Multiobjective firefly algorithm for continuous optimization

By Xin-She Yang. Engineering computations, 2012

527 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e512709f4ad7c46e8ed36a48493385da6266a422

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Iztok Fister
2. Janez Brest
3. Tansel Dökeroğlu
4. Yongquan Zhou
5. Qifang Luo
6. Ying-Biao Ling
7. Ahmet Cosar
8. Tayfun Kucukyilmaz

In Management Science and Operations Research

1. Qifang Luo
2. Yongquan Zhou
3. Ying-Biao Ling
4. Ahmet Cosar
5. Tansel Dökeroğlu
6. Tayfun Kucukyilmaz
7. Xin-She Yang
8. Ender Sevinç

In Statistics and Probability

1. Qifang Luo
2. Yongquan Zhou
3. Ying-Biao Ling
4. Xin-She Yang
5. Xingshi He
6. Arpan Kumar Kar
7. Mehmet Karamanoglu

Citations in the corpus

• Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization (2017, methodology): “In addition to these two algorithms, some of the latest effective algorithms are the bat algorithm (BA) [3], which simulates the echolocation behavior of bats, grey wolf optimization (GWO) [4], which mimics the behavior of wolves, flower pollination algorithm (FPA) [5], which simulates flower pollin”
• A comprehensive review of firefly algorithms (2013, background): “Yang in [100] formulated a new multi-objective FA for multi-objective optimization that extended FA for producing Pareto optimal front directly.”
• A survey on new generation metaheuristic algorithms (2019, background): “Yang (2013b) provides detailed theory and application information in his book about CSA and FA.”