A New Heuristic Optimization: Search and Rescue Algorithm and Solving the Function Optimization Problems

M. Ozdemir
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Abstract

Heuristic techniques are optimization methods that inspired by nature. Although there are many heuristics in the literature, a new heuristic technique is presented by researchers every day by observing nature-based or living behaviors in nature. In this study, a new heuristic optimization technique inspired by human behavior is proposed. In order to prove the validity of this method called Search and Rescue Optimization Algorithm (AKOA), the technique applied to find the global minimums of function optimization test problems in the literature. As a result of the experiments performed on 21 minimization problems, it has been observed that AKOA is quite competitive when compared to Dynamic Random Search Technique and Random Selection Walk Technique.
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一种新的启发式优化:搜索与救援算法及求解函数优化问题
启发式技术是一种受自然启发的优化方法。虽然文献中有许多启发式方法,但每天都有研究人员通过观察自然界中基于自然或生活的行为来提出一种新的启发式技术。本研究提出了一种受人类行为启发的启发式优化技术。为了证明这种被称为搜索和救援优化算法(AKOA)的方法的有效性,该技术应用于寻找文献中函数优化测试问题的全局最小值。通过对21个最小化问题的实验,我们观察到,与动态随机搜索技术和随机选择行走技术相比,AKOA具有很强的竞争力。
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