# Multiobjective Firefly Algorithm for Continuous Optimization

arXiv: https://arxiv.org/abs/1303.6336 · HTML: https://mlsanity.com/paper/1303.6336/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2013.

## Built on by (most cited first, 4 of 4)

- [Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints](https://mlsanity.com/paper/2501.03261/) (2025)
- [Nature-Inspired Optimization Algorithms: Research Direction and Survey](https://mlsanity.com/paper/2102.04013/) (2021)
- [A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs](https://mlsanity.com/paper/2506.04238/) (2025)
- [When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization](https://mlsanity.com/paper/2605.18460/) (2026)

## Abstract (arXiv)

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.