Saadi Younes, Yanto Iwan Tri Riyadi, Herawan Tutut, Balakrishnan Vimala, Chiroma Haruna, Risnumawan Anhar
Department of Information Systems, University of Malaya, 50603 Pantai Valley, Kuala Lumpur, Malaysia.
Department of Computer Science, University of Ahmad Dahlan, Jalan Kapas n 9, Yogyakarta, 55165, Indonesia.
PLoS One. 2016 Jan 20;11(1):e0144371. doi: 10.1371/journal.pone.0144371. eCollection 2016.
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems.
一种用于全局优化的元启发式算法的效率基于其搜索和找到全局最优解的能力。然而，良好的搜索通常需要在搜索空间的探索和利用之间取得平衡。本文介绍了一种名为环斑海豹搜索（RSS）的新元启发式算法。它受到海豹幼崽自然行为的启发。该算法模仿海豹幼崽的移动行为及其搜索和选择最佳巢穴以躲避捕食者的能力。场景始于海豹母亲在为此目的建造的分娩巢穴中产下一只新幼崽。海豹幼崽的策略包括通过随机游走寻找新巢穴来搜索和选择最佳巢穴。受海豹对捕食者发出的外部噪声敏感特性的影响，海豹幼崽的随机游走有两种不同的搜索状态，正常状态和紧急状态。在正常状态下，幼崽在相邻很近的巢穴之间进行密集搜索；这种移动通过布朗运动建模。在紧急状态下，幼崽离开附近区域，从稀疏目标中进行广泛搜索以找到新巢穴；这种移动通过莱维飞行建模。这两种状态之间的切换由捕食者发出的随机噪声实现。该算法在正常状态和紧急状态之间不断切换，直到达到全局最优解。使用十五个基准测试函数进行了测试和验证，以比较RSS与其他基线算法的性能。结果表明，在收敛到全局最优解的速度方面，RSS比遗传算法、粒子群优化和布谷鸟搜索更有效。RSS在搜索空间的探索（广泛）和利用（密集）之间的平衡方面有所改进。RSS可以有效地模仿海豹幼崽的行为来找到最佳巢穴，并提供一种用于全局优化问题的新算法。
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