Memetic firefly algorithm for combinatorial optimization

TL;DR

This paper introduces a memetic firefly algorithm hybridized with local search for solving combinatorial optimization problems, specifically graph 3-coloring, showing promising results compared to existing methods.

Contribution

The paper presents a novel hybrid memetic firefly algorithm tailored for combinatorial problems, demonstrating its effectiveness on graph coloring benchmarks.

Findings

The memetic firefly algorithm outperformed other algorithms on medium-sized graphs.

Results indicate high potential for applying firefly algorithms to various combinatorial problems.

The approach achieved competitive coloring results compared to state-of-the-art methods.

Abstract

Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.