基于自然群的Optimization算法的探索增强
Exploration Enhancement of Nature-Inspired Swarm-based Optimization Algorithms
摘要：
受自然启发的基于群体的算法已经被广泛应用于解决许多学科中的高维和复杂的最优化问题。它们是通用的优化算法，易于使用和实现，灵活且无限制。这些算法的一个共同缺点是过早收敛，并且找到的解不是全局最优值。本文给出了算法几乎处处收敛的充分条件。达到全局最优值。然后，我们提出了一个通用的，简单而有效的策略，称为扰动投影（PP），以提高算法的探索能力，使我们的收敛条件得到保证。我们使用三种广泛使用的自然启发的基于群的优化算法：粒子群优化（PSO），蝙蝠算法（BAT）和竞争性群优化（CSO）来说明这种方法。大量的数值实验表明，增强PP策略的三个算法中的每一个都在许多显着的方式优于原始版本。
英文摘要：
Nature-inspired swarm-based algorithms have been widely applied to tackle high-dimensional and complex optimization problems across many disciplines. They are general purpose optimization algorithms, easy to use and implement, flexible and assumption-free. A common drawback of these algorithms is premature convergence and the solution found is not a global optimum. We provide sufficient conditions for an algorithm to converge almost surely (a.s.) to a global optimum. We then propose a general, simple and effective strategy, called Perturbation-Projection (PP), to enhance an algorithm's exploration capability so that our convergence conditions are guaranteed to hold. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and competitive swarm optimizer (CSO). Extensive numerical experiments show that each of the three algorithms with the enhanced PP strategy outperforms the original version in a number of notable ways.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
