Title: New memetic self-adaptive firefly algorithm for continuous optimisation

Authors: Akemi Gálvez; Andrés Iglesias

Addresses: Department of Applied Mathematics and Computational Sciences, E.T.S.I. Caminos, Canales y Puertos, University of Cantabria, Avda. de los Castros, s/n 39005, Santander, Spain ' Department of Applied Mathematics and Computational Sciences, E.T.S.I. Caminos, Canales y Puertos, University of Cantabria, Avda. de los Castros, s/n 39005, Santander, Spain; Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, 274-8510, Funabashi, Japan

Abstract: 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.

Keywords: firefly algorithm; self-adaptive FFA; continuous optimisation; nature-inspired metaheuristics; memetics; breakpoint location; geometric modelling; manufacturing industry.

DOI: 10.1504/IJBIC.2016.079570

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.5, pp.300 - 317

Received: 02 Oct 2013
Accepted: 26 Nov 2013
Published online: 04 Oct 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article