Unknown area exploration for robots with energy constraints using a modified Butterfly Optimization Algorithm
This work addresses the problem of efficient robot exploration in unknown environments with limited energy, but it is incremental as it builds on existing metaheuristics.
The paper tackled the problem of unknown area exploration for robots with energy constraints by proposing a modified Butterfly Optimization Algorithm (xBOA) and a benchmarking framework for metaheuristics. The results showed that xBOA improved robustness to local optima, fitness convergence, and exploration rates compared to the original BOA and other variants, with BOA being a good alternative in terms of exploration time.
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.