A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Energy-efficient routing protocols in heterogeneous wireless sensor networks

Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints and limited battery energy. The usage of cheap and tiny wireless sensors will allow very large networks to be deployed at a feasible cost to provide a bridge between information systems and the physical world. Such large-scale deployments will require routing protocols that scale to large network sizes in an energy-efficient way. This thesis addresses the design of such network routing methods. A classification of existing routing protocols and the key factors in their design (i.e., hardware, topology, applications) provides the motivation for the new three-tier architecture for heterogeneous networks built upon a generic software framework (GSF). A range of new routing algorithms have hence been developed with the design goals of scalability and energy-efficient performance of network protocols. They are respectively TinyReg - a routing algorithm based on regular-graph theory, TSEP - topological stable election protocol, and GAAC - an evolutionary algorithm based on genetic algorithms and ant colony algorithms. The design principle of our routing algorithms is that shortening the distance between the cluster-heads and the sink in the network, will minimise energy consumption in order to extend the network lifetime, will achieve energy efficiency. Their performance has been evaluated by simulation in an extensive range of scenarios, and compared to existing algorithms. It is shown that the newly proposed algorithms allow long-term continuous data collection in large networks, offering greater network longevity than existing solutions. These results confirm the validity of the GSF as an architectural approach to the deployment of large wireless sensor networks

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

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.Ingeniería, Industria y Construcció

Optimización de algoritmos bioinspirados en sistemas heterogéneos CPU-GPU.

Los retos científicos del siglo XXI precisan del tratamiento y análisis de una ingente cantidad de información en la conocida como la era del Big Data. Los futuros avances en distintos sectores de la sociedad como la medicina, la ingeniería o la producción eficiente de energía, por mencionar sólo unos ejemplos, están supeditados al crecimiento continuo en la potencia computacional de los computadores modernos. Sin embargo, la estela de este crecimiento computacional, guiado tradicionalmente por la conocida “Ley de Moore”, se ha visto comprometido en las últimas décadas debido, principalmente, a las limitaciones físicas del silicio. Los arquitectos de computadores han desarrollado numerosas contribuciones multicore, manycore, heterogeneidad, dark silicon, etc, para tratar de paliar esta ralentización computacional, dejando en segundo plano otros factores fundamentales en la resolución de problemas como la programabilidad, la fiabilidad, la precisión, etc. El desarrollo de software, sin embargo, ha seguido un camino totalmente opuesto, donde la facilidad de programación a través de modelos de abstracción, la depuración automática de código para evitar efectos no deseados y la puesta en producción son claves para una viabilidad económica y eficiencia del sector empresarial digital. Esta vía compromete, en muchas ocasiones, el rendimiento de las propias aplicaciones; consecuencia totalmente inadmisible en el contexto científico. En esta tesis doctoral tiene como hipótesis de partida reducir las distancias entre los campos hardware y software para contribuir a solucionar los retos científicos del siglo XXI. El desarrollo de hardware está marcado por la consolidación de los procesadores orientados al paralelismo masivo de datos, principalmente GPUs Graphic Processing Unit y procesadores vectoriales, que se combinan entre sí para construir procesadores o computadores heterogéneos HSA. En concreto, nos centramos en la utilización de GPUs para acelerar aplicaciones científicas. Las GPUs se han situado como una de las plataformas con mayor proyección para la implementación de algoritmos que simulan problemas científicos complejos. Desde su nacimiento, la trayectoria y la historia de las tarjetas gráficas ha estado marcada por el mundo de los videojuegos, alcanzando altísimas cotas de popularidad según se conseguía más realismo en este área. Un hito importante ocurrió en 2006, cuando NVIDIA (empresa líder en la fabricación de tarjetas gráficas) lograba hacerse con un hueco en el mundo de la computación de altas prestaciones y en el mundo de la investigación con el desarrollo de CUDA “Compute Unified Device Arquitecture. Esta arquitectura posibilita el uso de la GPU para el desarrollo de aplicaciones científicas de manera versátil. A pesar de la importancia de la GPU, es interesante la mejora que se puede producir mediante su utilización conjunta con la CPU, lo que nos lleva a introducir los sistemas heterogéneos tal y como detalla el título de este trabajo. Es en entornos heterogéneos CPU-GPU donde estos rendimientos alcanzan sus cotas máximas, ya que no sólo las GPUs soportan el cómputo científico de los investigadores, sino que es en un sistema heterogéneo combinando diferentes tipos de procesadores donde podemos alcanzar mayor rendimiento. En este entorno no se pretende competir entre procesadores, sino al contrario, cada arquitectura se especializa en aquella parte donde puede explotar mejor sus capacidades. Donde mayor rendimiento se alcanza es en estos clústeres heterogéneos, donde múltiples nodos son interconectados entre sí, pudiendo dichos nodos diferenciarse no sólo entre arquitecturas CPU-GPU, sino también en las capacidades computacionales dentro de estas arquitecturas. Con este tipo de escenarios en mente, se presentan nuevos retos en los que lograr que el software que hemos elegido como candidato se ejecuten de la manera más eficiente y obteniendo los mejores resultados posibles. Estas nuevas plataformas hacen necesario un rediseño del software para aprovechar al máximo los recursos computacionales disponibles. Se debe por tanto rediseñar y optimizar los algoritmos existentes para conseguir que las aportaciones en este campo sean relevantes, y encontrar algoritmos que, por su propia naturaleza sean candidatos para que su ejecución en dichas plataformas de alto rendimiento sea óptima. Encontramos en este punto una familia de algoritmos denominados bioinspirados, que utilizan la inteligencia colectiva como núcleo para la resolución de problemas. Precisamente esta inteligencia colectiva es la que les hace candidatos perfectos para su implementación en estas plataformas bajo el nuevo paradigma de computación paralela, puesto que las soluciones pueden ser construidas en base a individuos que mediante alguna forma de comunicación son capaces de construir conjuntamente una solución común. Esta tesis se centrará especialmente en uno de estos algoritmos bioinspirados que se engloba dentro del término metaheurísticas bajo el paradigma del Soft Computing, el Ant Colony Optimization “ACO”. Se realizará una contextualización, estudio y análisis del algoritmo. Se detectarán las partes más críticas y serán rediseñadas buscando su optimización y paralelización, manteniendo o mejorando la calidad de sus soluciones. Posteriormente se pasará a implementar y testear las posibles alternativas sobre diversas plataformas de alto rendimiento. Se utilizará el conocimiento adquirido en el estudio teórico-práctico anterior para su aplicación a casos reales, más en concreto se mostrará su aplicación sobre el plegado de proteínas. Todo este análisis es trasladado a su aplicación a un caso concreto. En este trabajo, aunamos las nuevas plataformas hardware de alto rendimiento junto al rediseño e implementación software de un algoritmo bioinspirado aplicado a un problema científico de gran complejidad como es el caso del plegado de proteínas. Es necesario cuando se implementa una solución a un problema real, realizar un estudio previo que permita la comprensión del problema en profundidad, ya que se encontrará nueva terminología y problemática para cualquier neófito en la materia, en este caso, se hablará de aminoácidos, moléculas o modelos de simulación que son desconocidos para los individuos que no sean de un perfil biomédico.Ingeniería, Industria y Construcció

A swarm intelligence based approach to the mine detection problem

This research focuses on the application of swarm intelligence to the problem of mine detection. Swarm Intelligence concepts have captivated the interests of researchers mainly in collective robotics, optimization problems (traveling salesman problem (TSP), quadratic assignment problem, graph coloring etc.), and communication networks (routing) etc [1]. In the mine detection problem we are faced with sub problems such as searching for the mines over the minefield, defusing them effectively, and assuring that the field is clear of mines within the least possible time. In the problem, we assume that the mines can be diffused by the collective action of the robots for which a model based on ant colonies is given. In the first part of the project we study the ant colony system applied to the mine detection problem. The theoretical aspects such as the ant\u27s behavior (reaction of the ants to various circumstances that it faces), their motion over the minefield, and their process of defusing the mines are investigated. In the second section we highlight a certain formulation that the ants may be given for doing the task effectively. The ants do the task effectively when they are able to assure that the minefield is clear of the mines within the least possible time. A compilation of the results obtained by the various studies is tabulated. In the third and final section we talk about our emulations conducted on the Multi Agent Biorobotics Lab-built groundscout robots, which were used for the demonstration of our swarm intelligence-based algorithms at a practical basis. The various projects thus far conducted were a part of the Multi Agent Biorobotics Lab at Rochester Institute of Technology

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented