Agushaka Jeffrey O, Ezugwu Absalom E, Saha Apu K, Pal Jayanta, Abualigah Laith, Mirjalili Seyedali
Department of Computer Science, Federal University of Lafia, Lafia 950101, Nigeria.
Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom, 2520, South Africa.
Heliyon. 2024 May 23;10(11):e31629. doi: 10.1016/j.heliyon.2024.e31629. eCollection 2024 Jun 15.
This paper introduces a new metaheuristic technique known as the Greater Cane Rat Algorithm (GCRA) for addressing optimization problems. The optimization process of GCRA is inspired by the intelligent foraging behaviors of greater cane rats during and off mating season. Being highly nocturnal, they are intelligible enough to leave trails as they forage through reeds and grass. Such trails would subsequently lead to food and water sources and shelter. The exploration phase is achieved when they leave the different shelters scattered around their territory to forage and leave trails. It is presumed that the alpha male maintains knowledge about these routes, and as a result, other rats modify their location according to this information. Also, the males are aware of the breeding season and separate themselves from the group. The assumption is that once the group is separated during this season, the foraging activities are concentrated within areas of abundant food sources, which aids the exploitation. Hence, the smart foraging paths and behaviors during the mating season are mathematically represented to realize the design of the GCR algorithm and carry out the optimization tasks. The performance of GCRA is tested using twenty-two classical benchmark functions, ten CEC 2020 complex functions, and the CEC 2011 real-world continuous benchmark problems. To further test the performance of the proposed algorithm, six classic problems in the engineering domain were used. Furthermore, a thorough analysis of computational and convergence results is presented to shed light on the efficacy and stability levels of GCRA. The statistical significance of the results is compared with ten state-of-the-art algorithms using Friedman's and Wilcoxon's signed rank tests. These findings show that GCRA produced optimal or nearly optimal solutions and evaded the trap of local minima, distinguishing it from the rival optimization algorithms employed to tackle similar problems. The GCRA optimizer source code is publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/165241-greater-cane-rat-algorithm-gcra.
本文介绍了一种新的元启发式技术——大蔗鼠算法(GCRA),用于解决优化问题。GCRA的优化过程受到大蔗鼠在交配季节和非交配季节智能觅食行为的启发。作为高度夜行性动物,它们在穿过芦苇和草丛觅食时足够聪明地留下踪迹。这些踪迹随后会通向食物、水源和庇护所。当它们离开分布在领地周围的不同庇护所去觅食并留下踪迹时,就实现了探索阶段。据推测,优势雄性大蔗鼠掌握这些路线的信息,因此其他大蔗鼠会根据此信息改变自己的位置。此外,雄性大蔗鼠知道繁殖季节并与群体分开。假设在这个季节群体一旦分开,觅食活动就集中在食物丰富的区域,这有助于开发利用。因此,对交配季节智能觅食路径和行为进行数学表示,以实现GCR算法的设计并执行优化任务。使用22个经典基准函数、10个CEC 2020复杂函数以及CEC 2011实际连续基准问题对GCRA的性能进行了测试。为了进一步测试所提出算法的性能,使用了工程领域的六个经典问题。此外,还对计算结果和收敛结果进行了深入分析,以阐明GCRA的有效性和稳定性水平。使用Friedman检验和Wilcoxon符号秩检验将结果的统计显著性与十种先进算法进行了比较。这些结果表明,GCRA产生了最优或接近最优的解,并且避开了局部最小值陷阱,这使其有别于用于解决类似问题的其他竞争优化算法。GCRA优化器的源代码可在以下网址公开获取:https://www.mathworks.com/matlabcentral/fileexchange/165241 - greater - cane - rat - algorithm - gcra 。