New memetic self adaptive firefly algorithm for continuous optimisation
The firefly algorithm is a recent nature inspired algorithm that is receiving increasing attention from the scientific community during the last few years. One of its most promising variants is given by the memetic self adaptive firefly algorithm MSA FFA, recently introduced to solve combinatorial problems. In this paper we propose a modification of the original MSA FFA for continuous optimisation problems. The most important features of our method are: the problem dependent selection of control parameters for self adaptation, a simple population model providing an adequate trade off between exploration and exploitation, and the use of an adaptive size Luus Jaakola random local search. This new method is applied to solve a very difficult real world continuous optimisation problem arising in geometric modelling and manufacturing. The paper also provides the first reliable, standardised benchmark for this optimisation problem. This benchmark is used for a comparative analysis of our method with respect to some of the most popular nature inspired algorithms. Our results show that the proposed method outperforms previous approaches including the standard firefly algorithm for most of the instances in the benchmark.