PHC-NSGA-II: A Novel Multi-objective Memetic Algorithm for Continuous Optimization
Slim Bechikh, Nabil Belgasmi, Lamjed Ben Saïd, Khaled Ghédira · 2008
We introduce in this paper a new multi-objective memetic algorithm. This algorithm is a result of hybridization of the NSGA-II algorithm with a new designed local search procedure that we named Pareto Hill Climbing. Verification of our novel algorithm is carried out by testing it on two sets of multi-objective test problems and comparing it to other multi-objective evolutionary algorithms (MOEAs) and other multi-criterion memetic algorithms (MMAs). Simulation results show the algorithm ability in tackling continuous multi-objective problems in terms of convergence and diversity. Our hybrid algorithm (1) outperforms pure MOEAs, (2) is competent with other gradient based MMAs, and (3) can solve non differentiable problems.