论文标题
基准测试荟萃分析优化
Benchmarking Meta-heuristic Optimization
论文作者
论文摘要
在任何域中解决优化任务都是一个非常具有挑战性的问题，尤其是在处理非线性问题和非凸功能时。在求解非线性函数时，许多荟萃算法非常有效。荟萃算法是一种与问题无关的技术，可以应用于广泛的问题。在此实验中，将对某些进化算法进行测试，评估和对彼此进行比较。我们将通过遗传算法\，差异进化，粒子群优化算法，灰狼优化器和模拟退火。从许多角度来看，将对它们进行评估，例如算法在整个世代的性能以及算法的结果如何接近最佳结果。在后面的部分中，深入讨论了其他评估点。
Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm\, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm's result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.
