Appropriate noise addition to metaheuristic algorithms can enhance their performance.

ABSTRACT: 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

SUBMITTER: Choi KP 

PROVIDER: S-EPMC10066303 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature