Beetle Antennae Search without Parameter Tuning (BAS-WPT) for Multi-objective Optimization
This is an incremental improvement for researchers in meta-heuristic optimization algorithms.
The authors tackled the problem of parameter tuning and single-objective limitation in Beetle Antennae Search by developing a variant that eliminates tuning parameters and handles multi-objective optimization, with experimental results showing efficacy in constraint handling.
Beetle antennae search (BAS) is an efficient meta-heuristic algorithm inspired by foraging behaviors of beetles. This algorithm includes several parameters for tuning and the existing results are limited to solve single objective optimization. This work pushes forward the research on BAS by providing one variant that releases the tuning parameters and is able to handle multi-objective optimization. This new approach applies normalization to simplify the original algorithm and uses a penalty function to exploit infeasible solutions with low constraint violation to solve the constraint optimization problem. Extensive experimental studies are carried out and the results reveal efficacy of the proposed approach to constraint handling.