3,508 research outputs found
Firefly Algorithm, Stochastic Test Functions and Design Optimisation
Modern optimisation algorithms are often metaheuristic, and they are very
promising in solving NP-hard optimization problems. In this paper, we show how
to use the recently developed Firefly Algorithm to solve nonlinear design
problems. For the standard pressure vessel design optimisation, the optimal
solution found by FA is far better than the best solution obtained previously
in literature. In addition, we also propose a few new test functions with
either singularity or stochastic components but with known global optimality,
and thus they can be used to validate new optimisation algorithms. Possible
topics for further research are also discussed.Comment: 12 pages, 11 figure
A New Metaheuristic Bat-Inspired Algorithm
Metaheuristic algorithms such as particle swarm optimization, 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.Comment: 10 pages, 2 figure
Bat Algorithm for Multi-objective Optimisation
Engineering optimization is typically multiobjective and multidisciplinary
with complex constraints, and the solution of such complex problems requires
efficient optimization algorithms. Recently, Xin-She Yang proposed a
bat-inspired algorithm for solving nonlinear, global optimisation problems. In
this paper, we extend this algorithm to solve multiobjective optimisation
problems. The proposed multiobjective bat algorithm (MOBA) is first validated
against a subset of test functions, and then applied to solve multiobjective
design problems such as welded beam design. Simulation results suggest that the
proposed algorithm works efficiently.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1004.417
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