Department of Statistics and Data Science, National University of Singapore, Singapore, 117546, Singapore.
Department of Computer Science, National University of Singapore, Singapore, 117417, Singapore.
Sci Rep. 2023 Mar 31;13(1):5291. doi: 10.1038/s41598-023-29618-5.
Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some common drawbacks of these algorithms are their premature convergence and the solution found may not be a global optimum. We propose a general, simple and effective strategy, called heterogeneous Perturbation-Projection (HPP), to enhance an algorithm's exploration capability so that our sufficient convergence conditions are guaranteed to hold and the algorithm converges almost surely to a global optimum. In summary, HPP applies stochastic perturbation on half of the swarm agents and then project all agents onto the set of feasible solutions. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and Ant Colony Optimization for continuous domains (ACO). Extensive numerical experiments show that the three algorithms with the HPP strategy outperform the original versions with 60-80% the times with significant margins.
受自然启发的群体算法越来越多地被应用于解决跨学科的高维复杂优化问题。它们是通用的优化算法，易于实现且无需假设。这些算法的一些常见缺点是过早收敛，并且找到的解决方案可能不是全局最优解。我们提出了一种通用、简单且有效的策略，称为异构扰动-投影（HPP），以增强算法的探索能力，从而保证我们的充分收敛条件成立，并且算法几乎肯定会收敛到全局最优解。总的来说，HPP 对一半的群体智能体进行随机扰动，然后将所有智能体投影到可行解集上。我们使用三种广泛使用的基于自然启发的群体优化算法：粒子群优化（PSO）、蝙蝠算法（BAT）和蚁群优化（ACO）来说明这种方法。大量的数值实验表明，HPP 策略下的三种算法比原始算法快 60-80%，具有显著的优势。
