Stochastic Approximation and Brownian Repulsion based Evolutionary Search

作     者：Dutta, Rajdeep Varma, T. Venkatesh Sarkar, Saikat Mamajiwala, Mariya Awad, Noor Jayavelu, Senthilnath Roy, Debasish 

作者机构： 138632 Singapore Faculty of Mechanical Engineering Technion–Israel Institute of Technology Haifa32000 Israel Department of Civil Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi110016 India Department of Computer Science University of Sheffield United Kingdom Machine Learning Lab University of Freiburg Germany Centre of Excellence in Advanced Mechanics of Materials Indian Institute of Science Bangalore India 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Optimization algorithms 

摘      要：Many global optimization algorithms of the memetic variety rely on some form of stochastic search, and yet they often lack a sound probabilistic basis. Without a recourse to the powerful tools of stochastic calculus, treading the fine balance between exploration and exploitation could be tricky. In this work, we propose an evolutionary algorithm (EA) comprising two types of additive updates. The primary update utilizes stochastic approximation to guide a population of randomly initialized particles towards an optimum. We incorporate derivative-free Robbins-Monro type gains in the first update so as to provide a directional guidance to the candidate solutions. The secondary update leverages stochastic conditioning to apply random perturbations for a controlled yet efficient exploration. Specifically, conceptualized on a change of measures, the perturbation strategy discourages two or more trajectories exploring the same region of the search space. Our optimization algorithm, dubbed as SABRES (Stochastic Approximation and Brownian Repulsion based Evolutionary Search), is applied to CEC-2022 benchmark functions on global optimization. Numerical results are indicative of the potentialities of SABRES in solving a variety of challenging multi-modal, non-separable, and asymmetrical benchmark functions. © 2024, CC BY-NC-ND.