Appropriate noise addition to metaheuristic algorithms can enhance their performance

Kwok Pui Choi, Enzio Hai Hong Kam, Xin T. Tong, Weng Kee Wong

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號5291
期刊Scientific reports
13
發行號1
DOIs
出版狀態Published - 2023 12月

All Science Journal Classification (ASJC) codes

  • 多學科

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