Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

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.

Design for a Novel Framework of Hyper-Heuristic Algorithm

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.

A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

Maximizing the lifetime of wireless sensor networks(WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks,are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

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.

Binary Particle Swarm Optimization Based Hyper-Heuristic for Solving the Set-Union Knapsack Problem

The set-union knapsack problem(SUKP)is proved to be a strongly NP-hard problem,and it is an extension of the classic NP-hard problem:the 0-1 knapsack problem(KP).Solving the SUKP through exact approaches is computationally expensive.Therefore,several swarm intelligent algorithms have been proposed in order to solve the SUKP.Hyper-heuristics have received notable attention by researchers in recent years,and they are successfully applied to solve the combinatorial optimization problems.In this article,we propose a binary particle swarm optimization(BPSO)based hyper-heuristic for solving the SUKP,in which the BPSO is employed as a search methodology.The proposed approach has been evaluated on three sets of SUKP instances.The results are compared with 6 approaches:BABC,EMS,gPSO,DHJaya,b WSA,and HBPSO/TS,and demonstrate that the proposed approach for the SUKP outperforms other approaches.

A memetic algorithm based on hyper-heuristics for examination timetabling problems

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.