Firefly Algorithm, Stochastic Test Functions and Design Optimisation

TL;DR

This paper demonstrates the effectiveness of the Firefly Algorithm in solving nonlinear design optimization problems, outperforming previous methods, and introduces new stochastic test functions for validating optimization algorithms.

Contribution

It applies the Firefly Algorithm to nonlinear design problems and proposes new stochastic test functions with known global optima for benchmarking.

Findings

FA outperforms previous solutions in pressure vessel design

New stochastic test functions with known global optima are introduced

Discussion on future research directions in optimization algorithms

Abstract

Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality, and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.