Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms  

作　　者：Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall 

机构地区：[1]Faculty of Information Science and Technology,Universiti Kebangsaan Malaysia,Bangi,43600,Selangor,Malaysia [2]Center for Artificial Intelligence Technology,Universiti Kebangsaan Malaysia,Bangi,43600,Selangor,Malaysia [3]Faculty of Medicine,Universiti Kebangsaan Malaysia Medical Centre,Cheras,56000,Kuala Lumpur,Malaysia [4]School of Engineering and Computing,MILA University,Nilai,71800,Negeri Sembilan,Malaysia [5]School of Computer Science,University of Nottingham-Malaysia Campus,Semenyih,43500,Selangor,Malaysia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第2期1233-1288,共56页工程与科学中的计算机建模(英文)

基　　金：funded by Ministry of Higher Education(MoHE)Malaysia,under Transdisciplinary Research Grant Scheme(TRGS/1/2019/UKM/01/4/2).

摘　　要：The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process.Numerous selection hyper-heuristics have different imple-mentation strategies.However,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different components.The question is how to effectively employ them to produce an efficient search heuristic.Furthermore,the algorithms that competed in the inaugural CHeSC have not been collectively reviewed.This work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is presented.The analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter control.This review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these components.The effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic parameters.Findings are also compared with recent trends in hyper-heuristics.This work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关 键 词：HYPER-HEURISTICS search algorithms optimization heuristic selection move acceptance learning DIVERSIFICATION parameter control 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]