Title of article :
An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation
Author/Authors :
Xia Li، نويسنده , , Jianping Luo، نويسنده , , Min-Rong Chen، نويسنده , , Na Wang، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
9
From page :
143
To page :
151
Abstract :
Several types of evolutionary computing methods are documented in the literature and are well known for solving unconstrained optimisation problems. This paper proposes a hybrid scheme that combines the merits of a global search algorithm, the shuffled frog-leaping algorithm (SFLA) and local exploration, extremal optimisation (EO) and that exhibits strong robustness and fast convergence for high-dimensional continuous function optimisation. A modified shuffled frog-leaping algorithm (MSFLA) is investigated that improves the leaping rule by properly extending the leaping step size and adding a leaping inertia component to account for social behaviour. To further improve the local search ability of MSFLA and speed up convergence, we occasionally introduce EO, which has an excellent local exploration capability, in the local exploration process of the MSFLA. It is characterised by alternating the coarse-grained Cauchy mutation and the fine-grained Gaussian mutation. Compared with standard particle swarm optimisation (PSO), SFLA and MSFLA for six widely used benchmark examples, the hybrid MSFLA-EO is shown to be a good and robust choice for solving high-dimensional continuous function optimisation problems. It possesses excellent performance in terms of the mean function values, the success rate and the fitness function evaluations (FFE), which is a rough measure of the complexity of the algorithm.
Keywords :
Evolutionary Computation , Extremal optimisation , Shuffled frog-leaping algorithm , particle swarm optimisation , Continuous optimisation
Journal title :
Information Sciences
Serial Year :
2012
Journal title :
Information Sciences
Record number :
1215012
Link To Document :
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