The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.

中文翻译：

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HSWOA：用于全局优化的饥饿游戏搜索和鲸鱼优化算法的集合

在饥饿的刺激下寻找食物是动物界的普遍现象。模仿这个概念，最近，已经提出了一种优化算法饥饿游戏搜索（HGS）用于全局优化。另一方面，鲸鱼优化算法 (WOA) 是一种常用的受自然启发的算法，其结构简单，参数简单，模仿座头鲸的狩猎行为。然而，由于对搜索空间的探索最少，WOA 很有可能陷入局部解，更多的利用导致它过早收敛。HGS 的饥饿概念与鲸鱼的食物搜索技术相结合，以减少 WOA 的固有缺点。使用各自的饥饿水平来平衡搜索策略，为每条鲸鱼自适应地设计了两个 HGS 权重。所提出的基于饥饿搜索的鲸鱼优化算法 (HSWOA) 的性能验证是通过将其与 10 种最先进的算法进行比较来完成的，其中包括针对 30 个经典基准函数的三种最近开发的算法。使用 IEEE CEC 2019 函数集与一些基本算法、最近修改的算法和 WOA 变体进行比较。所提出算法的统计性能通过弗里德曼检验、箱线图分析和 Nemenyi 多重比较检验进行了验证。通过复杂度分析和收敛性分析来确定和测试算法的运行速度。最后，解决了七个现实世界的工程问题，并与元启发式算法列表进行了比较。与最先进算法的数值和统计性能比较证实了新设计算法的有效性。