  In engineering optimization problems, multiple objectives with a large number
of variables under highly nonlinear constraints are usually required to be
simultaneously optimized. Significant computing effort are required to find the
Pareto front of a nonlinear multi-objective optimization problem. Swarm
intelligence based metaheuristic algorithms have been successfully applied to
solve multi-objective optimization problems. Recently, an individual
intelligence based algorithm called beetle antennae search algorithm was
proposed. This algorithm was proved to be more computationally efficient.
Therefore, we extended this algorithm to solve multi-objective optimization
problems. The proposed multi-objective beetle antennae search algorithm is
tested using four well-selected benchmark functions and its performance is
compared with other multi-objective optimization algorithms. The results show
that the proposed multi-objective beetle antennae search algorithm has higher
computational efficiency with satisfactory accuracy.
