICSO 2010). Papers based on the presentations at the 4th international workshop, Granada, Spain, May 12–14, 2010. Berlin: Springer (ISBN 978-3-642-12537-9/hbk; 978-3-642-12538-6/ebook). Studies in Computational Intelligence 284, 65-74 (2010).

Summary: Metaheuristic algorithms such as particle swarm optimization, the firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the bat algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
For the entire collection see [Zbl 1192.68010].

MSC:

90C59

Approximation methods and heuristics in mathematical programming

Keywords:

genetic algorithms; particle swarm optimization; simulations