New memetic self-adaptive firefly algorithm for continuous optimisation. (2016)

• Record Type: Journal Article
• Authors: Gálvez, Akemi; Iglesias, Andrés
• Is Part Of: International journal of bio-inspired computation. Volume 8:Number 5(2016)
• Page Start: 300
• Page End: 317
• Publication Date: 2016
• Subjects: firefly algorithm -- self-adaptive FFA -- continuous optimisation -- nature-inspired metaheuristics -- memetics -- breakpoint location -- geometric modelling -- manufacturing industry; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

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.