Hyper-heuristic Approach Research Articles

Multi-objective scheduling in hybrid flow shop: Evolutionary algorithms using multi-decoding framework

Hybrid flow shops are common manufacturing environments applied in many industrial fields. This paper tackles the scheduling problem in hybrid flow shop with unrelated machines, machine eligibility and sequence-dependent setup times (SDST) to minimize the bi-criteria of total tardiness and total setup time. Evolutionary algorithms (EAs) are adopted to solve the problem. Firstly, four efficient decoding algorithms using different machine selection rules are developed for constructing a schedule from a job permutation. These decoding algorithms are able to map the job permutation space to distinct regions in the objective space. Then, we propose a multi-decoding framework (MDF) for taking advantage of multiple decoding algorithms along one evolution path. The hybridization of MDF and EAs leads to a hyper-heuristic approach. The proposed MDF is coupled with a genetic algorithm to solve the problem in “a priori” approach, that is, to optimize a convex combination of the objectives given user preference information. The framework is also embedded to a multi-objective genetic algorithm, known as NSGA-II, to solve the problem in “a posteriori” approach, which aims at approximating the Pareto-optimal set for the user to make posterior decisions. The efficiency of the proposed methods is validated by numerical results. More specifically, when “a priori” approach is used, the proposed MDF helps EAs adjusting the adopted decoding scheme and generating solution aligned to the user preference; when “a posteriori” approach is applied, the MDF extends the search space and improves the solution quality.

Two multi-start heuristics for the k-traveling salesman problem

This paper is concerned with the k-traveling salesman problem (k-TSP), which is a variation of widely studied traveling salesman problem (TSP). Given a set of n cities including a home city and a fixed value k such that $$1 < k \le n$$ , this problem seeks a tour of minimum length which starts and ends at the home city and visits k cities (including the home city) exactly out of these n cities. Finding a feasible solution to k-TSP involves finding a subset of k cities including the home city first, and then a circular permutation representing a tour of these k cities. In this paper, we have proposed two multi-start heuristic approaches for the k-TSP. The first approach is based on general variable neighborhood search algorithm (GVNS), whereas the latter approach is a hyper-heuristic (HH) approach. A variable neighborhood descent strategy operating over two neighborhood structures is utilized for doing the local search in the GVNS. As part of the hyper-heuristic, two low level heuristics are considered. To the best of our knowledge, these are the first metaheuristic and hyper-heuristic approaches for the k-TSP. To evaluate the performance of our approaches, a set of benchmark instances is created utilizing instances from TSPLIB. Computational results on these benchmark instances show HH approach to be better than GVNS approach.

Evaluation of Hyper-Heuristic Method Using Random-Hill Climbing Algorithm in the Examination Timetabling Problem

Examination timetabling is included in the category of Nondeterministic Polynomial-Hard (NP-Hard) problems, namely problems that cannot be solved by conventional methods in finding optimal solutions. One solution to this problem is to use the Simple Random - Hill Climbing - Hyper Heuristic (SR-HC-HH) approach. But this approach still cannot produce an optimum solution. The researcher presents a critical analysis of the performance evaluation of the solution method used. The stages of this research include: (1) problem identification; (2) literature study; (3) data retrieval and understanding; (4) translation of mathematical models into data structures; (5) evaluation of the SR-HC-HH algorithm; (6) trial implementation; (7) algorithmic experiment parameters; and (8) analysis of results and conclusions. SR-HC-HH algorithm used in Hyper-Heuristics-based applications is able to solve timetabling problems in the Examination Timetabling Problem domain in the ITC-2007 dataset, but it is still not optimal. Parameters that can be changed in this study are the number of iterations (time limit) and algorithms for selection of Low Level Heuristics. Changing parameters in the trial scenario can also affect the results of a more optimum solution. The parameters to be explored in this study include LLH selection strategies and move acceptance in hyper-heuristics.

Self Adaptive and Simulated Annealing Hyper-Heuristics Approach for Post-Enrollment Course Timetabling

One of timetabling problem in education field is Post-Enrollment Course Timetabling (PE-CTT). The challenges faced in the PE-CTT are differences in type of problems, a number of limitations, and requirements that differ from one university to another. It is difficult to find common and effective solutions. State-of-art method that can develop more general systems by using cheaper methods and still being able to solve problems is Hyper-Heuristic approach.The Self-Adaptive Strategy is used as a strategy for selecting Low-Level-Heuristics (LLH) and Simulated Annealing as a Move Acceptance (MA) strategy to solve the course timetabling problems. Self-adaptive can increase the level of convergence to the optimal value in the optimization process. Simulated Annealing can accept solutions that are no better so that the final solution is not trapped in the local optima solution.The contributions of this paper are state-of-art hybridization of Self-Adaptive and Simulated Annealing Hyper-Heuristic approach to solve post-enrollment course timetabling problem. The Self Adaptive and Simulated Annealing Hyper-Heuristics then compared with Simple Random and Simulated Annealing Hyper-Heuristics. Tests will be carried out on Socha dataset. The hybridization of Self Adaptive and Simulated Annealing shows competitive results.

Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects

This paper proposed a novel approach for a practical version of the cold chain, namely location-routing problem-based low-carbon cold chain (LRPLCCC). In the proposed bi-objective model, the first objective is the total logistics cost, including the fixed costs of the opened depots and leased vehicles, as well as the cost of fuel consumption and carbon emissions, and the second is to minimize the amount of quality degradation that aims at improving clients’ satisfaction and maintain product freshness. The cargos of clients are classified into three types: general, refrigerated, and frozen cargos. Since the presented problem is NP-hard, a novel multi-objective hyperheuristic (MOHH) was proposed to obtain the Pareto solutions. In this framework, three selection strategies were developed to improve the performance of MOHH, that is, random simple, choice function, and FRR-MAB (fitness rate rank based multi-armed bandit), and three acceptance criteria using the decomposition approaches in MOEA/D were also developed, namely penalty-based boundary intersection, Tchebycheff, and modified Tchebycheff approaches. Extensive experiments were provided to verify the efficiency of the proposed algorithms and assessed the effects of algorithm parameters on the Pareto front. The results showed that the efficiency of the proposed algorithm outperforms several existing well-known multi-objective evolutionary algorithms (MOEA).

An ant colony hyperheuristic approach for matrix bandwidth reduction

This paper considers the bandwidth reduction problem for large-scale matrices in serial computations. A heuristic for bandwidth reduction reorders the rows and columns of a given sparse matrix so that the method places entries with a nonzero value as close to the main diagonal as possible. Bandwidth optimization is a critical issue for many scientific and engineering applications. In this regard, this paper proposes an ant colony hyperheuristic approach for the bandwidth reduction of symmetric and nonsymmetric matrices. The ant colony hyperheuristic approach evolves and selects graph theory bandwidth reduction algorithms for application areas. This paper evaluates the resulting heuristics for bandwidth reduction in each application area against the most promising low-cost heuristics for bandwidth reduction. This paper also includes a numerical examination of the current state-of-the-art metaheuristic algorithms for matrix bandwidth reduction. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed approach outperformed state-of-the-art low-cost heuristics for bandwidth reduction when applied to problem cases arising from several application areas, clearly indicating the promise of the proposal.

H2-SLAN: A hyper-heuristic based on stochastic learning automata network for obtaining, storing, and retrieving heuristic knowledge

Over the years, the meta-heuristics have been adapted and based on metaphors, whose proposals show effective solutions. Nevertheless, these abstractions are proving to be simple camouflage processes. This study presents a hyper-heuristic based on stochastic automata networks with learning, controlling a set of meta-heuristics. In this sense, this work investigates the moving through the search space performed by heuristic mechanisms, free of abstractions used by meta-heuristic. Or, in the conceptual terms proposed, we built up a hyper-heuristic model based on stochastic automata networks with learning, for selection and parameterization of low-level heuristics. The approach works on eight known meta-heuristic, exploiting features, and strengths of each algorithm. This identification allied to the theory of stochastic automata networks with learning guided the construction of their representations. These representations are consolidated in a meta-space, a part of the architecture of the hyper-heuristic proposed in work, named H2-SLAN Model. The results illustrate the effectiveness of our hyper-heuristic approach, regardless of heuristic composition, when compared to each meta-heuristic. Besides, hyper-heuristic performs better than individual meta-heuristics, thus significantly increasing optimization opportunities. As a result of this work, we got a system capable of selecting and parameterize low-level heuristics, with the ability to learn the heuristic movements employed by the model in the search space.

A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs

This study addresses the stochastic parallel assembly line balancing problem with equipment costs and presents a hyper-heuristic approach based on simulated annealing for solving it. A cost-based objective function is employed to represent the incompletion, equipment, and station installation costs. The hyper-heuristic approach is utilized to search on sequencing heuristics search space, rather than a problem-specific solution space. This study focuses on the consideration of equipment costs while balancing a stochastic parallel assembly line. The performance of the solution approach is also tested on the single-model stochastic assembly line balancing problems and stochastic parallel assembly line balancing problems due to the generalizability of hyper-heuristics. The results of the benchmark problems show that in most cases the proposed algorithm provides better solutions than the best-known solutions in literature. An extensive computational study performed to determine the parameter levels derived from the problem and the solution method. The effect of the equipment costs for stochastic parallel assembly lines is also analyzed in detail.

A Hyperheuristic Approach for Location-Routing Problem of Cold Chain Logistics considering Fuel Consumption.

In response to violent market competition and demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emission for better development. In this paper, a biobjective mathematical model is established for cold chain logistics network in consideration of economic, social, and environmental benefits; in other words, the total cost and distribution period of cold chain logistics are optimized, while the total cost consists of cargo damage cost, refrigeration cost of refrigeration equipment, transportation cost, fuel consumption cost, penalty cost of time window, and operation cost of distribution centres. One multiobjective hyperheuristic optimization framework is proposed to address this multiobjective problem. In the framework, four selection strategies and four acceptance criteria for solution set are proposed to improve the performance of the multiobjective hyperheuristic framework. As known from a comparative study, the proposed algorithm had better overall performance than NSGA-II. Furthermore, instances of cold chain logistics are modelled and solved, and the resulting Pareto solution set offers diverse options for a decision maker to select an appropriate cold chain logistics distribution network in the interest of the logistics company.

A Hyper-Heuristic for the Orienteering Problem With Hotel Selection

We present a hyper-heuristic approach to solve Orienteering Problem with Hotel Selection (OPHS). In practical applications, OPHS appears when a tourist is planning to visit various attractions and there is not enough time to reach all of them in a single day. Therefore, the tourist must build a tour within several days by selecting hotels, where each day has a different time budget. We propose a hyper-heuristic based on a Large Neighborhood Search, composed by a set of low-level heuristics that satisfy the different constraints associated with the problem. We put special emphasis on collaboration between low-level heuristics in order to guide the algorithm to more promising areas. We use 395 benchmark instances with known optimal solutions. This approach proves to be a more general method, with a simpler design compared to the literature, and is able to find 217 of the 395 known optimal solutions, in acceptable computational times.

A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem

Multi-skill resource-constrained project scheduling problem (MS-RCPSP) is one of the most investigated problems in operations research and management science. In this paper, a genetic programming hyper-heuristic (GP-HH) algorithm is proposed to address the MS-RCPSP. Firstly, a single task sequence vector is used to encode solution, and a repair-based decoding scheme is proposed to generate feasible schedules. Secondly, ten simple heuristic rules are designed to construct a set of low-level heuristics. Thirdly, genetic programming is utilized as a high-level strategy which can manage the low-level heuristics on the heuristic domain flexibly. In addition, the design-of-experiment (DOE) method is employed to investigate the effect of parameters setting. Finally, the performance of GP-HH is evaluated on the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset consisting of 36 instances. Computational comparisons between GP-HH and the state-of-the-art algorithms indicate the superiority of the proposed GP-HH in computing feasible solutions to the problem.

Evolving reordering algorithms using an ant colony hyperheuristic approach for accelerating the convergence of the ICCG method

This paper proposes a novel ant colony hyperheuristic approach for reordering the rows and columns of symmetric positive definite matrices. This ant colony hyperheuristic approach evolves heuristics for bandwidth reduction applied to instances arising from specific application areas with the objective of generating low-cost reordering algorithms. This paper evaluates the resulting reordering algorithm in each application area against state-of-the-art reordering algorithms with the purpose of reducing the running times of the zero-fill incomplete Cholesky-preconditioned conjugate gradient method. The results obtained on a wide-ranging set of standard benchmark matrices show that the proposed approach compares favorably with state-of-the-art reordering algorithms when applied to instances arising from computational fluid dynamics, structural, and thermal problems.

An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem.

In this paper, we consider a variant of the location-routing problem (LRP), namely the the multiobjective regional low-carbon LRP (MORLCLRP). The MORLCLRP seeks to minimize service duration, client waiting time, and total costs, which includes carbon emission costs and total depot, vehicle, and travelling costs with respect to fuel consumption, and considers three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows. We formulated a multiobjective mixed integer programming formulations for the problem under study. Due to the complexity of the proposed problem, a general framework, named the multiobjective hyper-heuristic approach (MOHH), was applied for obtaining Pareto-optimal solutions. Aiming at improving the performance of the proposed approach, four selection strategies and three acceptance criteria were developed as the high-level heuristic (HLH), and three multiobjective evolutionary algorithms (MOEAs) were designed as the low-level heuristics (LLHs). The performance of the proposed approach was tested for a set of different instances and comparative analyses were also conducted against eight domain-tailored MOEAs. The results showed that the proposed algorithm produced a high-quality Pareto set for most instances. Additionally, extensive analyses were also carried out to empirically assess the effects of domain-specific parameters (i.e., fleet composition, client and depot distribution, and zones area) on key performance indicators (i.e., hypervolume, inverted generated distance, and ratio of nondominated individuals). Several management insights are provided by analyzing the Pareto solutions.

Towards effective resolution approaches for solving the sum coloring problem

ABSTRACTThe paper sheds light on the sum coloring of graphs which has a wealth of pertinence to several applications in scheduling and resource allocation. Throughout this work, we investigate solving the problem using three new mathematical formulations. Moreover, we integrate a general modelling methodology, namely, hyper-heuristic approach based on different metaheuristics to effectively get tight upper bounds. Comprehensive empirical study reports new exact solutions for some tested instances. Computational performance of our proposed hyper-heuristic approach, based on a set of benchmark instances, turned out to be effective on both computation time and solution quality.

The capacitated single-allocation p-hub location routing problem: a Lagrangian relaxation and a hyper-heuristic approach

A variant of the hub location routing problem studied in this work, which is the problem of locating a set of hub nodes, is establishing the hub-level network and allocating the spoke nodes to the hub nodes. As a particular property of this problem, each cluster of spoke nodes allocated to a hub constitutes a directed route that starts from the hub, visits all the spokes in the same cluster, and terminates to the same hub. We propose a hybrid of hyper-heuristic and a relax-and-cut solution method, which includes cooperation among several low-level heuristics governed and controlled by a learning mechanism. This hybridization provides a mechanism in which the obtained dual information through the Lagrangian relaxation (bundle) method being utilized to guide the local searches for constructing/improving feasible solutions. Several classes of valid inequalities as well as efficient separation routings are also proposed for being used within the relax-and-cut approach. Our extensive computational experiments confirm the efficiency of this solution method in terms of quality as well as computational time.

A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery

This paper addresses a new variant of location-routing problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). A hyper-heuristic approach based on iterated local search (ILS-HH) is introduced to automatically optimize the LRPSPD. On basis of the novel proposed framework of hyper-heuristic, four selections mechanisms and five activation strategies are developed to examine the performance of the proposed framework. Three types computational evaluations were carried out and several conclusions can be drawn: (1) the proposed framework performs better than the classical one with performing several heavy-duty combinations of strategies in terms of solution quality and computing time; (2) different activated strategies have slight (not significant) effect on exploiting best solutions; (3) FRR-MAB-TS (fitness ratio rank based on multi-armed bandit with tabu search) works best among all selection methods. Moreover, the proposed approach could provide competitive, even better results compared to fine-tuned bespoke state-of-the-art approaches.

Tackling Large-Scale and Combinatorial Bi-Level Problems With a Genetic Programming Hyper-Heuristic

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is an ${\mathcal {NP}}$ -hard problem. In this paper, we tackle large-scale and combinatorial bi-level problems using GP hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the bi-level cloud pricing optimization problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization.

Automated Course Timetabling Optimization Using Tabu-Variable Neighborhood Search Based Hyper-Heuristic Algorithm

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers’ requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works.

Hyper-heuristic approaches for strategic mine planning under uncertainty

A hyper-heuristic refers to a search method or a learning mechanism for selecting or generating heuristics to solve computational search problems. Operating at a level of abstraction above that of a metaheuristic, it can be seen as an algorithm that tries to find an appropriate solution method at a given decision point rather than a solution. This paper introduces a new hyper-heuristic that combines elements from reinforcement learning and tabu search. It is applied to solve two complex stochastic scheduling problems arising in mining, namely the stochastic open-pit mine production scheduling problem with one processing stream (SMPS) and one of its generalizations, SMPS with multiple processing streams and stockpiles (SMPS+). The performance of the new hyper-heuristic is assessed by comparing it to several solution methods from the literature: problem-specific algorithms tailored for the two problems addressed in the paper and general hyper-heuristics, which use only limited problem-specific information. The computational results indicate that not only is the proposed new hyper-heuristic approach superior to the other hyper-heuristics, but it also provides results that are comparable to or improve on the results obtained by the state-of-the-art problem-specific methods.

Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time

Flexible job-shop scheduling problem (FJSP) is among the most investigated scheduling problems over the past decades. The uncertainty of the processing time is an important practical characteristic in manufacturing. By considering the processing time to be fuzzy variable, the FJSP with fuzzy processing time (FJSPF) is more close to the reality. This paper proposes an effective backtracking search based hyper-heuristic (BS-HH) approach to address the FJSPF. Firstly, six simple and efficient heuristics are incorporated into the BS-HH to construct a set of low-level heuristics. Secondly, a backtracking search algorithm is introduced as the high-level strategy to manage the low-level heuristics to operate on the solution domain. Additionally, a novel hybrid solution decoding scheme is proposed to find an optimal solution more efficiently. Finally, the performance of the BS-HH is evaluated on two typical benchmark sets. The results show that the proposed hyper-heuristic outperforms the state-of-the-art algorithms in solving the FJSPF.