本文介绍了一种较为新颖的优化算法——拔河优化算法(tug of war optimization,TWO)[1].该算法属于自然启发式、基于群体的元启发式算法.利用体育隐喻,将每个候选解视为参与一系列拔河比赛的团队.这些团队根据它们所代表的解的质量对彼此施加拉力.竞争的团队根据牛顿力学的运动规律移动到新的位置.与许多其他元启发式方法不同,该算法考虑了相互作用团队的质量.TWO适用于全局优化问题,包括不连续、多峰、非光滑和非凸函数.并在本文中与PSO、SA等其它算法进行了对比验证,实验结果表明TWO具有较高的可靠性与搜索速度.
Tug of War Optimization
This article introduces a relatively novel optimization algorithm-tug of war optimization(TWO)[1].This algorithm belongs to natural heuristic and population-based metaheuristic algorithms.Using sports metaphors,treat each candidate solution as a team participating in a series of tug of war matches.These teams apply tension to each other based on the quality of the solutions they represent.The competing team moved to a new position based on the laws of motion in Newtonian mechanics.Unlike many other meta-heuristic methods,this algorithm takes into account the quality of the interacting team.TWO is suitable for global optimization problems,including discontinuous,multimodal,non smooth,and non convex functions.And compared with other algorithms such as PSO and SA in this article,the experimental results show that TWO has high reliability and search speed.
tug of war optimizationheuristic algorithmsglobal optimizationtug of war competition
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