A novel framework of hyper-heuristic algorithm was proposed to improve the adaption of evolutionary algorithms( EAs)in optimization. The algorithm could be changed during the evolutionary progress according to their p...A novel framework of hyper-heuristic algorithm was proposed to improve the adaption of evolutionary algorithms( EAs)in optimization. The algorithm could be changed during the evolutionary progress according to their performances. In addition,a large number of elite individuals were employed in the algorithm and the elite individuals helped algorithm achieve a better performance,while such number of elite individuals stagnated the global convergence in conventional single algorithm. The time complexity was analyzed to demonstrate the novel framework did not increase the time complexity. The simulation results indicate that the proposed framework outperforms any single algorithm that composes the framework.展开更多
The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the diff...The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.展开更多
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 dynamic...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.展开更多
Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative met...Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative methods.In hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.Design/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems.First,four graph coloring heuristics are employed to construct heuristic lists.Within the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.Originality/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.展开更多
A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuri...A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuristic and a set of low-level heuristics.The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic.In this study,a Cooperative Multi-Stage Hyper-Heuristic(CMS-HH)algorithm is proposed to address certain combinatorial optimization problems.In the CMS-HH,a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution.In the search phase,an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution.In addition,a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time.The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems,including Boolean satisfiability problems,one-dimensional packing problems,permutation flow-shop scheduling problems,personnel scheduling problems,traveling salesman problems,and vehicle routing problems.The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.展开更多
基金National Natural Science Foundations of China(Nos.70871091,61075064,61034004,61005090)Program for New Century Excellent Talents in University of Ministry of Education of ChinaPh.D.Programs Foundation of Ministry of Education of China(No.20100072110038)
文摘A novel framework of hyper-heuristic algorithm was proposed to improve the adaption of evolutionary algorithms( EAs)in optimization. The algorithm could be changed during the evolutionary progress according to their performances. In addition,a large number of elite individuals were employed in the algorithm and the elite individuals helped algorithm achieve a better performance,while such number of elite individuals stagnated the global convergence in conventional single algorithm. The time complexity was analyzed to demonstrate the novel framework did not increase the time complexity. The simulation results indicate that the proposed framework outperforms any single algorithm that composes the framework.
基金supported in part by the National Key R&D Program of China under Grant 2017YFB1302400the Jinan“20 New Colleges and Universities”Funded Scientific Research Leader Studio under Grant 2021GXRC079+2 种基金the Major Agricultural Applied Technological Innovation Projects of Shandong Province underGrant SD2019NJ014the Shandong Natural Science Foundation under Grant ZR2019MF064the Beijing Advanced Innovation Center for Intelligent Robots and Systems under Grant 2019IRS19.
文摘The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.
基金funded by Ministry of Higher Education(MoHE)Malaysia,under Transdisciplinary Research Grant Scheme(TRGS/1/2019/UKM/01/4/2).
文摘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.
文摘Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative methods.In hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.Design/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems.First,four graph coloring heuristics are employed to construct heuristic lists.Within the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.Originality/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.
基金supported by the National Key Research and Development Plan(No.2020YFB1713600)the National Natural Science Foundation of China(No.62063021)+2 种基金the Lanzhou Science Bureau Project(No.2018-rc-98)Public Welfare Project of Zhejiang Natural Science Foundation(No.LGJ19E050001)Project of Zhejiang Natural Science Foundation(No.LQ20F020011).
文摘A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems.A classical hyper-heuristic framework consists of two levels,including the high-level heuristic and a set of low-level heuristics.The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic.In this study,a Cooperative Multi-Stage Hyper-Heuristic(CMS-HH)algorithm is proposed to address certain combinatorial optimization problems.In the CMS-HH,a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution.In the search phase,an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution.In addition,a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time.The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems,including Boolean satisfiability problems,one-dimensional packing problems,permutation flow-shop scheduling problems,personnel scheduling problems,traveling salesman problems,and vehicle routing problems.The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.
