Penerapan Hiperheuristik Berbasis Metode Simulated Annealing untuk Penyelesaian Permasalahan Optimasi Lintas Domain

Permasalahan optimasi lintas domain merupakan permasalahan optimasi yang sangat rumit karena masing-masing permasalahan mempunyai karakteristik yang berbeda. Penyelesaian terhadap permasalahan optimasi lintas domain tersebut melibatkan metode pencarian komputasional untuk memperoleh hasil yang mendekati optimal. Beberapa peneliti terdahulu mengembangkan metode hiperheutistik untuk memperoleh solusi generik yang diharapkan mampu memberikan hasil yang mendekati optimal. Hasil penelitian terdahulu mengidikasikan bahwa strategi hiperheuristik yang lebih baik diperlukan guna memperoleh solusi yang mendekati optimal untuk lintas domain permasalahan. Dalam penelitian ini, upaya untuk mendapatkan solusi generik yang mendekati optimal terhadap permasalahan optimasi lintas domain dilakukan dengan mengembangkan strategi pencarian komputasional pada tatanan High Level Heuristics (HLH) dalam mengatur proses seleksi pada rangkaian  Low Level Heuristics (LLH) kemudian melakukan mekanisme penerimaan solusi. Penelitian ini menguji metode Simulated Annealing (SA) sebagai mekanisme penerimaan solusi dalam tatanan HLH agar dapat menghasilkan solusi mendekati optimal pada berbagai domain masalah optimasi yang dikombinasikan dengan metode seleksi LLH. Penelitian ini melakukan eksperimen untuk menentukan nilai parameter yang tepat untuk mengotomatiskan parameter kontrol SA dalam menyelesaikan permasalahan optimasi lintas domain. Strategi yang digunakan dalam penelitian ini diuji coba untuk menyelesaikan enam permasalahan optimasi domain yang berbeda yang diperoleh dari HyFlex, yaitu Satisfiability (SAT), Bin Packing, Flow Shop, Personnel Scheduling, Travelling Salesmen Problem (TSP), dan Vehicle Routing Problem (VRP). Dari hasil pengujian terhadap enam permasalahan optimasi tersebut, nilai parameter untuk suhu awal T adalah 100 dan faktor penurunan suhu α adalah 0,995

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

Exact and hyper?heuristic solutions for the distribution?installation problem from the VeRoLog 2019 challenge

This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper?heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real?world size revealed the success of the hyper?heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper?heuristic framework

Hyper-heuristics İn Dynamic Environments

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2014Son zamanlarda önerilen metotlar daha çok statik eniyileme problemleri için geliştirilmişlerdir. Fakat gerçek hayatta karşılaşılan eniyileme problemlerinin pek çoğu dinamik bir yapı göstermektedir. Dinamik bir ortamda, eniyileme yönteminin üzerinde çalışmaya başladığı ortamda zaman içinde değişimler olabilir. Ancak bu problemlerin çözümünde genelde bu dinamiklik göz ardı edilerek klasik eniyileme yaklaşımları uygulanmaktadır. Halbuki bu dinamikliği de göz önüne alarak çalışan bir eniyileme yaklaşımı, ortamdaki değişimleri hızlı bir şekilde izleyebilmeli ve bunlara uyum sağlayabilmek için adaptif olmalıdır. Eniyileme algoritması açısından bakıldığında problem ortamı, problemin tanımlı değerleri, eniyilemede kullanılan amaç fonksiyonları ve kısıtlardan oluşur. Ortamdaki dinamiklik, problem ortamını oluşturan bu parçalardan herhangi birisinde veya birkaçında meydana gelen tekil ya da eş zamanlı değişimlerden kaynaklanabilir. Farklı problemlerde bu değişimler de farklı özellikler göstermektedir. Bu özellikler  genelde  değişimlerin şiddetine, sıklığına, periyodik olup olmamasına göre sınıflandırılırlar. Ortamdaki dinamizmin özelliklerine göre farklı durumlarda farklı yaklaşımlar başarılı olmaktadır. Bu ise eniyileme yaklaşımını seçerken ortamdaki değişimlerin özelliklerinin bilinmesi anlamına gelir. Halbuki gerçek hayatta bu her zaman mümkün olmayabilir. Ayrıca ortamın gösterdiği değişimin özellikleri de zaman içinde değişebilir. Bu durumda başta seçilen yaklaşım, eniyilemenin ilerleyen aşamalarında başarılı olmayabilir.  Üst-sezgiseller problem uzayında problem ile etkileşim halinde olan ve aday çözümü güncelleyen alt seviyedeki sezgiseller aracılığı ile arama yapar. Alt seviyede kullanılan, probleme özel sezgiseller ise problemin çözüm uzayında arama yaparlar. Bu nedenle alt seviyedeki sezgiseller, üst-sezgiseller ile problemin çözüm uzayı arasında bir ara katman olarak düşünülebilir. Böylece problem uzayında aramayı alt sezgiseller yapmış olur. Bu özellik sayesinde bir üst-sezgisel, uygun alt sezgisellerin kullanılmasıyla, değiştirilmeden çeşitli problemlere uygulanabilir. Sezgisel seçen üst-sezgiseller konusunda yapılan araştırmaların temel hedefi, eniyilemenin genelleştirme seviyesini yükselterek pek çok farklı problem domeninde ve farklı özellikler gösteren ortamlarda uygulanabilir bir yaklaşım geliştirmektir. Bu nedenle üst-sezgiseller, doğaları gereği adaptif yapıdadırlar. Bu özellikleri sayesinde dinamik ortamlardaki değişimlere, herhangi bir dış müdahale gerektirmeden hızla uyum gösterip, etkin çözümler üretebilirler. Bu tezde öncelikle literatürde var olan üst-sezgisellerin dinamik ortamlar için uygunluğu üzerinde çalışılmıştır. Elde edilen bilgiler ışığında dinamik ortamlarda başarılı çözümler üretecek yeni üst-sezgisel yaklaşım geliştirilmiş ve başarımı ölçülmüştür. Tezin ilk aşamasında, otuz beş tek çözüm üreten sezgisel seçen üst-sezgisellerin başarımını, farklı değişim dinamikleri sergileyen sürekli dinamik eniyileme problemleri için değerlendirdik. Deneylerde üzerinde çalışmak için yapay oluşturulmuş test problemi (Moving Peaks Benchmark) kullanılmıştır. Ayrık eniyileme problemleri için sezgisel seçen üst-sezgisellerin birçok başarılı uygulamaları olmasına rağmen, bilgimiz dahilinde, bu çalışma reel değerli (sürekli) eniyileme problemleri için sezgisel seçen üst-sezgisellerin ilk uygulamalarından biridir. Bunun yanı sıra bu çalışma, bu teknikleri kullanarak dinamik eniyileme problemlerini ele alan çok az çalışma arasında yer almaktadır. Deneysel sonuçlar göstermiştir ki; uygun bileşenli öğrenme tabanlı üst-sezgiseller ortamdaki farklı tipteki değişimlere hızlı bir şekilde tepki gösterebilmekte ve onları takip edebilmektedir. Bu çalışma üst-sezgisellerin dinamik eniyileme problemlerini çözmek için uygun olduğunu göstermektedir.  İkinci aşamada, karınca kolonisi algoritmasından esinlenerek yeni öğrenme tabanlı üst-sezgisel yaklaşım, karınca tabanlı seçim, geliştirilmiştir. Önerilen üst-sezgisel düşük seviyeli bütün sezgisel çiftleri arasındaki feromon yoğunluklarının bir matrisini tutar. Her adımda bir sezgisel, önceden çağırılan sezgisel ile düşük seviyeli sezgisel kümesinden her bir eleman arasındaki feromon değerlerine göre seçilir. Bu çalışmada iyileştiren ve eşit hareket kabul yöntemi kullanılmıştır. Önerdiğimiz üst-sezgisel yönteminin başarımı yapay oluşturulmuş test problemi (Moving Peaks Benchmark) kullanılarak değerlendirilmiştir. Test sonuçlarına göre, önerilen yaklaşım daha önceden dinamik ortamlar için en iyi olarak belirlenen sezgisel seçme yöntemleri ile benzer sonuçlar vermiştir. Önerilen yaklaşım ortam değiştiğinde herhangi bir özel eyleme gerek duymamaktadır. Fakat hareket kabul yönteminin doğası gereği, her bir değişimden sonra üretilen ilk çözüm adayı niteliğine bakılmaksızın kabul edilmektedir. Bundan dolayı hareket kabul yöntemi ortamdaki değişikliği algılamak zorundadır. Bu çalışmada ortamdaki değişimleri algılamak için basit bir yöntem kullanılmıştır. Bu yöntemde şu anki çözümün başarım değeri her adımda tekrardan hesaplanmaktadır. Eğer şu anki çözümün başarım değerinde bir değişiklik varsa ortam değişmiş demektir. Sezgisel seçme yöntemi olarak seçin fonksiyonu, destekli öğrenme ve karınca tabanlı seçim kullanılmıştır. Test sonuçlarına göre yeniden değerlendirme yöntemi bütün yaklaşımların başarımını azaltmıştır.  Bu çalışmada ayrıca önerilen yaklaşımın kapsamlı bir analizi yapılmıştır. Bu amaçla önerilen yaklaşımın adaptasyon yeteneği ve algoritmaların parametrelerinin başarıma etkisi incelenmiştir. Deneysel sonuçlara göre, önerilen yaklaşım hızlı bir şekilde değişimlere uyum sağlayabilmektedir. Önerilen yaklaşım parametre atamalarından çok fazla etkilenmemekte ve geniş aralıklı parametre değerleri için benzer sonuçlar vermektedir.     Tezin son aşamasında, önerilen yaklaşımın başarımı üç farklı uygulamada değerlendirilmiştir. Öncelikle, sezgisel seçen üst-sezgiseller çok popülasyonlu hibrid bir çerçeve içinde kullanılmışlardır. Bu çerçeve çevrimiçi ve çevrimdışı öğrenme mekanizmalarına dayanan üst-sezgiseller ile dağılım tahmini algoritmasının hibridleştirilmesine olanak sağlamaktadır. İyi çözümler üretmek için olasılık vektörlerinin listesi ilk aşamada çevrimdışı olarak öğrenilir. İkinci aşamada iki ayrı popülasyon ve her popülasyonun kendi olasılık vektörleri vardır. Bir alt popülasyon dağılım tahmini algoritması kullanarak örneklendirilirken, diğer alt popülasyon çevrimiçi olarak uygun olasılık vektörünü çevrimiçi aşamada öğrenilen olasılık vektörleri listesinden örneklemek için üst-sezgiselleri kullanır. Önerilen hidrid yöntemin başarımı farklı sezgisel seçme yöntemleri kullanılarak denenmiştir ve Rastgele Permütasyon metodunun daha başarılı olduğu gözlemlenmiştir. Ayrıca bu hibrid yapı literatürde iyi bilinen benzer yaklaşımlarla  karşılaştırılmış ve bunlara göre daha iyi sonuç verdiği gözlemlenmiştir. Önerilen yöntem dinamik ortamlar için önerilmiştir. Bununla birlikte, yöntemin statik ortamlardaki başarımını gözlemlemek için, ikinci uygulama olarak, önerilen metot HyFlex arayüzü üzerinde uygulanmıştır. HyFlex'in Java uygulaması CHeSC2011 yarışmasında kullanılmıştır. Bu uygulama altı statik problem domeni sağlamaktadır. Önerilen yaklaşımın başarımı yarışmadaki katılımcılarla karşılaştırılmıştır. Son uygulama olarak önerilen yaklaşımın başarısı gerçek dünya problemi kullanılarak değerlendirilmiştir. Yapay oluşturulmuş test problemleri problem örneklerini yaratmak için kullanılan önemli araştırma araçları olup verilen domende bu örneklerin özelliklerini kontrol etmemizi sağlar. Bu problem örnekleri farklı algoritmaların başarımını karşılaştırmak için çoğunlukla kullanılmaktadırlar. Öte yandan, gerçek dünya problemleri yapay olarak oluşturulan örneklerden farklı olabilir. Yapay örnekleri kullanarak yapılan algoritmaların test edilmesi verilen algoritmanın gerçek dünya problemi üzerindeki asıl performansını  yansıtmayabilir. Dolayısıyla, bu çalışmada, Dinamik Gezgin Satıcı Problemi olarak bilinen gerçek dünya problemi ele alınmış ve  önerilen yaklaşımın başarımı değerlendirilmiştir. Dinamik Gezgin Satıcı Problemi örneklerini oluşturmak için literatürde çokça kullanılan Gezgin Satıcı Problemi' nin örneklerine trafik faktörü eklenmiştir. Genel olarak, test edilen problemler üzerinde önerilen metodun iyi sonuç verdiği gözlemlenmiştir. En son yapılan testler üst-sezgisellerin genel bir yapı olduğunu vurgulamıştır. Üst-sezgiseller hiçbir değişikliğe ya da parametre ayarlarına gerek duymadan bu çalışmada kullanılan tüm eniyileme problemlerine uygulanmıştır.Current state-of-the-art methodologies are mostly developed for stationary optimization problems. However, many real world problems are dynamic in nature. To handle the complexity of dealing with the changes in the environment, an optimization algorithm needs to be adaptive and hence capable of following the change dynamics. From the point of view of an optimization algorithm, the problem environment consists of the instance, the objectives and the constraints. The dynamism may arise due to a change in any of the components of the problem environment. Existing search methodologies have been modified suitably with respect to the change properties, in order to tackle dynamic environment problems. Population based approaches, such as evolutionary algorithms are frequently used for solving dynamic environment problem.  Hyper-heuristics are high-level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. They operate at a higher level, communicating with the problem domain through a domain barrier. Any type of problem specific information is filtered through the domain barrier. Due to this feature, a hyper-heuristic can be directly employed in various problem domains without requiring any change, of course, through the use of appropriate domain specific low-level heuristics.  Selection hyper-heuristics are highly adaptive search methodologies that aim to raise the level of generality by providing solutions to a diverse set of problems having different characteristics. In this thesis, we investigate single point search based selection hyper-heuristics in dynamic environments. We first work on the applicability of selection hyper-heuristics proposed in literature for dynamic environments. Then, we propose a novel learning hyper-heuristic for dynamic environments and investigate the performance of the proposed hyper-heuristic and its variants.  In the first phase, the performances of thirty-five single point search based selection hyper-heuristics are investigated on continuous dynamic environments exhibiting various change dynamics, produced by the Moving Peaks Benchmark generator. Even though there are many successful applications of selection hyper-heuristics to discrete optimization problems, to the best of our knowledge, this study is one of the initial applications of selection hyper-heuristics to real-valued optimization as well as being among the very few which address dynamic optimization issues using these techniques. The empirical results indicate that learning selection hyper-heuristics which incorporate compatible components can react to different types of changes in the environment and are capable of tracking them. This study shows the suitability of selection hyper-heuristics as solvers in dynamic environments.  In the second phase, we propose a new learning hyper-heuristic, called the {\em Ant-based Selection} ({\em AbS}), for dynamic environments which is inspired from the ant colony optimization algorithm components. The proposed hyper-heuristic maintains a matrix of pheromone intensities (utility values) between all pairs of low-level heuristics. A heuristic is selected based on the utility values between the previously invoked heuristic and each heuristic from the set of low-level heuristics. For this study, we employ the generic Improving and Equal acceptance scheme. We explore the performance of the proposed hyper-heuristic and its variants using Moving Peaks Benchmark (MPB) generator. The empirical results indicate that the proposed heuristic selection scheme provides slightly better performance than the heuristic selection scheme that was previously reported to be the best in dynamic environments.   The proposed approach does not require any special actions whenever a change occurs in the environment. However, the first candidate solution generated after each change is accepted regardless of its quality. Therefore, the move acceptance needs to detect the change. In this study, we use a simple detection mechanism in which the current solution is re-evaluated at each step. If there is a change in the fitness of the current solution, a change is considered to be detected. We consider {\em Ant-based selection}, Choice Function and Reinforcement Learning as the heuristic selection methods. The results show that the re-evaluation process slightly deteriorates the performance of approaches for especially high frequency changes, however, the approach is suitable for cases where changes cannot be made known to the optimization algorithm. We then investigate the effect of the parameters of the proposed algorithm on overall performance. The results show that the settings of the parameters are not very sensitive  and similar results are obtained for a wide range of parameter values. In the third phase, we explore the performance of the proposed hyper-heuristic through three different applications. As the first application, the selection hyper-heuristics are used in a hybrid multi-population framework. We use a hybridization of the Estimation of Distribution Algorithm (EDA) with hyper-heuristics in the form of a two-phase framework. We investigate the influence of different heuristic selection methods. The empirical results show that a heuristic selection method that relies on a fixed permutation of the underlying low-level heuristics is more successful than the learning approaches across different dynamic environments produced by a well-known benchmark generator. The proposed approach also outperforms some of the top approaches in literature for dynamic environment problems. Ant-based selection is proposed for dynamic environments. However, to see its performance in a stationary environment, Ant-based Selection is applied to six stationary optimization problems provided in HyFlex as the  second application. The results are compared with the results of participants in CHeSC2011 competition. Finally, we present the performance of Ant-based Selection on a real-world optimization problem referred to as the Dynamic Traveling Salesman Problem. The overall results show that the proposed approach delivers good performance on the tested optimization problems. These last set of experiments also emphasize the general nature of hyper-heuristics. For all optimization problems in this study, all hyper-heuristics are applied without requiring any modifications or parameter tuning.DoktoraPh

Multi-stage hyper-heuristics for optimisation problems

There is a growing interest towards self configuring/tuning automated general-purpose reusable heuristic approaches for combinatorial optimisation, such as, hyper-heuristics. Hyper-heuristics are search methodologies which explore the space of heuristics rather than the solutions to solve a broad range of hard computational problems without requiring any expert intervention. There are two common types of hyper-heuristics in the literature: selection and generation methodologies. This work focuses on the former type of hyper-heuristics. Almost all selection hyper-heuristics perform a single point based iterative search over the space of heuristics by selecting and applying a suitable heuristic to the solution in hand at each decision point. Then the newly generated solution is either accepted or rejected using an acceptance method. This improvement process is repeated starting from an initial solution until a set of termination criteria is satisfied. The number of studies on the design of hyper-heuristic methodologies has been rapidly increasing and currently, we already have a variety of approaches, each with their own strengths and weaknesses. It has been observed that different hyper-heuristics perform differently on a given subset of problem instances and more importantly, a hyper-heuristic performs differently as the set of low level heuristics vary. This thesis introduces a general "multi-stage" hyper-heuristic framework enabling the use and exploitation of multiple selection hyper-heuristics at different stages during the search process. The goal is designing an approach utilising multiple hyper-heuristics for a more effective and efficient overall performance when compared to the performance of each constituent selection hyper-heuristic. The level of generality that a hyper-heuristic can achieve has always been of interest to the hyper-heuristic researchers. Hence, a variety of multi-stage hyper-heuristics based on the framework are not only applied to the real-world combinatorial optimisation problems of high school timetabling, multi-mode resource-constrained multi-project scheduling and construction of magic squares, but also tested on the well known hyper-heuristic benchmark of CHeSC 2011. The empirical results show that the multi-stage hyper-heuristics designed based on the proposed framework are still inherently general, easy-to-implement, adaptive and reusable. They can be extremely effective solvers considering their success in the competitions of ITC 2011 and MISTA 2013. Moreover, a particular multi-stage hyper-heuristic outperformed the state-of-the-art selection hyper-heuristic from CHeSC 2011

Multi-stage hyper-heuristics for optimisation problems

There is a growing interest towards self configuring/tuning automated general-purpose reusable heuristic approaches for combinatorial optimisation, such as, hyper-heuristics. Hyper-heuristics are search methodologies which explore the space of heuristics rather than the solutions to solve a broad range of hard computational problems without requiring any expert intervention. There are two common types of hyper-heuristics in the literature: selection and generation methodologies. This work focuses on the former type of hyper-heuristics. Almost all selection hyper-heuristics perform a single point based iterative search over the space of heuristics by selecting and applying a suitable heuristic to the solution in hand at each decision point. Then the newly generated solution is either accepted or rejected using an acceptance method. This improvement process is repeated starting from an initial solution until a set of termination criteria is satisfied. The number of studies on the design of hyper-heuristic methodologies has been rapidly increasing and currently, we already have a variety of approaches, each with their own strengths and weaknesses. It has been observed that different hyper-heuristics perform differently on a given subset of problem instances and more importantly, a hyper-heuristic performs differently as the set of low level heuristics vary. This thesis introduces a general "multi-stage" hyper-heuristic framework enabling the use and exploitation of multiple selection hyper-heuristics at different stages during the search process. The goal is designing an approach utilising multiple hyper-heuristics for a more effective and efficient overall performance when compared to the performance of each constituent selection hyper-heuristic. The level of generality that a hyper-heuristic can achieve has always been of interest to the hyper-heuristic researchers. Hence, a variety of multi-stage hyper-heuristics based on the framework are not only applied to the real-world combinatorial optimisation problems of high school timetabling, multi-mode resource-constrained multi-project scheduling and construction of magic squares, but also tested on the well known hyper-heuristic benchmark of CHeSC 2011. The empirical results show that the multi-stage hyper-heuristics designed based on the proposed framework are still inherently general, easy-to-implement, adaptive and reusable. They can be extremely effective solvers considering their success in the competitions of ITC 2011 and MISTA 2013. Moreover, a particular multi-stage hyper-heuristic outperformed the state-of-the-art selection hyper-heuristic from CHeSC 2011

A study of evoluntionary perturbative hyper-heuristics for the nurse rostering problem.

Master of Science in Computer Science. University of KwaZulu-Natal, Pietermaritzburg 2017.Hyper-heuristics are an emerging field of study for combinatorial optimization. The aim of a hyper-heuristic is to produce good results across a set of problems rather than producing the best results. There has been little investigation of hyper-heuristics for the nurse rostering problem. The majority of hyper-heuristics for the nurse rostering problem fit into a single type of hyper-heuristic, the selection perturbative hyper-heuristic. There is no work in using evolutionary algorithms employed as selection perturbative hyper-heuristics for the nurse rostering problem. There is also no work in using the generative perturbative type of hyper-heuristic for the nurse rostering problem. The first objective of this dissertation is to investigate the selection perturbative hyper-heuristic for the nurse rostering problem and the effectiveness of employing an evolutionary algorithm (SPHH). The second objective is to investigate a generative perturbative hyper-heuristic to evolve perturbation heuristics for the nurse rostering problem using genetic programming (GPHH). The third objective is to compare the performance of SPHH and GPHH. SPHH and GPHH were evaluated using the INRC2010 benchmark data set and the results obtained were compared to available results from literature. The INRC2010 benchmark set is comprised of sprint, medium and long instance types. SPHH and GPHH produced good results for the INRC2010 benchmark data set. GPHH and SPHH were found to have different strengths and weaknesses. SPHH found better results than GPHH for the medium instances. GPHH found better results than SPHH for the long instances. SPHH produced better average results. GPHH produced results that were closer to the best known results. These results suggest future research should investigate combining SPHH and GPHH to benefit from the strengths of both perturbative hyper-heuristics

Hyper-heuristics for two complex vehicle routing problems: the urban transit routing problem, and a delivery and installation problem

Hyper-heuristics have emerged as general purpose search techniques that explore the space of low-level heuristics to improve a given solution under an iterative framework. They were introduced to raise the level of generality of search techniques representing self-configuring and automated reusable heuristic approaches for solving combinatorial problems. There are two classes of hyper-heuristics identified in the literatire: generation and selection hyper-heuristics. In this thesis, we focus on the class of selection hyperheuristics and their efficient design and application on complex routing problems. We specifically focus on two routing problems: the Urban Transit Network design Problem (UTRP), and a rich vehicle routing problem for the delivery and installation of equipment which was the subject of the VeRoLog solver challenge 2019. The urban transit routing problem (UTRP) aims to find efficient travelling routes for vehicles in public transportation systems. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of combinatorial problems whose optimal solution is hard to find with the complexity that arises from the large search space, and the multiple constraints imposed in constructing the solution. Furthermore, realistic benchmark data sets are lacking, making it difficult for researchers to compare their problem solving techniques with those of other researchers. We evaluate and compare the performance of a set of selection hyperheuristics on the UTRP, with the goal of minimising the passengers’ travel time and the operators’ costs. Each selection hyper-heuristic is empirically tested on a set of known benchmark instances and statistically compared against all the other hyper-heuristics to determine the best approach. A sequence-based selection method utilising a hidden markov model achieved the best performance between the tested selection methods, and better solutions than the current known best solutions are achieved on benchmark instances. Then, we propose a hyper-heuristic algorithm specifically designed to solve the UTRP with defined terminal nodes that determine the start and end points of bus journeys. The algorithm is applied to a novel set of benchmark instances with real world size and characteristics representing the extended urban area of Nottingham city. We compare the hyper-heuristic performance on the data set with the NSGAII algorithm and real world bus routes, and prove that better solutions are found by hyper-heuristics. Due to the clear gap in research between the application of optimisation algorithms in public routes network optimisation and the real world planning processes, we implemented a hyper-heuristic algorithm that interactively work with interface procedures to optimise the public transport lines in Visum transportation modelling software. We adopt Selection Hyper-heuristics for two optimisation problems and the optimisation objectives include the passengers’ average travel time and operators’ costs. The results demonstrate the successful implementation of the applied optimisation methods for multi-modal public transport networks. Finally we introduce a population based hyperheuristic algorithm and apply it on a complex vehicle routing problem consisting of two stages: a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) for the delivery of equipment, and the Service Technician Routing and Scheduling Problem (STRSP) for the installation of the delivered equipment. This problem was the subject of the VeRoLog solver challenge 2019. We apply the hyper-heuristic population-based algorithm on a small and large size data sets, and show that our approach performed better in terms of results and run time on small instances compared to the results of mathematical model implemented for this problem. We perform analysis of the new proposed algorithm and show that it finds better quality solutions compared to its constituent selection hyper-heuristics when applied individually. Finally we conclude the thesis with a summary of the work and future plans