An Evolutionary Algorithm for solving the Two-Dimensional Irregular Shape Packing Problem combined with the Knapsack Problem

This work presents an evolutionary algorithm to solve a joint problem of the Packing Problem and the Knapsack Problem, where the objective is to place items (with shape, value and weight) in a container (defined by its shape and capacity), maximizing the container's value, without intersections

Bio-Inspired Computing For Complex And Dynamic Constrained Problems

Bio-inspired algorithms are general-purpose optimisation methods that can find solutions with high qualities for complex problems. They are able to find these solutions with minimal knowledge of a search space. Bio-inspired algorithms (the design of which is inspired by nature) can easily adapt to changing environments. In this thesis, we contribute to the theoretical and empirical understanding of bioinspired algorithms, such as evolutionary algorithms and ant colony optimisation. We address complex problems as well as problems with dynamically changing constraints. Firstly, we review the most recent achievements in the theoretical analysis of dynamic optimisation via bio-inspired algorithms. We then continue our investigations in two major areas: static and dynamic combinatorial problems. To tackle static problems, we study the evolutionary algorithms that are enhanced by using a knowledge-based mutation approach in solving single- and multi-objective minimum spanning tree (MST) problems. Our results show that proper development of biased mutation can significantly improve the performance of evolutionary algorithms. Afterwards, we analyse the ability of single- and multi-objective algorithms to solve the packing while travelling (PWT) problem. This NP-hard problem is chosen to represent real-world multi-component problems. We outline the limitations of randomised local search in solving PWT and prove the advantage of using evolutionary algorithms. Our dynamic investigations begin with an empirical analysis of the ability of simple and advanced evolutionary algorithms to optimise the dynamic knapsack (KP) problem. We show that while optimising a population of solutions can speed up the ability of an algorithm to find optimal solutions after a dynamic change, it has the exact opposite effect in environments with high-frequency changes. Finally, we investigate the dynamic version of a more general problem known as the subset selection problem. We prove the inability of the adaptive greedy approach to maintain quality solutions in dynamic environments and illustrate the advantage of using evolutionary algorithms theoretically and practically.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

The germinal centre artificial immune system

This thesis deals with the development and evaluation of the Germinal centre artificial immune system (GC-AIS) which is a novel artificial immune system based on advancements in the understanding of the germinal centre reaction of the immune system. The key research questions addressed in this thesis are: can an artificial immune system (AIS) be designed by taking inspiration from recent developments in immunology to tackle multi-objective optimisation problems? How can we incorporate desirable features of the immune system like diversity, parallelism and memory into this proposed AIS? How does the proposed AIS compare with other state of the art techniques in the field of multi-objective optimisation problems? How can we incorporate the learning component of the immune system into the algorithm and investigate the usefulness of memory in dynamic scenarios? The main contributions of the thesis are: • Understanding the behaviour and performance of the proposed GC-AIS on multiobjective optimisation problems and explaining its benefits and drawbacks, by comparing it with simple baseline and state of the art algorithms. • Improving the performance of GC-AIS by incorporating a popular technique from multi-objective optimisation. By overcoming its weaknesses the capability of the improved variant to compete with the state of the art algorithms is evaluated. • Answering key questions on the usefulness of incorporating memory in GC-AIS in a dynamic scenario

Parallel optimization algorithms for high performance computing : application to thermal systems

The need of optimization is present in every field of engineering. Moreover, applications requiring a multidisciplinary approach in order to make a step forward are increasing. This leads to the need of solving complex optimization problems that exceed the capacity of human brain or intuition. A standard way of proceeding is to use evolutionary algorithms, among which genetic algorithms hold a prominent place. These are characterized by their robustness and versatility, as well as their high computational cost and low convergence speed. Many optimization packages are available under free software licenses and are representative of the current state of the art in optimization technology. However, the ability of optimization algorithms to adapt to massively parallel computers reaching satisfactory efficiency levels is still an open issue. Even packages suited for multilevel parallelism encounter difficulties when dealing with objective functions involving long and variable simulation times. This variability is common in Computational Fluid Dynamics and Heat Transfer (CFD & HT), nonlinear mechanics, etc. and is nowadays a dominant concern for large scale applications. Current research in improving the performance of evolutionary algorithms is mainly focused on developing new search algorithms. Nevertheless, there is a vast knowledge of sequential well-performing algorithmic suitable for being implemented in parallel computers. The gap to be covered is efficient parallelization. Moreover, advances in the research of both new search algorithms and efficient parallelization are additive, so that the enhancement of current state of the art optimization software can be accelerated if both fronts are tackled simultaneously. The motivation of this Doctoral Thesis is to make a step forward towards the successful integration of Optimization and High Performance Computing capabilities, which has the potential to boost technological development by providing better designs, shortening product development times and minimizing the required resources. After conducting a thorough state of the art study of the mathematical optimization techniques available to date, a generic mathematical optimization tool has been developed putting a special focus on the application of the library to the field of Computational Fluid Dynamics and Heat Transfer (CFD & HT). Then the main shortcomings of the standard parallelization strategies available for genetic algorithms and similar population-based optimization methods have been analyzed. Computational load imbalance has been identified to be the key point causing the degradation of the optimization algorithm¿s scalability (i.e. parallel efficiency) in case the average makespan of the batch of individuals is greater than the average time required by the optimizer for performing inter-processor communications. It occurs because processors are often unable to finish the evaluation of their queue of individuals simultaneously and need to be synchronized before the next batch of individuals is created. Consequently, the computational load imbalance is translated into idle time in some processors. Several load balancing algorithms have been proposed and exhaustively tested, being extendable to any other population-based optimization method that needs to synchronize all processors after the evaluation of each batch of individuals. Finally, a real-world engineering application that consists on optimizing the refrigeration system of a power electronic device has been presented as an illustrative example in which the use of the proposed load balancing algorithms is able to reduce the simulation time required by the optimization tool.El aumento de las aplicaciones que requieren de una aproximación multidisciplinar para poder avanzar se constata en todos los campos de la ingeniería, lo cual conlleva la necesidad de resolver problemas de optimización complejos que exceden la capacidad del cerebro humano o de la intuición. En estos casos es habitual el uso de algoritmos evolutivos, principalmente de los algoritmos genéticos, caracterizados por su robustez y versatilidad, así como por su gran coste computacional y baja velocidad de convergencia. La multitud de paquetes de optimización disponibles con licencias de software libre representan el estado del arte actual en tecnología de optimización. Sin embargo, la capacidad de adaptación de los algoritmos de optimización a ordenadores masivamente paralelos alcanzando niveles de eficiencia satisfactorios es todavía una tarea pendiente. Incluso los paquetes adaptados al paralelismo multinivel tienen dificultades para gestionar funciones objetivo que requieren de tiempos de simulación largos y variables. Esta variabilidad es común en la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT), mecánica no lineal, etc. y es una de las principales preocupaciones en aplicaciones a gran escala a día de hoy. La investigación actual que tiene por objetivo la mejora del rendimiento de los algoritmos evolutivos está enfocada principalmente al desarrollo de nuevos algoritmos de búsqueda. Sin embargo, ya se conoce una gran variedad de algoritmos secuenciales apropiados para su implementación en ordenadores paralelos. La tarea pendiente es conseguir una paralelización eficiente. Además, los avances en la investigación de nuevos algoritmos de búsqueda y la paralelización son aditivos, por lo que el proceso de mejora del software de optimización actual se verá incrementada si se atacan ambos frentes simultáneamente. La motivación de esta Tesis Doctoral es avanzar hacia una integración completa de las capacidades de Optimización y Computación de Alto Rendimiento para así impulsar el desarrollo tecnológico proporcionando mejores diseños, acortando los tiempos de desarrollo del producto y minimizando los recursos necesarios. Tras un exhaustivo estudio del estado del arte de las técnicas de optimización matemática disponibles a día de hoy, se ha diseñado una librería de optimización orientada al campo de la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT). A continuación se han analizado las principales limitaciones de las estrategias de paralelización disponibles para algoritmos genéticos y otros métodos de optimización basados en poblaciones. En el caso en que el tiempo de evaluación medio de la tanda de individuos sea mayor que el tiempo medio que necesita el optimizador para llevar a cabo comunicaciones entre procesadores, se ha detectado que la causa principal de la degradación de la escalabilidad o eficiencia paralela del algoritmo de optimización es el desequilibrio de la carga computacional. El motivo es que a menudo los procesadores no terminan de evaluar su cola de individuos simultáneamente y deben sincronizarse antes de que se cree la siguiente tanda de individuos. Por consiguiente, el desequilibrio de la carga computacional se convierte en tiempo de inactividad en algunos procesadores. Se han propuesto y testado exhaustivamente varios algoritmos de equilibrado de carga aplicables a cualquier método de optimización basado en una población que necesite sincronizar los procesadores tras cada tanda de evaluaciones. Finalmente, se ha presentado como ejemplo ilustrativo un caso real de ingeniería que consiste en optimizar el sistema de refrigeración de un dispositivo de electrónica de potencia. En él queda demostrado que el uso de los algoritmos de equilibrado de carga computacional propuestos es capaz de reducir el tiempo de simulación que necesita la herramienta de optimización

Data Placement And Task Mapping Optimization For Big Data Workflows In The Cloud

Data-centric workflows naturally process and analyze a huge volume of datasets. In this new era of Big Data there is a growing need to enable data-centric workflows to perform computations at a scale far exceeding a single workstation\u27s capabilities. Therefore, this type of applications can benefit from distributed high performance computing (HPC) infrastructures like cluster, grid or cloud computing. Although data-centric workflows have been applied extensively to structure complex scientific data analysis processes, they fail to address the big data challenges as well as leverage the capability of dynamic resource provisioning in the Cloud. The concept of “big data workflows” is proposed by our research group as the next generation of data-centric workflow technologies to address the limitations of exist-ing workflows technologies in addressing big data challenges. Executing big data workflows in the Cloud is a challenging problem as work-flow tasks and data are required to be partitioned, distributed and assigned to the cloud execution sites (multiple virtual machines). In running such big data work-flows in the cloud distributed across several physical locations, the workflow execution time and the cloud resource utilization efficiency highly depends on the initial placement and distribution of the workflow tasks and datasets across the multiple virtual machines in the Cloud. Several workflow management systems have been developed for scientists to facilitate the use of workflows; however, data and work-flow task placement issue has not been sufficiently addressed yet. In this dissertation, I propose BDAP strategy (Big Data Placement strategy) for data placement and TPS (Task Placement Strategy) for task placement, which improve workflow performance by minimizing data movement across multiple virtual machines in the Cloud during the workflow execution. In addition, I propose CATS (Cultural Algorithm Task Scheduling) for workflow scheduling, which improve workflow performance by minimizing workflow execution cost. In this dissertation, I 1) formalize data and task placement problems in workflows, 2) propose a data placement algorithm that considers both initial input dataset and intermediate datasets obtained during workflow run, 3) propose a task placement algorithm that considers placement of workflow tasks before workflow run, 4) propose a workflow scheduling strategy to minimize the workflow execution cost once the deadline is provided by user and 5)perform extensive experiments in the distributed environment to validate that our proposed strategies provide an effective data and task placement solution to distribute and place big datasets and tasks into the appropriate virtual machines in the Cloud within reasonable time

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Model and solutions to campus parking space allocation problem.

M. Sc. University of KwaZulu-Natal, Durban 2013.Parking is considered a major land use challenge in campus planning. The problem can be in terms of scarcity (few available spaces compared to demand) or management (ineffi cient usage of available facilities). Many studies have looked at the parking problem from the administrative and management points of view. However, it is believed that mathematical models and optimiza- tion can provide substantial solution to the parking problem. This study investigates a model for allocating car parking spaces in the university environment and improves on the constraints to address the reserved parking policy on campus. An investigation of both the exact and heuristic techniques was undergone to provide solutions to this model with a case study of the University of KwaZulu-Natal (UKZN), Westville Campus. The optimization model was tested with four different set of data that were generated to mimic real life situations of parking supply and demand on campus for reserved and unreserved parking spaces. These datasets consist of the number of parking lots and offi ce buildings in the case study. The study also investigate some optimization algorithms that can be used to obtain solutions to this problem. An exact solution of the model was generated with CPLEX solver (as incorporated in AIMMS software). Further investigation of the performance of the three meta-heuristics to solve this problem was done. A comparative study of the performance of these techniques was conducted. Results obtained from the meta-heuristic algorithms indicate that the algorithms used can successfully solve the parking allocation problem and can give solutions that are near optimal. The parking allocation and fitness value for each of the meta-heuristic algorithms on the sets of data used were obtained and compared to each other and also to the ones obtained from CPLEX solver. The results suggest that PSwarm performs better and faster than the other two algorithms and gives solutions that are close to the exact solutions obtained from CPLEX solver

Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

A quantum behaved particle swarm approach to multi-objective optimization

Many real-world optimization problems have multiple objectives that have to be optimized simultaneously. Although a great deal of effort has been devoted to solve multi-objective optimization problems, the problem is still open and the related issues still attract significant research efforts. Quantum-behaved Particle Swarm Optimization (QPSO) is a recently proposed population based metaheuristic that relies on quantum mechanics principles. Since its inception, much effort has been devoted to develop improved versions of QPSO designed for single objective optimization. However, many of its advantages are not yet available for multi-objective optimization. In this thesis, we develop a new framework for multi-objective problems using QPSO. The contribution of the work is threefold. First a hybrid leader selection method has been developed to compute the attractor of a given particle. Second, an archiving strategy has been proposed to control the growth of the archive size. Third, the developed framework has been further extended to handle constrained optimization problems. A comprehensive investigation of the developed framework has been carried out under different selection, archiving and constraint handling strategies. The developed framework is found to be a competitive technique to tackle this type of problems when compared against the state-of-the-art methods in multi-objective optimization