Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

Algorithms for the weighted independent domination problem

El problema de la dominació independent ponderada és un problema NP-hard d'optimització combinatòria en grafs. Aquest problema només ha estat abordat a la literatura per enfocaments de programació lineal entera, heurístiques voraces i diferents versions d'algoritmes voraços iteratius basats en poblacions. En aquest projecte, primer apliquem una millora sobre les heurístiques voraces existents. Això ho fem implementant les versions rollout d'aquestes heurístiques i provant-les en un marc multistart on són aplicades de forma probabilística. En segon lloc, implementem tres versions d'un algorisme genètic esbiaixat de clau aleatòria. La diferència entre aquestes versions es troba en la forma en què els individus són descodificats en solucions viables del problema. Els resultats mostren que els algorismes desenvolupats poden competir amb els que són estat de l'art en el conjunt d'instàncies relativament petites. No obstant això, amb una mida creixent de les instàncies del problema, els nostres algorismes no poden arribar al nivell dels resultats obtinguts per l'algorisme més punter. Tot i això, els nostres algorismes poden ser millorats de moltes formes diferents, les quals expliquem en detall. Per tant, creiem que els nostres algorismes haurien de ser més estudiats en treballs futurs.The weighted independent domination problem is an NP-hard combinatorial optimization problem in graphs. This problem has only been tackled in the literature by integer linear programming approaches, by Greedy heuristics, and by different versions of a population-based iterated greedy algorithm. In this project, we first improve over the existing Greedy heuristics. This is done by implementing the rollout versions of these heuristics, and by testing them in a multistart framework in which they are applied in a probabilistic way. Second, we implement three versions of a biased random key genetic algorithm. The difference between these versions is found in the way in which individuals are decoded into feasible solutions to the problem. Moreover, we study the rollout versions of the corresponding decoders. Our results show that the developed algorithms can compete with the state of the art in the group of rather small-scale problem instances. However, with a growing size of the problem instances, our algorithms can not quite match the results of the current state-of-the-art algorithm. Nevertheless, our algorithms can potentially be improved in several different ways, which we explain in detail. Therefore, we believe that our algorithms should be further studied in future work

Parameter-less Late Acceptance Hill-climbing: Foundations & Applications.

PhD Theses.Stochastic Local Search (SLS) methods have been used to solve complex hard combinatorial problems in a number of elds. Their judicious use of randomization, arguably, simpli es their design to achieve robust algorithm behaviour in domains where little is known. This feature makes them a general purpose approach for tackling complex problems. However, their performance, usually, depends on a number of parameters that should be speci ed by the user. Most of these parameters are search-algorithm related and have little to do with the user's problem. This thesis presents search techniques for combinatorial problems that have fewer parameters while delivering good anytime performance. Their parameters are set automatically by the algorithm itself in an intelligent way, while making sure that they use the entire given time budget to explore the search space with a high probability of avoiding the stagnation in a single basin of attraction. These algorithms are suitable for general practitioners in industry that do not have deep insight into search methodologies and their parameter tuning. Note that, to all intents and purposes, in realworld search problems the aim is to nd a good enough quality solution in a pre-de ned time. In order to achieve this, we use a technique that was originally introduced for automating population sizing in evolutionary algorithms. In an intelligent way, we adapted it to a particular one-point stochastic local search algorithm, namely Late Acceptance Hill-Climbing (LAHC), to eliminate the need to manually specify the value of the sole parameter of this algorithm. We then develop a mathematically sound dynamic cuto time strategy that is able to reliably detect the stagnation point for these search algorithms. We evaluated the suitability and scalability of the proposed methods on a range of classical combinatorial optimization problems as well as a real-world software engineering proble

A Heuristically Generated Metric Approach to the Solution of Chase Problem

In this work, heuristic, hyper-heuristic, and metaheuristic approaches are reviewed. Distance metrics are also examined to solve the “puzzle problems by searching” in AI. A viewpoint is brought by introducing the so-called Heuristically Generated Angular Metric Approach (HAMA) through the explanation of the metrics world. Distance metrics are applied to “cat and mouse” problem where cat and mouse makes smart moves relative to each other and therefore makes more appropriate decisions. The design is built around Fuzzy logic control to determine route finding between the pursuer and prey. As the puzzle size increases, the effect of HAMA can be distinguished more clearly in terms of computation time towards a solution. Hence, mouse will gain more time in perceiving the incoming danger, thus increasing the percentage of evading the danger. ‘Caught and escape percentages vs. number of cats’ for three distance metrics have been created and the results evaluated comparatively. Given three termination criteria, it is never inconsistent to define two different objective functions: either the cat travels the distance to catch the mouse, or the mouse increases the percentage of escape from the cat

Learning Methods for Variable Selection and Time Series Prediction

In the recent years, machine learning methods have become increasingly popular for modelling many different phenomena: financial markets, spatio-temporal data sets, pattern recognition, speech and image processing, recommender systems and many others. This huge interest in machine learning comes from the great success of their application and the increasingly easier acquisition, storage and access of data. In this thesis, two general problems in machine learning are discussed and several solutions are offered. The first problem is variable selection, an approach to automatically select the most relevant features in the data. Two key phases of variable selection are the search criterion and the search algorithm. The thesis focuses on the Delta test as a search criterion, while several solutions are offered for the search algorithm, such as the Genetic Algorithm and Tabu Search. Furthermore, the selection procedure is extended for more general cases of scaling and projection, as well as their combination. Finally, some of the above proposed solutions have been developed for parallel architectures which enable the whole variable selection procedure to be used for data sets with a high number of features. The second problem tackled in the thesis is time series prediction that arises in many fields of science and industry. In simple words: time series prediction involves the estimation of future values for a series of measurements of a/the phenomenon of interest. The number of these estimations can be small, leading to short-term prediction, or several hundreds which constitute long-term prediction. Two models have been developed for this particular task. One is based on a recently popular neural network type called Extreme Learning Machine, while the other is a juxtaposition of Generative Topographic Mapping and Relevance Learning modified for regression tasks. Finally, the above problems are tackled together for real-world time series coming from a biological domain. The difficulty of making any kind of inference in biological time series is due to really small amount of available samples, irregular sampling frequency and spatial coverage of areas of interest. Nevertheless, more stable model parameter estimation is possible with the combined use of global climate indicators and regional measurements in the form of a multifactor approach.Peer reviewe

Optimization of storage and picking systems in warehouses

La croissance du commerce électronique exige une hausse des performances des systèmes d'entreposage, qui sont maintenant repensés pour faire face à un volume massif de demandes à être satisfait le plus rapidement possible. Le système manuel et le système à robots mobile (SRM) sont parmi les plus utilisés pour ces activités. Le premier est un système centré sur l'humain pour réaliser des opérations complexes que les robots actuels ne peuvent pas effectuer. Cependant, les nouvelles générations de robots autonomes mènent à un remplacement progressif par le dernier pour augmenter la productivité. Quel que soit le système utilisé, plusieurs problèmes interdépendants doivent être résolus pour avoir des processus de stockage et de prélèvement efficaces. Les problèmes de stockage concernent les décisions d'où stocker les produits dans l'entrepôt. Les problèmes de prélèvement incluent le regroupement des commandes à exécuter ensemble et les itinéraires que les cueilleurs et les robots doivent suivre pour récupérer les produits demandés. Dans le système manuel, ces problèmes sont traditionnellement résolus à l'aide de politiques simples que les préparateurs peuvent facilement suivre. Malgré l'utilisation de robots, la même stratégie de solution est répliquée aux problèmes équivalents trouvés dans le SRM. Dans cette recherche, nous étudions les problèmes de stockage et de prélèvement rencontrés lors de la conception du système manuel et du SRM. Nous développons des outils d'optimisation pour aider à la prise de décision pour mettre en place leurs processus, en améliorant les mesures de performance typiques de ces systèmes. Certains problèmes traditionnels sont résolus avec des techniques améliorées, tandis que d'autres sont intégrés pour être résolus ensemble au lieu d'optimiser chaque sous-système de manière indépendante. Nous considérons d'abord un système manuel avec un ensemble connu de commandes et intégrons les décisions de stockage et de routage. Le problème intégré et certaines variantes tenant compte des politiques de routage communes sont modélisés mathématiquement. Une métaheuristique générale de recherche de voisinage variable est présentée pour traiter des instances de taille réelle. Des expériences attestent de l'efficience de la métaheuristique proposée par rapport aux modèles exacts et aux politiques de stockage communes. Lorsque les demandes futures sont incertaines, il est courant d'utiliser une stratégie de zonage qui divise la zone de stockage en zones et attribue les produits les plus demandés aux meilleures zones. Les tailles des zones sont à déterminer. Généralement, des dimensions arbitraires sont choisies, mais elles ignorent les caractéristiques de l'entrepôt et des demandes. Nous abordons le problème de dimensionnement des zones pour déterminer quels facteurs sont pertinents pour choisir de meilleures tailles de zone. Les données générées à partir de simulations exhaustives sont utilisées pour trainer quatre modèles de régression d'apprentissage automatique - moindres carrés ordinaire, arbre de régression, forêt aléatoire et perceptron multicouche - afin de prédire les dimensions optimales des zones en fonction de l'ensemble de facteurs pertinents identifiés. Nous montrons que tous les modèles entraînés suggèrent des dimensions sur mesure des zones qui performent meilleur que les dimensions arbitraires couramment utilisées. Une autre approche pour résoudre les problèmes de stockage pour le système manuel et pour le SRM considère les corrélations entre les produits. L'idée est que les produits régulièrement demandés ensemble doivent être stockés près pour réduire les coûts de routage. Cette politique de stockage peut être modélisée comme une variante du problème d'affectation quadratique (PAQ). Le PAQ est un problème combinatoire traditionnel et l'un des plus difficiles à résoudre. Nous examinons les variantes les plus connues du PAQ et développons une puissante métaheuristique itérative de recherche tabou mémétique en parallèle capable de les résoudre. La métaheuristique proposée s'avère être parmi les plus performantes pour le PAQ et surpasse considérablement l'état de l'art pour ses variantes. Les SRM permettent de repositionner facilement les pods d'inventaire pendant les opérations, ce qui peut conduire à un processus de prélèvement plus économe en énergie. Nous intégrons les décisions de repositionnement des pods à l'attribution des commandes et à la sélection des pods à l'aide d'une stratégie de prélèvement par vague. Les pods sont réorganisés en tenant compte du moment et de l'endroit où ils devraient être demandés au futur. Nous résolvons ce problème en utilisant la programmation stochastique en tenant compte de l'incertitude sur les demandes futures et suggérons une matheuristique de recherche locale pour résoudre des instances de taille réelle. Nous montrons que notre schéma d'approximation moyenne de l'échantillon est efficace pour simuler les demandes futures puisque nos méthodes améliorent les solutions trouvées lorsque les vagues sont planifiées sans tenir compte de l'avenir. Cette thèse est structurée comme suit. Après un chapitre d'introduction, nous présentons une revue de la littérature sur le système manuel et le SRM, et les décisions communes prises pour mettre en place leurs processus de stockage et de prélèvement. Les quatre chapitres suivants détaillent les études pour le problème de stockage et de routage intégré, le problème de dimensionnement des zones, le PAQ et le problème de repositionnement de pod. Nos conclusions sont résumées dans le dernier chapitre.The rising of e-commerce is demanding an increase in the performance of warehousing systems, which are being redesigned to deal with a mass volume of demands to be fulfilled as fast as possible. The manual system and the robotic mobile fulfillment system (RMFS) are among the most commonly used for these activities. The former is a human-centered system that handles complex operations that current robots cannot perform. However, newer generations of autonomous robots are leading to a gradual replacement by the latter to increase productivity. Regardless of the system used, several interdependent problems have to be solved to have efficient storage and picking processes. Storage problems concern decisions on where to store products within the warehouse. Picking problems include the batching of orders to be fulfilled together and the routes the pickers and robots should follow to retrieve the products demanded. In the manual system, these problems are traditionally solved using simple policies that pickers can easily follow. Despite using robots, the same solution strategy is being replicated to the equivalent problems found in the RMFS. In this research, we investigate storage and picking problems faced when designing manual and RMFS warehouses. We develop optimization tools to help in the decision-making process to set up their processes and improve typical performance measures considered in these systems. Some classic problems are solved with improved techniques, while others are integrated to be solved together instead of optimizing each subsystem sequentially. We first consider a manual system with a known set of orders and integrate storage and routing decisions. The integrated problem and some variants considering common routing policies are modeled mathematically. A general variable neighborhood search metaheuristic is presented to deal with real-size instances. Computational experiments attest to the effectiveness of the metaheuristic proposed compared to the exact models and common storage policies. When future demands are uncertain, it is common to use a zoning strategy to divide the storage area into zones and assign the most-demanded products to the best zones. Zone sizes are to be determined. Commonly, arbitrary sizes are chosen, which ignore the characteristics of the warehouse and the demands. We approach the zone sizing problem to determine which factors are relevant to choosing better zone sizes. Data generated from exhaustive simulations are used to train four machine learning regression models - ordinary least squares, regression tree, random forest, and multilayer perceptron - to predict the optimal zone sizes given the set of relevant factors identified. We show that all trained models suggest tailor-made zone sizes with better picking performance than the arbitrary ones commonly used. Another approach to solving storage problems, both in the manual and RMFS, considers the correlations between products. The idea is that products constantly demanded together should be stored closer to reduce routing costs. This storage policy can be modeled as a quadratic assignment problem (QAP) variant. The QAP is a traditional combinatorial problem and one of the hardest to solve. We survey the most traditional QAP variants and develop a powerful parallel memetic iterated tabu search metaheuristic capable of solving them. The proposed metaheuristic is shown to be among the best performing ones for the QAP and significantly outperforms the state-of-the-art for its variants. The RMFS allows easy repositioning of inventory pods during operations that can lead to a more energy-efficient picking process. We integrate pod repositioning decisions with order assignment and pod selection using a wave picking strategy such that pods are parked after being requested considering when and where they are expected to be requested next. We solve this integrated problem using stochastic programming considering the uncertainty about future demands and suggest a local search matheuristic to solve real-size instances. We show that our sample average approximation scheme is effective to simulate future demands since our methods improve solutions found when waves are planned without considering the future demands. This thesis is structured as follows. After an introductory chapter, we present a literature review on the manual and RMFS, and common decisions made to set up their storage and picking processes. The next four chapters detail the studies for the integrated storage and routing problem, the zone sizing problem, the QAP, and the pod repositioning problem. Our findings are summarized in the last chapter

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

ASALBP: the Alternative Subgraphs Assembly Line Balancing Problem. Formalization and Resolution Procedures

Hoy en día, los problemas de equilibrado de líneas de montaje se encuentran comúnmente en la mayoría de sistemas industriales y de manufactura. Básicamente, estos problemas consisten en asignar un conjunto de tareas a una secuencia ordenada de estaciones de trabajo, de manera que se respeten las restricciones de precedencia y se optimice una medida de eficiencia dada (como, por ejemplo, el número de estaciones de trabajo o el tiempo ciclo). Dada la complejidad de los problemas de equilibrado de líneas, en los trabajos de investigación tradicionalmente se consideraban numerosas simplificaciones en las que, por ejemplo, una sola línea serial procesaba un único modelo de un solo producto. Además, los problemas estaban principalmente restringidos por las relaciones de precedencia y el tiempo ciclo. Sin embargo, la disponibilidad de recursos computacionales de hoy en día, así como la necesidad de las empresas a adaptarse a los rápidos cambios en los procesos de producción, han motivado tanto a investigadores como a gerentes a tratar problemas más realistas. Algunos ejemplos incluyen problemas que procesan modelos mixtos, estaciones de trabajo y líneas en paralelo, consideran múltiples objetivos y restricciones adicionales, como la capacidad de proceso de las estaciones de trabajo y la ubicación de los recursos en la línea de montaje.Esta tesis doctoral trata un nuevo problema de equilibrado de líneas, que ha sido titulado ASALBP: the Alternative Subgraphs Assembly Line Balancing Problem, en el que se consideran variantes alternativas para diferentes partes de un proceso de montaje o de manufactura. Cada alternativa puede ser representada por un subgrafo de precedencias, que determina las tareas requeridas para procesar un producto particular, las restricciones de precedencia y los tiempos de proceso. Para resolver eficientemente el ASALBP, se deben resolver dos problemas simultáneamente: (1) el problema de decisión para seleccionar un subgrafo de montaje para cada parte que admite alternativas y (2) el problema de equilibrado para asignar las tareas a las estaciones de trabajo. El análisis del estado del arte revela que este problema no ha sido estudiado previamente en la literatura, lo que ha conducido a la caracterización y a la definición de un nuevo problema. Por otra parte, dado que no es posible representar las variantes de montaje en un diagrama de precedencias estándar, se propone el S-grafo como una herramienta de diagramación, para representar en un único grafo todas las alternativas de montaje.Habitualmente, los problemas de equilibrado de líneas que consideran alternativas de montaje se resuelven en dos etapas. En la etapa inicial, el diseñador de sistema selecciona una de las variantes posibles utilizando cierto criterio de decisión como por ejemplo tiempo total de proceso. Una vez que se han seleccionado las alternativas de montaje, y se dispone de un diagrama de precedencias (es decir, el problema de planificación ha sido resuelto), la línea de montaje es equilibrada en una segunda etapa. Sin embargo, utilizando dicho procedimiento de dos etapas no se puede garantizar que una solución óptima del problema global se pueda obtener, porque las decisiones tomadas por el diseñador de sistema restringen el problema y causan perdida de información; es decir, cuando se selecciona una alternativa priori los efectos de las posibilidades restantes quedan sin explorar. Por ejemplo, si el diseñador de sistema utiliza tiempo total de proceso como criterio de decisión, la alternativa con el tiempo total de proceso más grande será descartada a pesar de que pueda ser la que proporcione la mejor solución del problema (es decir, requiere el mínimo número de estaciones de trabajo o el mínimo tiempo ciclo). Por lo tanto, pareciera razonable considerar que para solucionar eficientemente un ALBP que implica alternativas de proceso, todas las alternativas de montaje deben ser tomadas en cuenta en el proceso de equilibrado. Para este propósito, en esta tesis el problema de selección de una variante de montaje y el problema de equilibrado de la línea se consideran conjuntamente en lugar de independientemente.Para resolver el Problema de Equilibrado de Líneas con Alternativas de Montaje (ASALBP) se usan varios enfoques. El problema se formaliza y se resuelve de manera óptima a través de dos modelos de programación matemática. Un enfoque aproximativo es usado para resolver problemas de tamaño industrial. Además, se proponen procedimientos de optimización local con el objetivo de mejorar la calidad de las soluciones obtenidas por los métodos heurísticos desarrollados en este trabajo.Nowadays assembly line balancing problems are commonly found in most industrial and manufacturing systems. Basically, these problems seek to assign a set of assembly tasks to an ordered sequence of workstations in such a way that precedence constraints are maintained and a given efficiency measure (e.g. the number of workstations or the cycle time) is optimized.Because of the computational complexity of balancing problems, research works traditionally considered numerous simplifying assumptions in which, for example, a single model of a unique product were processed in a single line; moreover, problems were mainly restricted by precedence and cycle time constrains. Nevertheless, the current availability of computing resources and the enterprises need to adapt to rapid changes in production and manufacturing processes have encouraged researchers and decision-makers to address more realistic problems. Some examples include problems that involve mixed models, parallel workstations and parallel lines, multiple objectives and also further restrictions such as workstation processing capacity and resource allocation constraints. This doctoral thesis addresses a novel assembly line balancing problem, entitled here ASALBP: the Alternative Subgraphs Assembly Line Balancing Problem, which considers alternative variants for different parts of an assembly or manufacturing process. Each variant can be represented by a precedence subgraph that establishes the tasks required to process a particular product, their precedence requirements and their processing times. Therefore, to efficiently solve the Alternative Subgraphs Assembly Line Balancing Problem two subproblems need to be solved simultaneously: (1) the decision problem that selects one assembly variant for each part that admit alternatives and (2) the balancing problem that assigns the tasks to the workstations. The analysis of the state-of-the-art carried out revealed that the Alternative Subgraphs Assembly Line Balancing Problem has not been addressed before in literature studies, which leaded to the characterization and definition of this new problem. Moreover, due to the impossibility of representing assembly variants in a standard precedence graph, the S-Graph is proposed here as a diagramming tool to represent all available assembly alternatives in a unique diagram. Habitually, problems involving assembly alternatives are solved by using a two-stage based approach. In the initial stage, the system designer selects one of the possible variants according to criteria such as total processing time. Once the assembly alternatives have been selected, and a precedence graph is available (i.e. the assembly planning problem has been already solved), the line is then balanced in the second stage. However, by following this two-stage procedure it cannot be guaranteed that an optimal solution of the global problem can be obtained, because the decisions taken by the system designer restrict the problem and cause information loss; i.e., a priori selection of an alternative leaves the effects of the other possibilities unexplored. For instance, if the system designer uses total processing time as decision criterion, the alternative with largest total processing time will be discarded notwithstanding it may provide the best solution of the problem (i.e., it requires the minimum number of workstations or minimum cycle time). Therefore, it seems reasonable to consider that to solve efficiently an ALBP that involves processing alternatives all possibilities must be considered within the balancing process. For this purpose, in this thesis both the variant selection problem and the balancing problem are jointly considered instead of independently.Different approaches are used here to address the Alternative Subgraphs Assembly Line Balancing Problem (ASALBP). The problem is formalize and optimally solved by means of two mathematical programming models. An approximate approach is used to address industrial-scale problems. Furthermore, local optimization procedures are proposed aiming at improving the quality of the solutions provided by all heuristic methods developed here