A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades:Part B

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out

The Encyclopedia of Neutrosophic Researchers, 5th Volume

Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements. There are about 7,000 neutrosophic researchers, within 89 countries around the globe, that have produced about 4,000 publications and tenths of PhD and MSc theses, within more than two decades. This is the fifth volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation, with an introduction contains a short history of neutrosophics, together with links to the main papers and books

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

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Glosarium Matematika

273 p.; 24 cm

Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only virtually replicate a system's detailed operations but also analyze the current condition, predict future behaviour, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate remote monitoring, diagnosis, prescription, surgery and rehabilitation. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT

Aeronautical engineering: A continuing bibliography with indexes (supplement 275)

This bibliography lists 379 reports, articles, and other documents introduced into the NASA scientific and technical information system in Jan. 1991

Multi-objective optimisation applied to industrial energy problems

This thesis presents the development of a new multi-objective optimisation tool and applies it to a number of industrial problems related to optimising energy systems. Multi-objective optimisation techniques provide the information needed for detailed analyses of design trade-offs between conflicting objectives. For example, if a product must be both inexpensive and high quality, the multi-objective optimiser will provide a range of optimal options from the cheapest (but lowest quality) alternative to the highest quality (but most expensive), and a range of designs in between – those that are the most interesting to the decision-maker. The optimisation tool developed is the queueing multi-objective optimiser (QMOO), an evolutionary algorithm (EA). EAs are particularly suited to multi-objective optimisation because they work with a population of potential solutions, each representing a different trade-off between objectives. EAs are ideal to energy system optimisation because problems from that domain are often non-linear, discontinuous, disjoint, and multi-modal. These features make energy system optimisation problems difficult to resolve with other optimisation techniques. QMOO has several features that improve its performance on energy systems problems – features that are applicable to a wide range of optimisation problems. QMOO uses cluster analysis techniques to identify separate local optima simultaneously. This technique preserves diversity and helps convergence to difficult-to-find optima. Once normal dominance relations no longer discriminate sufficiently between population members certain individuals are chosen and removed from the population. Careful choice of the individuals to be removed ensures that convergence continues throughout the optimisation. Preserving of the "tail regions" of the population helps the algorithm to explore the full extent of the problem's optimal regions. QMOO is applied to a number of problems: coke factory placement in Shanxi Province, China; choice of heat recovery system operating temperatures; design of heat-exchanger networks; hybrid vehicle configuration; district heating network design, and others. Several of the problems were optimised previously using single-objective EAs. QMOO proved capable of finding entire ranges of solutions faster than the earlier methods found a single solution. In most cases, QMOO successfully optimises the problems without requiring any specific tuning to each problem. QMOO is also tested on a number of test problems found in the literature. QMOO's techniques for improving convergence prove effective on these problems, and its non-tuned performance is excellent compared to other algorithms found in the literature

Metaheurísticas de otimização aplicadas à sintonia dos ganhos de controlador PI multivariável

Orientador : Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 04/05/2016Inclui referências : f. 79-87Área de concentração: Sistemas eletrônicosResumo: Esta dissertação tem por objetivo avaliar abordagens de sintonia de controladores PI (Proporcional e Integral) multivariável e acoplado, utilizando metaheurísticas de otimização aplicada a soma ponderada dos sinais de erro do sistema. Os controladores PI e PID (Proporcional, Integral e Derivativo) são os controladores mais utilizados na indústria, pois possui um algoritmo simples e eficiente. Nesta dissertação, o algoritmo evolutivo denominado evolução diferencial (DE), é comparado a outros algoritmos derivados do DE clássico e também a outros algoritmos evolutivos baseados em população. Estes algoritmos são aplicados na otimização de controle PI em dois estudos de caso: um processo de uma caldeira de turbina (Boiler-Turbine) e um processo de controle de nível (Quadruple Tank). O processo de otimização lida com a soma ponderada dos sinais de erro dos sistemas tratando-os como um problema de otimização mono-objetivo. Nos dois estudos de caso o algoritmo que obteve o melhor desempenho entre todos os algoritmos foi o EPSDE (Ensemble of Mutation and Crossover Strategies and Parameters in DE), e o que apresentou o desempenho menos promissor entre todos os algoritmos testados foi o CMAES (do inglês, Covariance Matrix Adaptation Evolution Strategy). Entre os algoritmos baseados em população o que apresentou o pior desempenho nos dois estudos de caso foi o MVO (do inglês, Multi-Verse Optimization) e o que apresentou o melhor desempenho foi PSO (do inglês, Particle Swarm Optimization). Para o primeiro estudo de caso, o DE clássico teve um bom desempenho, o que não ocorreu no segundo estudo de caso. Os algoritmos variantes de DE apresentaram um bom desempenho para os dois estudos de caso quando comparados a outros algoritmos baseados em população aplicados nesta dissertação, concluindo assim, a eficácia dos algoritmos DE para os casos testados. Palavras-chave: Controle PI Multivariável, Metaheurísticas de Otimização, Algoritmo de Evolução Diferencial.Abstract: This thesis focuses on validate the approaches used for PI control (proportional and integral) multivariable and coupled using metaheuristics optimization applied the weighted sum of the system error signals. The PI and PID (proportional, integral and derivative) controllers are the controllers most commonly used in the industry because it has a simple and efficient algorithm. In this thesis the evolutionary algorithm named differential evolution (DE) is compared to other derived algorithms and also other evolutionary algorithms based on population. These algorithms are applied to the optimization of PI control in two case studies: a process of a boiler turbine (Boiler-Turbine) and level control process (Quadruple Tank). The optimization process deals with the weighted sum of the systems errors signals by treating them as a singleobjective optimization problem. In the two case studies the algorithm which obtained the best performance among all algorithms was the EPSDE (Ensemble of Mutation and Crossover Strategies and Parameters in DE) and presented the performance less promising among all algorithms tested was the CMAES (Covariance Matrix Adaptation Evolution Strategy). Among the algorithms based on population presented the worst performance in two case studies was the MVO (Multi-Verse Optimization) and presented the best performance was PSO (Particle Swarm Optimization). For the first case study, the classic DE had a good performance, which did not occur in the second case study. The algorithms DE variants performed well for the two case studies compared to other based population algorithms applied in this thesis, concluding thus the effectiveness of DE algorithms for the cases tested. Key-words: PI Control Multivariable, Optimization Metaheuristics, Differential Evolutionary Algorithm