Hyper-heuristic Algorithm Research Articles

A Hybrid Approach of Genetic Algorithm and Multi Objective PSO Task Scheduling in Cloud Computing

The genetic algorithm is an evolutionary optimization algorithm based upon Initial population, crossover, mutation and Evaluation. On the other side, Multi Objective particle swarm optimization (MOPSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability. In high-performance hyper-heuristic algorithm is used to find better scheduling solutions in cloud computing. To improve the scheduling results in terms of makespan, throughput, cost. Hyper-heuristic algorithm finds better scheduling solutions for cloud computing systems and to further improve the scheduling results in terms of make span. A novel Multi objective particle swarm optimization and Genetic Algorithm based hyper-heuristic resource scheduling algorithm has been designed as the hybrid algorithm. Performance of the proposed algorithm has also been evaluated through the Cloud Sim toolkit. We have compared our hybrid scheduling algorithm with existing common heuristic-based scheduling algorithms. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving reduce cost and improve makespan. The proposed model shows the improved resource utilization, makespan, throughput.

A Multi-Agent based Hyper-Heuristic Algorithm for the Winner Determination Problem

In this paper, we propose a Multi-Agent based Hyper-Heuristic algorithm for the Winner Determination Problem named MAH2-WDP. This algorithm explores a set of cooperating agents to select the appropriate operation using learning techniques. MAH2-WDP is specialized for local search methods and evolutionary methods where the following agents are seeking to improve the search within reinforcement learning: the mediator agent, two local search agents, the perturbation agent and two recombination agents. Our computational study shows that the proposed algorithm performs well on the tested benchmark instances in terms of solution quality.

An efficient energy and thermal-aware mapping for regular network-on-chip

Chip temperature and energy consumption become one of the most critical design issues with technology scaling to nanometre-scale, especially for NoC systems with large number of cores and shrunken core size. To balance the temperature and energy consumption on NoC-based multi-cores system, this paper proposes an efficient NoC mapping approach in which the hyper-heuristic algorithm based on genetic operators (HAGO) is the core of this approach. Compared to simulated annealing algorithm and genetic algorithm, HAGO demonstrates a faster convergence speed and excellent stability. Experimental results show that our proposed mapping approach can make a better balance between energy consumption and temperature, which reduces the peak temperature from 365.2 K to 352 K and only increases the energy consumption by 1.12%.

Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design

The paper presents a differential evolution (DE)-based hyper-heuristic algorithm suitable for the optimization of mixed-integer non-linear programming (MINLP) problems. The hyper-heuristic framework includes self-adaptive parameters, an e-constrained method for handling constraints, and 18 DE variants as low-level heuristics. Using the proposed approach, we solved a set of classical test problems on process synthesis and design and compared the results with those of several state-of-the-art evolutionary algorithms. To verify the consistency of the proposed approach, the above-mentioned comparison was made with respect to the percentage of convergences to the global optimum (NRC) and the average number of objective function evaluations (NFE) over several trials. Thus, we found that the proposed methodology significantly improves performance in terms of NRC and NFE.

Joint optimization models for shelf display and inventory control considering the impact of spatial relationship on demand

This research investigates joint optimization models for shelf space allocation and display location with multi-item replenishment. The demand for each item is considered to be dependent not only on its and other items’ allocated shelf space and displayed locations, but also on spatial relationships between items. Joint optimization models are developed for two different scenarios: (a) each item is replenished individually; and (b) multiple items are replenished jointly. A multi-stage simulated annealing (SA) based hyper-heuristic algorithm is proposed to solve both joint optimization models. These models are then evaluated numerically for different problem sizes. The results demonstrate that: (1) the proposed SA based hyper-heuristic algorithm is robust and efficient for both joint optimization models; and (2) the model for the joint replenishment policy leads to a higher profit than that of the model for the individual replenishment policy. Hence, the joint optimization model with joint replenishment policy will be helpful for retailers making decisions about shelf display arrangement and inventory control for multiple items.

A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem

Hyper-heuristics introduce novel approaches for solving challenging combinatorial optimization problems by operating over a set of low level (meta)-heuristics. This is achieved by an evolutionary selection mechanism that controls and combines the strengths of the low level (meta)-heuristics. In this study, we propose a high-performance MultiStart Hyper-heuristic algorithm (MSH-QAP) on the grid for the solution of the Quadratic Assignment Problem (QAP). MSH-QAP algorithm makes use of state-of-the-art (meta)-heuristics, Simulated Annealing (SA), Robust Tabu Search (RTS), Ant Colony Optimization (FAnt), and Breakout Local Search (BLS) that have been reported among the best performing algorithms for the solution of difficult QAP instances in standard benchmark libraries. In the first phase of the algorithm, the most appropriate (meta)-heuristic with its near-optimal parameter settings is selected by using a genetic algorithm optimization layer that uses a self-adaptive parameter setting method for the given problem instance. In the second phase, if an optimal solution cannot be found, selected best performing (meta)-heuristic (with its finely adjusted parameter settings) is executed on the grid using parallel processing and performing several multistarts in order to increase the quality of the discovered solution. MSH-QAP algorithm is tested on 134 problem instances of the QAPLIB benchmark and is shown to be able to solve 122 of the instances exactly. The overall deviation for the problem instances is obtained as 0.013% on the average.

Rule based scheduling Algorithm for Scheduling Mechanism in Large Scale Data Center

Rule Based Scheduling Algorithm have been widely used in the cloud computing as it is simple and easy to implement the Scheduling criteria in terms of energy efficiency and less delay, In this paper, we propose Improved Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) form Scheduling Solutions (heuristics algorithms) from the simulated annealing and genetic algorithm in dynamic large scale Cloud Computing system with diversity operator as sequence dependent and sequence independent scheduling. Specifically, Resources and workloads characterised using the simulated annealing and improved genetic algorithm with n point crossover. Hyper heuristic algorithm is used select best possible solution to the dynamic workload to candidate solutions. The Simulation results on cloudsim proves that proposed system outperforms existing state of approaches in terms of reduced make span and flow time for the task scheduling and resource management

Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems

The offline 2D bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem in which objects with various width and length sizes are packed into minimized number of 2D bins. Various versions of this well-known industrial engineering problem can be faced frequently. Several heuristics have been proposed for the solution of 2DBPP but it has not been possible to find the exact solutions for large problem instances. Next fit, first fit, best fit, unified tabu search, genetic and memetic algorithms are some of the state-of-the-art methods successfully applied to this important problem. In this study, we propose a set of novel hyper-heuristic algorithms that select/combine the state-of-the-art heuristics and local search techniques for minimizing the number of 2D bins. The proposed algorithms introduce new crossover and mutation operators for the selection of the heuristics. Through the results of exhaustive experiments on a set of offline 2DBPP benchmark problem instances, we conclude that the proposed algorithms are robust with their ability to obtain high percentage of the optimal solutions.

Optimal discharge scheduling of energy storage systems in MicroGrids based on hyper-heuristics

In this paper we tackle the optimal Discharge Scheduling of Energy Storage systems Problem (DSESP) in MicroGrids, considering renewable generation, and applying hyper-heuristic (HH) algorithms. The problem consists of, given the generation and load profiles in the MicroGrid, obtaining the optimal discharge scheduling of the Energy Storage System (ESS) that minimizes the consumption from the utility grid. HHs are a novel methodology in optimization problems that constructs a solution to a given problem by means of the application of basic heuristics, evolved using a global search algorithm. This methodology can be easily adapted to solve the DSESP, in this case by using an evolutionary algorithm as global approach. In this paper we detail the adaptations performed to a HH to tackle the DSESP, mainly in the encoding of solutions, and new evolutionary operators that improve the evolution of good solutions to the problem. The performance of the proposed approach has been evaluated in a real Microgrid, with different scenarios of generation and load profiles, obtaining around 5% reduction of the energy consumption from the utility grid.

Solution methods for scheduling of heterogeneous parallel machines applied to the workover rig problem

We take into account a parallel heterogenous machine scheduling problem arising in maintenance planning of heterogeneous wells. This problem particularly arises in the context of workover rig scheduling. The oil wells need regular maintenance to ensure an optimal level of production. After oil production being decreased at some wells, appropriate workover rigs with compatible service capacity, are deployed to serve the wells at discrete locations. Every well needs a certain level of maintenance and rehabilitation services that can only be offered by compatible workover rigs. A new mixed integer linear programming model is propose for this problem that is an arc-time-indexed formulation. We propose a heuristic selection type hyper-heuristic algorithm, which is guided by a learning mechanism resulting in a clever choice of moves in the space of heuristics that are applied to solve the problem. The output is then used to warm start a branch, price and cut algorithm. Our numerical experiments are conducted on instances of a case study of Petrobras, the Brazilian National Petroleum Corporation. The computational experiments prove the efficiency of our hyper-heuristic in searching the right part of the search space using the right alternation among different heuristics and confirms the high quality of solutions obtained by our hyper-heuristic.

Evolution of new algorithms for the binary knapsack problem

Due to its NP-hard nature, it is still difficult to find an optimal solution for instances of the binary knapsack problem as small as 100 variables. In this paper, we developed a three-level hyper-heuristic framework to generate algorithms for the problem. From elementary components and multiple sets of problem instances, algorithms are generated. The best algorithms are selected to go through a second step process, where they are evaluated with problem instances that differ in size and difficulty. The problem instances are generated according to methods that are found in the literature. In all of the larger problem instances, the generated algorithms have less than 1 % error with respect to the optimal solution. Additionally, generated algorithms are efficient, taking on average fractions of a second to find a solution for any instance, with a standard deviation of 1 s. In terms of structure, hyper-heuristic algorithms are compact in size compared with those in the literature, allowing an in-depth analysis of their structure and their presentation to the scientific world.

Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification

In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance.

A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem

In this paper, we propose a particle swarm optimization (PSO) based hyper-heuristic algorithm for solving the resource constrained project scheduling problem (RCPSP). To the best of our knowledge, this is the first attempt to develop a PSO hyper-heuristic and apply to the classic RCPSP. The hyper-heuristic works as an upper-level algorithm that controls several low-level heuristics which operate to the solution space. The solution representation is based on random keys. Active schedules are constructed by the serial scheduling generation scheme using the priorities of the activities which are modified by the low-level heuristics of the algorithm. Also, the double justification operator, i.e. a forward–backward improvement procedure, is applied to all solutions. The proposed approach was tested on a set of standard problem instances of the well-known library PSPLIB and compared with other approaches from the literature. The promising computational results validate the effectiveness of the proposed approach.

A Parametric Hybrid Method for the Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the typical NP–Hard problems of combinatorial optimization area. This paper proposes a new hyper heuristic algorithm named Parametric Hybrid Method (PHM) based on The Farthest Vertex (FV) and Greedy heuristics for solving the traveling salesman problem. In addition, many problem instances from TSPLIB (traveling salesman problem library) were solved with NN, Greedy and PHM algorithms. The experimental results show that the new hybrid algorithm is more effective and efficient than both Greedy and Nearest Neighbor algorithms.

An evolutionary-based hyper-heuristic approach for the Jawbreaker puzzle

In this paper a hyper-heuristic algorithm is designed and developed for its application to the Jawbreaker puzzle. Jawbreaker is an addictive game consisting in a matrix of colored balls, that must be cleared by popping sets of balls of the same color. This puzzle is perfect to be solved by applying hyper-heuristics algorithms, since many different low-level heuristics are available, and they can be applied in a sequential fashion to solve the puzzle. We detail a set of low-level heuristics and a global search procedure (evolutionary algorithm) that conforms to a robust hyper-heuristic, able to solve very difficult instances of the Jawbreaker puzzle. We test the proposed hyper-heuristic approach in Jawbreaker puzzles of different size and difficulty, with excellent results.

Unified encoding for hyper-heuristics with application to bioinformatics

This paper introduces a new approach to applying hyper-heuristic algorithms to solve combinatorial problems with less effort, taking into account the modelling and algorithm construction process. We propose a unified encoding of a solution and a set of low level heuristics which are domain-independent and which change the solution itself. This approach enables us to address NP-hard problems and generate good approximate solutions in a reasonable time without a large amount of additional work required to tailor search methodologies for the problem in hand. In particular, we focused on solving DNA sequencing by hybrydization with errors, which is known to be strongly NP-hard. The approach was extensively tested by solving multiple instances of well-known combinatorial problems and compared with results generated by meta heuristics that have been tailored for specific problem domains.

A new tabu search-based hyper-heuristic algorithm for solving construction leveling problems with limited resource availabilities

We present a tabu search-based hyper-heuristic algorithm for solving construction resource leveling problems, i.e. resource leveling under resource constraints with the prescribed maximum project duration to be equal or greater than the initial/minimum duration, as well as the related resource availability cost problem. The algorithm operates within a commercial project management software package by altering the priorities assigned to activities. The hyper-heuristic controls a set of low-level heuristics, which modify the priorities of selected activities by performing simple moves such as “replace” and “swap”. The most promising heuristics according to their efficiency are applied first, and a tabu list is used to prohibit heuristics with recently poor performance from being applied too soon. The application of the algorithm in three project cases showed that the proposed procedure is promising for handling resource optimization problems. ► Hyperheuristics recently introduced to treat construction project scheduling problems. ► THH is the first tabu hyperheuristic applied to construction scheduling problems. ► THH is a flexible procedure that easily can be applied to different problems. ► Application of the algorithm proved it as promising for treating leveling problems.

A hyper-heuristic for the Longest Common Subsequence problem

The Longest Common Subsequence Problem is the problem of finding a longest string that is a subsequence of every member of a given set of strings. It has applications in FPGA circuit minimization, data compression, and bioinformatics, among others. The problem is NP-hard in its general form, which implies that no exact polynomial-time algorithm currently exists for the problem. Consequently, inexact algorithms have been proposed to obtain good, but not necessarily optimal, solutions in an affordable time. In this paper, a hyper-heuristic algorithm incorporated within a constructive beam search is proposed for the problem. The proposed hyper-heuristic is based on two basic heuristic functions, one of which is new in this paper, and determines dynamically which one to use for a given problem instance. The proposed algorithm is compared with state-of-the-art algorithms on simulated and real biological sequences. Extensive experimental reveals that the proposed hyper-heuristic is superior to the state-of-the-art methods with respect to the solution quality and the running-time.

Construction Resource Allocation and Leveling Using a Threshold Accepting–Based Hyperheuristic Algorithm

AbstractIn this study we propose a threshold accepting based hyperheuristic for solving in a single run both the resource-constrained project scheduling problem or resource allocation, and the resource leveling problem. Having their roots in the field of artificial intelligence, hyperheuristics operate in the “low-level” heuristics domain rather than in the solutions domain. The hyperheuristic has been implemented within a commercial project management software package. Low-level heuristics operate on the solution domain defined by the priority values that the software uses for resource allocation. A case example from the literature and computational experiments on randomly generated projects demonstrate that the hyperheuristic achieves good performance in a timely manner, improving the results provided by the software.

A hyper-heuristic approach to sequencing by hybridization of DNA sequences

In this paper we investigate the use of hyper-heuristic methodologies for predicting DNA sequences. In particular, we utilize Sequencing by Hybridization. We believe that this is the first time that hyper-heuristics have been investigated in this domain. A hyper-heuristic is provided with a set of low-level heuristics and the aim is to decide which heuristic to call at each decision point. We investigate three types of hyper-heuristics. Two of these (simulated annealing and tabu search) draw their inspiration from meta-heuristics. The choice function hyper-heuristic draws its inspiration from reinforcement learning. We utilize two independent sets of low-level heuristics. The first set is based on a previous tabu search method, with the second set being a significant extension to this basic set, including utilizing a different representation and introducing the definition of clusters. The datasets we use comprises two randomly generated datasets and also a publicly available biological dataset. In total, we carried out experiments using 70 different combinations of heuristics, using the three datasets mentioned above and investigating six different hyper-heuristic algorithms. Our results demonstrate the effectiveness of a hyper-heuristic approach to this problem domain. It is necessary to provide a good set of low-level heuristics, which are able to both intensify and diversify the search but this approach has demonstrated very encouraging results on this extremely difficult and important problem domain.