Multiobjective Firefly Algorithm for Continuous Optimization

TL;DR

This paper extends the firefly algorithm to handle multiobjective continuous optimization problems, demonstrating its effectiveness on test functions and design benchmarks, highlighting its potential for complex industrial engineering tasks.

Contribution

The paper introduces a multiobjective firefly algorithm tailored for continuous optimization, expanding the applicability of firefly algorithms to multiobjective problems.

Findings

Effective in approximating Pareto fronts

Successfully applied to benchmark design problems

Shows promise for complex industrial applications

Abstract

Design problems in industrial engineering often involve a large number of design variables with multiple objectives, under complex nonlinear constraints. The algorithms for multiobjective problems can be significantly different from the methods for single objective optimization. To find the Pareto front and non-dominated set for a nonlinear multiobjective optimization problem may require significant computing effort, even for seemingly simple problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we extend the recently developed firefly algorithm to solve multiobjective optimization problems. We validate the proposed approach using a selected subset of test functions and then apply it to solve design optimization benchmarks. We will discuss our results and provide topics for further research.