LAUSR

Cuckoo search (CS) is a recently developed meta-heuristic, which has shown good search abilities on many optimization problems. In this paper, we present a hybrid multi-objective CS (HMOCS) for solving multi-objective optimization problems (MOPs). The HMOCS employs the non-dominated sorting procedure and a dynamical local search. The former is helpful to generate Pareto fronts, and the latter focuses on enhance the local search. In order to verify the performance of our approach HMOCS, six well-known benchmark MOPs were used in the experiments. Simulation results show that HMOCS outperforms three other multi-objective algorithms in terms of convergence, spread and distributions.