Multiobjective Lévy-Flight Firefly Algorithm for Multiobjective Optimization

The firefly algorithm (FA) was firstly proposed during 2008–2009 as one of the powerful population-based metaheuristic optimization techniques for solving continuous and combinatorial optimization problems. The FA has been proved and applied to various real-world problems in mostly single objective optimization manner. However, many real-world problems are typically formulated as the multiobjective optimization problems with complex constraints. In this paper, the multiobjective Levy-flight firefly algorithm (mLFFA) is developed for multiobjective optimization. The proposed mLFFA is validated against four standard multiobjective test functions to perform its effectiveness. The simulation results show that the proposed mLFFA algorithm is more efficient than the well-known algorithms from literature reviews including the vector evaluated genetic algorithm (VEGA), non-dominated sorting genetic algorithm II (NSGA-II), differential evolution for multiobjective optimization (DEMO) and multiobjective multipath adaptive tabu search (mMATS).