arXiv:1004.4170v1 [math.OC] 23 Apr 2010
A New Metaheuristic Bat-Inspired Algorithm
Xin-She Yang
Department of Engineering, University of Cambridge,
Trumpington Street, Cambridge CB2 1PZ, UK
Abstract
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and
harmony search are now becoming powerful methods fo r solving many tough optimiza-
tion problems. In this paper, we propose a new metaheuristic method, the Bat Algo -
rithm, based on the echolocation behaviour of bats. We also intend to combine the
advantages of existing algorithms into the new ba t algorithm. After a deta iled formu-
lation and ex planation of its implementation, we will then co mpare the proposed algo-
rithm with other existing algorithms, including genetic algorithms and particle swarm
optimization. Simulations s how tha t the proposed algorithm seems much superior to
other algorithms, and further studies are also discussed.
Citation detail:
X.-S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Coop-
erative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies
in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010).
1 Introduction
Metaheuristic algorithms such as particle swarm optimization and simulated annealing
are now beco ming powerful methods for solving many tough optimization problems [3-
7,11]. The vast majority of heuristic and metaheuristic algorithms have been derived
from the behaviour of biological systems and/or physical systems in nature. For exam-
ple, particle s warm optimization was developed based on the swarm behaviour of birds
and fish [7, 8], while s imulated annealing was based on the annealing process of metals
[9].
New algorithms are also emerg ing recently, including harmony search and the firefly
algorithm. The former was inspired by the improvising process of composing a piece
of music [4], while the latter was formulated based on the flashing behaviour of fireflies
[14]. Each of these algorithms has cer tain adva ntages and disadvantages. For example,
simulating annealing can almost guarantee to find the optimal solution if the cooling
process is slow enough and the simulation is running long enough; however, the fine
adjustment in parameters does affect the convergence rate o f the optimization proce ss.
A natural questio n is whether it is possible to combine major advantages of these
algorithms and try to develop a potentially better algorithm? This paper is such an
attempt to address this issue.
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