Lin Yaoyao, Heidari Ali Asghar, Wang Shuihua, Chen Huiling, Zhang Yudong
Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.
Biomimetics (Basel). 2023 Sep 20;8(5):441. doi: 10.3390/biomimetics8050441.
The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm's exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.
饥饿游戏搜索算法（HGS）是一种创新的优化器，它不依赖梯度进行操作，而是采用基于种群的方法。它的灵感来源于在自然栖息地中观察到的群居动物的协作觅食活动。然而，尽管HGS有显著优势，但也存在局限性，包括多样性不足、早熟收敛以及易陷入局部最优。为了克服这些挑战，本研究引入了两种调整策略来改进原始的HGS算法。第一种自适应策略将对数螺旋（LS）技术与基于对立学习（OBL）相结合，形成了LS - OBL方法。该策略在缩小搜索空间和保持HGS种群多样性方面发挥着关键作用，有效增强了算法的探索能力。第二种自适应策略是动态罗森布罗克方法（RM），它通过调整搜索方向和步长对HGS有所贡献。这种调整使HGS能够逃离次优解并提高其收敛精度。这两种策略相结合，形成了本研究提出的改进算法，即RLHGS。为了评估所引入策略的有效性，设计了特定实验来评估LS - OBL和RM对提升HGS性能的影响。实验结果明确表明，整合这两种策略显著增强了HGS的能力。此外，使用23个成熟的基准函数和CEC2020测试套件将RLHGS与八种先进算法进行了比较。实验结果一致表明，RLHGS优于其他算法，在两个测试套件中均位居榜首。这一有力证据证实了RLHGS相对于其他同类算法具有卓越的功能和性能。此外，RLHGS被应用于解决四个实际的约束工程优化问题。最终结果强调了RLHGS在解决此类问题方面的有效性，进一步支持了其作为一种高效优化方法的价值。
