Beetle Antennae Search without Parameter Tuning (BAS-WPT) for Multi-objective Optimization

TL;DR

This paper introduces BAS-WPT, a parameter-free variant of the Beetle Antennae Search algorithm, capable of solving multi-objective constrained optimization problems effectively.

Contribution

It presents a novel, parameter-tuning free version of BAS that handles multi-objective and constrained problems using normalization and penalty functions.

Findings

Effective in solving multi-objective constrained optimization problems

Outperforms traditional BAS with parameter tuning in experiments

Simplifies algorithm design by removing tuning parameters

Abstract

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.