Ant algorithm hyperheuristic approaches for scheduling problems

For decades, optimisation research has investigated methods to find optimal solutions to many problems in the fields of scheduling, timetabling and rostering. A family of abstract methods known as metaheuristics have been developed and applied to many of these problems, but their application to specific problems requires problem-specific coding and parameter adjusting to produce the best results for that problem. Such specialisation makes code difficult to adapt to new problem instances or new problems. One methodology that intended to increase the generality of state of the art algorithms is known as hyperheuristics. Hyperheuristics are algorithms which construct algorithms: using "building block" heuristics, the higher-level algorithm chooses between heuristics to move around the solution space, learning how to use the heuristics to find better solutions. We introduce a new hyperheuristic based upon the well-known ant algorithm metaheuristic, and apply it towards several real-world problems without parameter tuning, producing results that are competitive with other hyperheuristic methods and established bespoke metaheuristic techniques

Ant algorithm hyperheuristic approaches for scheduling problems

For decades, optimisation research has investigated methods to find optimal solutions to many problems in the fields of scheduling, timetabling and rostering. A family of abstract methods known as metaheuristics have been developed and applied to many of these problems, but their application to specific problems requires problem-specific coding and parameter adjusting to produce the best results for that problem. Such specialisation makes code difficult to adapt to new problem instances or new problems. One methodology that intended to increase the generality of state of the art algorithms is known as hyperheuristics. Hyperheuristics are algorithms which construct algorithms: using "building block" heuristics, the higher-level algorithm chooses between heuristics to move around the solution space, learning how to use the heuristics to find better solutions. We introduce a new hyperheuristic based upon the well-known ant algorithm metaheuristic, and apply it towards several real-world problems without parameter tuning, producing results that are competitive with other hyperheuristic methods and established bespoke metaheuristic techniques

Development and application of hyperheuristics to personnel scheduling

This thesis is concerned with the investigation of hyperheuristic techniques. Hyperheuristics are heuristics which choose heuristics in order to solve a given optimisation problem. In this thesis we investigate and develop a number of hyperheuristic techniques including a hyperheuristic which uses a choice function in order to select which low-level heuristic to apply at each decision point. We demonstrate the effectiveness of our hyperheuristics by means of three personnel scheduling problems taken from the real world. For each application problem, we apply our hyperheuristics to several instances and compare our results with those of other heuristic methods. For all problems, the choice function hyperheuristic appears to be superior to other hyperheuristics considered. It also produces results competitive with those obtained using other sophisticated means. It is hoped that - hyperheuristics can produce solutions of good quality, often competitive with those of modern heuristic techniques, within a short amount of implementation and development time, using only simple and easy-to-implement low-level heuristics. - hyperheuristics are easily re-usable methods as opposed to some metaheuristic methods which tend to use extensive problem-specific information in order to arrive at good solutions. These two latter points constitute the main contributions of this thesis

Development and application of hyperheuristics to personnel scheduling

This thesis is concerned with the investigation of hyperheuristic techniques. Hyperheuristics are heuristics which choose heuristics in order to solve a given optimisation problem. In this thesis we investigate and develop a number of hyperheuristic techniques including a hyperheuristic which uses a choice function in order to select which low-level heuristic to apply at each decision point. We demonstrate the effectiveness of our hyperheuristics by means of three personnel scheduling problems taken from the real world. For each application problem, we apply our hyperheuristics to several instances and compare our results with those of other heuristic methods. For all problems, the choice function hyperheuristic appears to be superior to other hyperheuristics considered. It also produces results competitive with those obtained using other sophisticated means. It is hoped that - hyperheuristics can produce solutions of good quality, often competitive with those of modern heuristic techniques, within a short amount of implementation and development time, using only simple and easy-to-implement low-level heuristics. - hyperheuristics are easily re-usable methods as opposed to some metaheuristic methods which tend to use extensive problem-specific information in order to arrive at good solutions. These two latter points constitute the main contributions of this thesis

Non-linear great deluge with learning mechanism for solving the course timetabling problem

International audienc

An efficient robust hyperheuristic clustering algorithm

Observations on recent research of clustering problems illustrate that most of the approaches used to deal with these problems are based on meta-heuristic and hybrid meta-heuristic to improve the solutions. Hyperheuristic is a set of heuristics, meta- heuristics and high-level search strategies that work on the heuristic search space instead of solution search space. Hyperheuristics techniques have been employed to develop approaches that are more general than optimization search methods and traditional techniques. In the last few years, most studies have focused considerably on the hyperheuristic algorithms to find generalized solutions but highly required robust and efficient solutions. The main idea in this research is to develop techniques that are able to provide an appropriate level of efficiency and high performance to find a class of basic level heuristic over different type of combinatorial optimization problems. Clustering is an unsupervised method in the data mining and pattern recognition. Nevertheless, most of the clustering algorithms are unstable and very sensitive to their input parameters. This study, proposes an efficient and robust hyperheuristic clustering algorithm to find approximate solutions and attempts to generalize the algorithm for different cluster problem domains. Our proposed clustering algorithm has managed to minimize the dissimilarity of all points of a cluster using hyperheuristic method, from the gravity center of the cluster with respect to capacity constraints in each cluster. The algorithm of hyperheuristic has emerged from pool of heuristic techniques. Mapping between solution spaces is one of the powerful and prevalent techniques in optimization domains. Most of the existing algorithms work directly with solution spaces where in some cases is very difficult and is sometime impossible due to the dynamic behavior of data and algorithm. By mapping the heuristic space into solution spaces, it would be possible to make easy decision to solve clustering problems. The proposed hyperheuristic clustering algorithm performs four major components including selection, decision, admission and hybrid metaheuristic algorithm. The intensive experiments have proven that the proposed algorithm has successfully produced robust and efficient clustering results

An Ant Colony based Hyper-Heuristic Approach for the Set Covering Problem

The Set Covering Problem (SCP) is a NP-hard combinatorial optimization problem that is challenging for meta-heuristic algorithms. In the optimization literature, several approaches using meta-heuristics have been developed to tackle the SCP and the quality of the results provided by these approaches highly depends on customized operators that demands high effort from researchers and practitioners. In order to alleviate the complexity of designing metaheuristics, a methodology called hyper-heuristic has emerged as a possible solution. A hyper-heuristic is capable of dynamically selecting simple low-level heuristics accordingly to their performance, alleviating the design complexity of the problem solver and obtaining satisfactory results at the same time. In a previous study, we proposed a hyper-heuristic approach based on Ant Colony Optimization (ACO-HH) for solving the SCP. This paper extends our previous efforts, presenting better results and a deeper analysis of ACO-HH parameters and behavior, specially about the selection of low-level heuristics. The paper also presents a comparison with an ACO meta-heuristic customized for the SCP

An investigation of the ant-based hyper-heuristic for capacitated vehicle routing problem and traveling salesman problem

A brief observation on recent research of routing problems shows that most of the methods used to tackle the problems are using heuristics and metaheuristics; and they often use problem specific knowledge to build or improve solutions. In the last few years, research on hyper-heuristic has been investigated which aims to raise the generality of optimisation systems. This thesis is concerned with the investigation of ant-based hyper-heuristic. Ant algorithms have been applied to vehicle routing problems and have produced competitive results. Therefore, it is assumed that there is a reasonable possibility that ant-based hyperheuristic could perform well for the problem. The thesis first surveys the literature for some common solution methodologies for optimisation problems and explores in some detail the ant algorithms and ant algorithm hyperheuristic methods. Furthermore, the literature specifically concerns with routing problems; the capacitated routing problem (CVRP) and the travelling salesman problem (TSP). The thesis studies the ant system algorithm and further proposes the ant algorithm hyper-heuristic, which introduces a new pheromone update rule in order to improve its performance. The proposed approach, called the ant-based hyper-heuristic is tested to two routing problems; the CVRP and TSP. Although it does not produce any best known results, the experimental results have shown that it is competitive with other methods. Most importantly, it demonstrates how simple and easy to implement low level heuristics, with no extensive parameter tuning. Further analysis shows that the approach possesses learning mechanism when compared to random hyper-heuristic. The approach investigates the number of low level heuristics appropriate and found out that the more low level heuristics used, the better solution is generated. In addition an ACO hyper-heuristic which has two categories of pheromone updates is developed. However, ant-based hyper-heuristic performs better and this is inconsistent with the performance of ACO algorithm in the literature. In TSP, we utilise two different categories of low level heuristics, the TSP heuristics and the CVRP heuristics that were previously used for the CVRP. From the observation, it can be seen that by using any heuristics for the same class of problems, ant-based hyper-heuristic is seen to be able to produce competitive results. This has demonstrated that the ant-based hyper-heuristic is a reusable method. One major advantage of this work is the usage of the same parameter for all problem instances with simple moves and swap procedures. It is hoped that in the future, results obtained will be better than current results by using better intelligent low level heuristics

Optimization grid scheduling with priority base and bees algorithm

Grid computing depends upon sharing large-scales in a network that is widely connected within itself such as the Internet. Therefore, many grid computing researchers and scholars have focused on task scheduling, which is considered one of the NP-Complete issues. The main aim of this current research to propose an optimization of the initial scheduler for grid computing using the bees algorithm. Modern algorithms informed this research. The suggested procedure means that a newly developed algorithm can implement the schedule grid task while accounting for priorities and deadlines to decrease the completion time required for the tasks. The average waiting time of the grid environment can be minimized, and this minimization, in turn, creates an increase in the throughput of the environment