Multi-objective beetle antennae search algorithm
This work provides an incremental improvement for engineers and researchers dealing with complex optimization problems, offering a more efficient algorithm for multi-objective scenarios.
The authors tackled the problem of solving multi-objective optimization problems with high computational demands by extending the beetle antennae search algorithm to handle multiple objectives, achieving higher computational efficiency with satisfactory accuracy in benchmark tests.
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.