Unknown area exploration for robots with energy constraints using a modified Butterfly Optimization Algorithm

TL;DR

This paper introduces a modified Butterfly Optimization Algorithm (xBOA) with a crossover operator, and a framework for energy-constrained unknown area exploration in robotics, benchmarking metaheuristics in single- and multi-robot scenarios.

Contribution

It proposes a new xBOA algorithm and a comprehensive framework for evaluating metaheuristics in robotic exploration with energy constraints.

Findings

xBOA shows better convergence and exploration rates than BOA.

BOA is efficient in exploration time.

xBOA is more robust to local optima.

Abstract

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with wellknown metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants.