Stochastic local search: a state-of-the-art review

The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search algorithms is given. Finally, the last part of the paper present and discuss some of the most latest trends in application of stochastic local search algorithms in machine learning, data mining and some other areas of science and engineering. We conclude with a discussion on capabilities and limitations of stochastic local search algorithms

An investigation of multi-objective hyper-heuristics for multi-objective optimisation

In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function. GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems

Metaheuristics “In the Large”

Many people have generously given their time to the various activities of the MitL initiative. Particular gratitude is due to Adam Barwell, John A. Clark, Patrick De Causmaecker, Emma Hart, Zoltan A. Kocsis, Ben Kovitz, Krzysztof Krawiec, John McCall, Nelishia Pillay, Kevin Sim, Jim Smith, Thomas Stutzle, Eric Taillard and Stefan Wagner. J. Swan acknowledges the support of UK EPSRC grant EP/J017515/1 and the EU H2020 SAFIRE Factories project. P. GarciaSanchez and J. J. Merelo acknowledges the support of TIN201785727-C4-2-P by the Spanish Ministry of Economy and Competitiveness. M. Wagner acknowledges the support of the Australian Research Council grants DE160100850 and DP200102364.Following decades of sustained improvement, metaheuristics are one of the great success stories of opti- mization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to sup- port the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics “In the Large”project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field’s progress. We argue that, via such principled choice of infrastructure support, the field can pur- sue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) EP/J017515/1EU H2020 SAFIRE Factories projectSpanish Ministry of Economy and Competitiveness TIN201785727-C4-2-PAustralian Research Council DE160100850 DP20010236

An investigation of multi-objective hyper-heuristics for multi-objective optimisation

In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function. GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems

Metaheuristics and their application in engineering optimization

Esta tesina final de carrera estudia la posibilidad de utilizar metaheurísticas en problemas de optimización en ingeniería. Las metaheurísticas son métodos computacionales basados en la naturaleza, con cierta aleatoriedad en su comportamiento, desarrollados en las últimas décadas que permiten encontrar aproximaciones aceptables a problemas de optimización muy complejos. En este estudio se implementan dos metaheurísticas, Algoritmo Genético y Recocido Simulado, y se ponen a prueba mediante problemas típicos de optimización relacionados con la ingeniería. A su vez, se compara los resultados obtenidos con los de métodos clásicos como son la búsqueda exhaustiva o el método de Newton

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Multiobjective in-core fuel management optimisation for nuclear research reactors

Thesis (PhD)--Stellenbosch University, 2016.ENGLISH SUMMARY : The efficiency and effectiveness of fuel usage in a typical nuclear reactor is influenced by the specific arrangement of available fuel assemblies in the reactor core positions. This arrangement of assemblies is referred to as a fuel reload configuration and usually has to be determined anew for each operational cycle of a reactor. Very often, multiple objectives are pursued simultaneously when designing a reload configuration, especially in the context of nuclear research reactors. In the multiobjective in-core fuel management optimization (MICFMO) problem, the aim is to identify a Pareto optimal set of compromise or trade-off reload configurations. Such a set may then be presented to a decision maker (i.e. a nuclear reactor operator) for consideration so as to select a preferred configuration. In the first part of this dissertation, a secularization-based methodology for MICFMO is pro- posed in order to address several shortcomings associated with the popular weighting method often employed in the literature for solving the MICFMO problem. The proposed methodology has been implemented in a reactor simulation code, called the OSCAR-4 system. In order to demonstrate its practical applicability, the methodology is applied to solve several MICFMO problem instances in the context of two research reactors. In the second part of the dissertation, an extensive investigation is conducted into the suitability of several multiobjective optimization algorithms for solving the constrained MICFMO problem. The computation time required to perform the investigation is reduced through the usage of several artificial neural networks constructed in the dissertation for objective and constraint function evaluations. Eight multiobjective metaheuristics are compared in the context of a test suite of several MICFMO problem instances, based on the SAFARI-1 research reactor in South Africa. The investigation reveals that the NSGA-II, the P-ACO algorithm and the MOOCEM are generally the best-performing metaheuristics across the problem instances in the test suite, while the MOVNS algorithm also performs well in the context of bi-objective problem instances. As part of this investigation, a multiplicative penalty function (MPF) constraint handling technique is also proposed and compared to an existing constraint handling technique, called constrained-domination. The comparison reveals that the MPF technique is a competitive alternative to constrained-domination. In an attempt to raise the level of generality at which MICFMO may be performed and potentially improve the quality of optimization results, a multiobjective hyperheuristic, called the AMALGAM method, is also considered in this dissertation. This hyperheuristic incorporates multiple metaheuristic sub-algorithms simultaneously for optimization. Testing reveals that the AMALGAM method yields superior results in the majority of problem instances in the test suite, thus achieving the dual goal of raising the level of generality and of yielding improved optimization results. The method has also been implemented in the OSCAR-4 system and is applied to solve several MICFMO case study problem instances, based on two research reactors, in order to demonstrate its practical applicability. Finally, in the third part of this dissertation, a conceptual framework is proposed for an optimization-based personal decision support system, dedicated to MICFM. This framework may serve as the basis for developing a computerized tool to aid nuclear reactor operators in designing suitable reload configurations.AFRIKAANSE OPSOMMING : Die doeltreffendheid en doelmatigheid van brandstofverbruik in 'n tipiese kernreaktor word deur die spesieke rangskikking van beskikbare brandstofelemente in die laaiposisies van die reaktor beinvloed. Hierdie rangskikking staan bekend as 'n brandstof herlaaikongurasie en word gewoonlik opnuut bepaal vir elke operasionele siklus van 'n reaktor. Die gelyktydige optimering van veelvuldige doele word dikwels tydens die ontwerp van 'n herlaaikongurasie nagestreef, veral binne die konteks van navorsingsreaktore. Die doelwit van meerdoelige binne-kern brandstofbeheeroptimering (MBKBBO) is om 'n Pareto optimale versameling van herlaaikongurasieafruilings te identiseer. So 'n versameling mag dan vir oorweging (deur byvoorbeeld 'n kernreaktoroperateur) voorgele word sodat 'n voorkeurkongurasie gekies kan word. In die eerste gedeelte van hierdie proefskrif word 'n skalariseringsgebaseerde metodologie vir MBKBBO voorgestel om verskeie tekortkominge in die gewilde gewigverswaringsmetode aan te spreek. Laasgenoemde metode word gereeld in die literatuur gebruik om die MBKBBO probleem op te los. Die voorgestelde metodologie is in 'n reaktorsimulasiestelsel, bekend as die OSCAR-4 stelsel, geimplementeer. Om die praktiese toepasbaarheid daarvan te demonstreer, word die metodologie gebruik om 'n aantal MBKBBO probleemgevalle binne die konteks van twee navorsingsreaktore op te los. In die tweede gedeelte van die proefskrif word 'n uitgebreide ondersoek ingestel om die geskiktheid van verskeie meerdoelige optimeringsalgoritmes vir die oplos van die beperkte MBKBBO probleem te bepaal. Die berekeningstyd wat vir die ondersoek benodig word, word verminder deur die gebruik van kunsmatige neurale netwerke, wat in die proefskrif gekonstrueer word, om doelfunksies en beperkings te evalueer. Agt meerdoelige metaheuristieke word binne die konteks van verskeie MBKBBO toetsprobleemgevalle vergelyk wat op die SAFARI-1 navorsingsreaktor in Suid-Afrika gebaseer is. Toetse dui daarop dat die NSGA-II, die P-ACO algoritme en die MOOCEM oor die algemeen die beste oor al die toetsprobleemgevalle presteer. Die MOVNS algoritme presteer ook goed in die konteks van tweedoelige probleemgevalle. 'n Vermenigvuldigende boetefunksie (VBF) beperkinghanteringstegniek word ook voorgestel en vergelyk met 'n bestaande tegniek bekend as beperkte dominasie. Daar word bevind dat the VBF tegniek 'n mededingende alternatief tot beperkte dominasie is. 'n Poging word aangewend om die vlak van algemeenheid waarmee MBKBBO uitgevoer word, te verhoog, asook om potensieel die kwaliteit van die optimeringsresultate te verbeter. 'n Meerdoelige hiperheuristiek, bekend as die AMALGAM metode, word in die nastreef van hierdie twee doelwitte oorweeg. Die metode funksioneer deur middel van die gelyktydige insluiting van 'n aantal metaheuristieke deel-algoritmes. Toetse dui daarop dat the AMALGAM metode beter resultate vir die meerderheid van toetsprobleme lewer, en dus word die bogenoemde twee doelwitte bereik. Die metode is ook in the OSCAR-4 stelsel ge mplementeer en word gebruik om 'n aantal MBKBBO gevallestudie probleemgevalle (binne die konteks van twee navorsingsreaktore) op te los. Sodoende word die praktiese toepasbaarheid van die metode gedemonstreer. In die derde deel van die proefskrif word 'n konseptuele raamwerk laastens vir 'n optimeringsgebaseerde persoonlike besluitsteunstelsel gemik op MBKBB, voorgestel. Hierdie raamwerk mag as grondslag dien vir die ontwikkeling van 'n gerekenariseerde hulpmiddel vir kernreaktoroperateurs om aanvaarbare herlaaikongurasies te ontwerp.Doctora

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Overview of System Identification with Focus on Inverse Modeling: Literature Review

The intention behind this literature review is to obtain knowledge about the current status in the field of system identification with special focus put on the inverse modelling step. There the parameters for a model are to be determined by taking data obtained from the true system into account. The application in mind is located in geophysics, especially oil reservoir engineering, so special focus is put on methods which are relevant for system identification problems that arise in that context. Nonetheless the review should be interesting for everybody who works on system identification problems.--- Die Intention des Literaturreviews ist eine Übersicht über den Bereich der Systemidentifikation, im speziellen den Bereich der inversen Modellierung, zu erhalten. In diesem Schritt werden Parameter für ein Modell durch Konditionierung auf gemessene Daten eines realen Systems bestimmt. Das Anwendungsgebiet ist im Bereich der Geophysik, im speziellen Erdöl-Reservoirs, angesiedelt. Daher werden besonders die dort genutzten Methoden betrachtet

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment