A new hyper-heuristic as a general problem solver: an implementation in HyFlex

This study provides a new hyper-heuristic design using a learning-based heuristic selection mechanism together with an adaptive move acceptance criterion. The selection process was supported by an online heuristic subset selection strategy. In addition, a pairwise heuristic hybridization method was designed. The motivation behind building an intelligent selection hyper-heuristic using these adaptive hyper-heuristic sub-mechanisms is to facilitate generality. Therefore, the designed hyper-heuristic was tested on a number of problem domains defined in a high-level framework, i.e., HyFlex. The framework provides a set of problems with a number of instances as well as a group of low-level heuristics. Thus, it can be considered a good environment to measure the generality level of selection hyper-heuristics. The computational results demonstrated the generic performance of the proposed strategy in comparison with other tested hyper-heuristics composed of the sub-mechanisms from the literature. Moreover, the performance and behavior analysis conducted for the hyper-heuristic clearly showed its adaptive characteristics under different search conditions. The principles comprising the here presented algorithm were at the heart of the algorithm that won the first international cross-domain heuristic search competition.     

超启发式遗传算法求解模糊柔性作业车间调度

本文提出一种混合超启发式遗传算法(HHGA),用于求解一类采用三角模糊数表示工件加工时间的模糊柔性作业车间调度问题(FFJSP),优化目标为最小化最大模糊完工时间(即makespan).首先,详细分析现有三角模糊数排序准则性质,并充分考虑取大操作的近似误差和模糊度,设计一种更为准确的三角模糊数排序准则,可合理计算FFJSP和其他各类调度问题解的目标函数值.其次,为实现对FFJSP解空间不同区域的有效搜索,HHGA将求解过程分为两层,高层利用带自适应变异算子的遗传算法对6种特定操作(即6种有效邻域操作)的排列进行优化;低层将高层所得的每种排列作为一种启发式算法,用于对低层相应个体进行操作来执行紧凑的变邻域局部搜索并生成新个体,同时加入模拟退火机制来避免搜索陷入局部极小.最后,仿真实验和算法比较验证了所提排序准则和HHGA的有效性.     

A multi-objective hyper-heuristic based on choice function

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM.     

Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported that hyper heuristics are well applied in combinatorial optimisation problems. As a classic combinatorial optimisation problem, the row layout problem has not been publicly reported on applying hyper heuristics to its various sub-problems. To fill this gap, this study proposes a parallel hyper-heuristic approach based on reinforcement learning for corridor allocation problems and parallel row ordering problems. For the proposed algorithm, an outer layer parallel computing framework was constructed based on the encoding of the problem. The simulated annealing, tabu search, and variable neighbourhood algorithms were used in the algorithm as low-level heuristic operations, and Q-learning in reinforcement learning was used as a high-level strategy. A state space containing sequences and fitness values was designed. The algorithm performance was then evaluated for benchmark instances of the corridor allocation problem (37 groups) and parallel row ordering problem (80 groups). The results showed that, in most cases, the proposed algorithm provided a better solution than the best-known solutions in the literature. Finally, the meta-heuristic algorithm applied to three low-level heuristic operations is taken as three independent algorithms and compared with the proposed hyper-heuristic algorithm on four groups of parallel row ordering problem instances. The effectiveness of Q-learning in selection is illustrated by analysing the comparison results of the four algorithms and the number of calls of the three low-level heuristic operations in the proposed method.     

一种基于动态决策块的超启发式跨单元调度方法

对运输能力受限条件下的跨单元调度问题进行分析, 提出一种基于动态决策块和蚁群优化 (Ant colony optimization, ACO) 的超启发式方法, 同时解决跨单元生产调度和运输调度问题. 在传统超启发式方法的基础上, 采用动态决策块策略, 通过蚁群算法合理划分决策块, 并为决策块选择合适的规则. 实验表明, 采用动态决策块策略的超启发式方法比传统的超启发式方法具有更好的性能, 本文所提的方法在最小化加权延迟总和目标方面有较好的优化能力 并且具有较高的计算效率.     

A greedy gradient-simulated annealing selection hyper-heuristic

Educational timetabling problem is a challenging real world problem which has been of interest to many researchers and practitioners. There are many variants of this problem which mainly require scheduling of events and resources under various constraints. In this study, a curriculum based course timetabling problem at Yeditepe University is described and an iterative selection hyper-heuristic is presented as a solution method. A selection hyper-heuristic as a high level methodology operates on the space formed by a fixed set of low level heuristics which operate directly on the space of solutions. The move acceptance and heuristic selection methods are the main components of a selection hyper-heuristic. The proposed hyper-heuristic in this study combines a simulated annealing move acceptance method with a learning heuristic selection method and manages a set of low level constraint oriented heuristics. A key goal in hyper-heuristic research is to build low cost methods which are general and can be reused on unseen problem instances as well as other problem domains desirably with no additional human expert intervention. Hence, the proposed method is additionally applied to a high school timetabling problem, as well as six other problem domains from a hyper-heuristic benchmark to test its level of generality. The empirical results show that our easy-to-implement hyper-heuristic is effective in solving the Yeditepe course timetabling problem. Moreover, being sufficiently general, it delivers a reasonable performance across different problem domains.     

超启发式交叉熵算法求解分布式装配柔性作业车间调度问题

本文针对一类新型两阶段分布式装配柔性作业车间调度问题(DAFJSP),建立问题模型,以最小化最大完工时间为优化目标并提出一种超启发式交叉熵算法(HHCEA)进行求解.首先,设计基于工序序列、工厂分配和产品序列的三维向量编码规则和结合贪婪策略的解码规则,同时提出4种启发式方法以提高初始解的质量.然后,设计高低分层结构的HHCEA,高层为提高对搜索方向的引导性,采用交叉熵算法(CEA)学习和积累优质排列的信息,其中各排列由结合问题特点设计的11种启发式操作(即11种有效的邻域操作)构成;低层为增加在解空间中的搜索深度,将高层确定的每个排列中的启发式操作依次重复执行指定次数并在执行过程中加入基于模拟退火的扰动机制,以此作为一种新的启发式方法执行搜索.最后,通过仿真实验与算法对比验证HHCEA可有效求解DAFJSP.     

Choice function based hyper-heuristics for multi-objective optimization

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic.     

Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated heuristics have an improved performance when compared against human-designed heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good heuristics. However, to generate more general heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved heuristics, and the evolutionary search strategy produced slightly better results.     

Solving constraint satisfaction and optimization problems by aneuro-fuzzy approach

The solution of constrained satisfaction and constrained optimization problems using a Hopfield model requires determination of the values of a certain number of coefficients linked to the surrounding conditions of the problem. It is quite difficult to determine these values, mainly because a heuristic search is necessary. This is not only time-consuming but may lead to solutions that are far from optimal, or even nonvalid ones. So far, there have been no works in literature offering a general method for the search for coefficents with will guarantee optimal or close to optimal solutions. This paper proposes a fuzzy approach which allows automatic determination of Hopfield coefficients.     

A hyper-heuristic based framework for dynamic optimization problems

Most of the real world problems have dynamic characteristics, where one or more elements of the underlying model for a given problem including the objective, constraints or even environmental parameters may change over time. Hyper-heuristics are problem-independent meta-heuristic techniques that are automating the process of selecting and generating multiple low-level heuristics to solve static combinatorial optimization problems. In this paper, we present a novel hybrid strategy for applicability of hyper-heuristic techniques on dynamic environments by integrating them with the memory/search algorithm. The memory/search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems. We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark. The former problem is extended from the generalized assignment problem by changing resource consumptions, capacity constraints and costs of jobs over time; and the latter one is a well-known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation performed on various instances of the given two problems validates that our hyper-heuristic integrated framework significantly outperforms the memory/search algorithm.     

超启发算法研究进展综述

超启发算法是一类新兴的优化方法,通过机器学习、算法选择、算法生成等技术求解组合优化等问题,具备跨问题领域求解的能力。针对超启发算法研究进展进行综述和讨论。首先,梳理超启发算法的定义、结构、特点和分类;其次,归纳选择式超启发算法和生成式超启发算法的研究进展及相关技术,包括选择低层启发式算法采用的学习方法,迭代计算中的移动接受策略,低层启发式算法的生成方法;最后,讨论现有超启发算法研究中存在的不足及未来的研究方向。     

Stochastic resource allocation using a predictor-based heuristic for optimization via simulation

Some combinatorial stochastic resource allocation problems lack algebraically defined objective functions and hence require optimization via simulation as a mechanism for obtaining good solutions. For this class of problems, we propose a new predictor-based heuristic that uses a distance criterion to perform the solution search. To demonstrate our solution approach, we apply this heuristic to the problem of selecting the proper design configuration of an unmanned aerial system (UAS) fleet so as to maximize mission effectiveness. We compare our approach to black box optimization via simulation approaches (two tabu search-based procedures and a greedy heuristic) and glean both methodological and practical insights.     

策略梯度的超启发算法求解带容量约束车辆路径问题

有容量车辆路径问题是组合优化问题中比较热门的问题, 它属于经典的NP-hard问题并且时间复杂度高.本文提出了一种基于策略梯度的超启发算法, 将强化学习中的确定性策略梯度算法引入到超启发算法的高层策略中的底层算法选择策略, 确定性策略梯度算法采用Actor-Critic框架, 另外为了能够在后续计算和神经网络参数更新中引用历史经验数据, 在确定性策略梯度算法中设计了经验池用于存储状态转移数据. 在超启发算法解的接受准则方面, 文中通过实验对比了3种接受准则的效果, 最终选择了自适应接受准则作为高层策略中解的接受准则. 通过对有容量车辆路径问题标准算例的计算, 并将求解结果与其他算法对比, 验证了所提算法在该问题求解上的有效性和稳定性.     

基于差分进化算法的动态环境经济电力系统调度优化

电力系统动态环境经济调度优化隶属于非线性优化问题范畴,并具有多目标、高维、多约束条件等特点。经 典的数学规划方法无法处理此类复杂问题。为此,提出了新的方法来解决这个问题。首先,通过代价惩罚因子将双目 标优化问题转化为单目标优化问题。然后,设计启发式搜索策略来解决调度问题中的爬坡约束、动态电力平衡约束。 采用启发式策略修正解决方案,能够提高群体的多样性,拓展搜索空间。基于优先列表的启发式策略能够使能耗低的 火力发电机拥有更高的优先级进行更多的电力输出,以得到更优的调度解决方案。最后,改进差分进化算法,以加快 搜索的速度并提高解决方案的质量。     

Embedded local search approaches for routing optimization

This paper presents a new class of heuristics which embed an exact algorithm within the framework of a local search heuristic. This approach was inspired by related heuristics which we developed for a practical problem arising in electronics manufacture. The basic idea of this heuristic is to break the original problem into small subproblems having similar properties to the original problem. These subproblems are then solved using time intensive heuristic approaches or exact algorithms and the solution is re-embedded into the original problem. The electronics manufacturing problem where we originally used the embedded local search approach, contains the Travelling Salesman Problem (TSP) as a major subproblem. In this paper we further develop our embedded search heuristic, HyperOpt, and investigate its performance for the TSP in comparison to other local search based approaches. We introduce an interesting hybrid of HyperOpt and 3-opt for asymmetric TSPs which proves more efficient than HyperOpt or 3-opt alone. Since pure local search seldom yields solutions of high quality we also investigate the performance of the approaches in an iterated local search framework. We examine iterated approaches of Large-Step Markov Chain and Variable Neighbourhood Search type and investigate their performance when used in combination with HyperOpt. We report extensive computational results to investigate the performance of our heuristic approaches for asymmetric and Euclidean Travelling Salesman Problems. While for the symmetric TSP our approaches yield solutions of comparable quality to 2-opt heuristic, the hybrid methods proposed for asymmetric problems seem capable of compensating for the time intensive embedded heuristic by finding tours of better average quality than iterated 3-opt in many less iterations and providing the best heuristic solutions known, for some instance classes.     

Coordinated selection of procurement bids in finite capacity environments

Pressure to increase agility and reduce costs is pushing enterprises to dynamically select among offers from a broader range of suppliers. This process is facilitated by the adoption of web services standards. An important requirement in this context is the ability to move away from unidimensional price-based e-procurement models and develop richer solutions that are capable of capturing other important attributes in the selection of supplier bids. Research on the evaluation and selection of supplier bids (“winner determination”) has traditionally ignored the temporal and finite capacity constraints under which manufacturers and service providers often operate. We consider the problem faced by a firm that procures multiple key components or services from a number of possible suppliers. Bids submitted by suppliers include both a price and a delivery date. The firm has to select a combination of supplier bids that will maximize its overall profit. Profit is determined by the revenue generated by the products (or services) sold by the firm, the costs of the components (or services) it acquires as well as late delivery penalties it incurs if it fails to deliver its products/services in time to its own customers. We provide a formal model of this important class of problems, discuss its complexity and introduce rules that can be used to efficiently prune the resulting search space. We proceed to show that our model can be characterized as a pseudo-early/tardy scheduling problem and use this observation to build an efficient heuristic search procedure. Computational results show that our heuristic procedure typically yields solutions that are within a few percent from the optimum. They further indicate that taking into account the manufacturer/service provider’s capacity can significantly improve its bottom line.     

基于BDD的增量启发式搜索

增量搜索是一种利用先前的搜索信息提高本次搜索效率的方法,通常可以用来解决动态环境下的重规划问题.在人工智能领域,一些实时系统常常需要根据外界环境的变化不断修正自身,这样就会产生一系列变化较小的相似问题,此时应用增量搜索将会非常有效.另外,基于BDD(binary decision diagram)的启发式搜索,结合了基于BDD的搜索和启发式搜索这两种方法的优点.它既用BDD这一紧凑的数据结构来表示系统的状态空间,又通过使用启发信息来进一步压缩搜索树的大小.在介绍基于BDD的启发式搜索和增量搜索之后,结合这两种方法给出了基于BDD的增量启发式搜索算法--BDDRPA*.大量的实验结果表明,BDDRPA*算法是非常有效的,它可以被广泛地应用到智能规划、移动机器人问题等领域中.     

Design of efficient packing system using genetic algorithm based on hyper heuristic approach

In practice the maximum usage of container space arises in many applications which is one of the crucial economical requirements that have a wide impact on good transportation. A huge amount of monetary infrastructure is spent by companies on packing and transportation. This study recommends that there exists a scope for further optimization which if implemented can lead to huge saving. In this paper, we propose a new hyper heuristic approach which automates the design process for packing of two dimensional rectangular blocks. The paper contributes to the literature by introducing a new search technique where genetic algorithm is coupled with the hyper heuristic to get the optimal or sub optimal solution at an acceptable rate. The results obtained show the benefits of hyper-heuristic over traditional one when compared statistically on large benchmark dataset at the 5% level of significance. Improvements on the solution quality with high filling rate up to 99% are observed on benchmark instances.