A non-dominated sorting firefly algorithm for multi-objective optimization
Chun‐Wei Tsai, Yao‐Ting Huang, Ming‐Chao Chiang · 2014
The so-called multi-objective optimization problem (MOP) has become a critical research area because many MOPs exist in our daily life and solutions to these problems may strongly impact the performance of systems we use. Unlike solving a single-objective problem, solving a MOP requires that many conflicting objectives be optimized altogether at the same time. Since most MOPs are NP-hard, how to find an approximate solution using a limited computation resource has become an active research topic in recent years. In this paper, we present a high-performance algorithm for solving the MOP that leverages the strengths of firefly algorithm and non-dominated sorting genetic algorithm II (NSGA-II). To evaluate the performance of the proposed algorithm, we apply it to several MOPs. Simulation results show that the proposed algorithm can essentially provide a better result than all the state-of-the-art multi-objective optimization algorithms compared in this paper in most cases.