Optimal control problems solved via swarm intelligence

Questa tesi descrive come risolvere problemi di controllo ottimo tramite swarm in telligence. Grande enfasi viene posta circa la formulazione del problema di controllo ottimo, in particolare riguardo a punti fondamentali come l’identificazione delle incognite, la trascrizione numerica e la scelta del risolutore per la programmazione non lineare. L’algoritmo Particle Swarm Optimization viene preso in considerazione e la maggior parte dei problemi proposti sono risolti utilizzando una formulazione differential flatness. Quando viene usato l’approccio di dinamica inversa, il problema di ottimo relativo ai parametri di trascrizione è risolto assumendo che le traiettorie da identificare siano approssimate con curve B-splines. La tecnica Inverse-dynamics Particle Swarm Optimization, che viene impiegata nella maggior parte delle applicazioni numeriche di questa tesi, è una combinazione del Particle Swarm e della formulazione differential flatness. La tesi investiga anche altre opportunità di risolvere problemi di controllo ottimo tramite swarm intelligence, per esempio usando un approccio di dinamica diretta e imponendo a priori le condizioni necessarie di ottimalitá alla legge di controllo. Per tutti i problemi proposti, i risultati sono analizzati e confrontati con altri lavori in letteratura. Questa tesi mostra quindi the algoritmi metaeuristici possono essere usati per risolvere problemi di controllo ottimo, ma soluzioni ottime o quasi-ottime possono essere ottenute al variare della formulazione del problema.This thesis deals with solving optimal control problems via swarm intelligence. Great emphasis is given to the formulation of the optimal control problem regarding fundamental issues such as unknowns identification, numerical transcription and choice of the nonlinear programming solver. The Particle Swarm Optimization is taken into account, and most of the proposed problems are solved using a differential flatness formulation. When the inverse-dynamics approach is used, the transcribed parameter optimization problem is solved assuming that the unknown trajectories are approximated with B-spline curves. The Inverse-dynamics Particle Swarm Optimization technique, which is employed in the majority of the numerical applications in this work, is a combination of Particle Swarm and differential flatness formulation. This thesis also investigates other opportunities to solve optimal control problems with swarm intelligence, for instance using a direct dynamics approach and imposing a-priori the necessary optimality conditions to the control policy. For all the proposed problems, results are analyzed and compared with other works in the literature. This thesis shows that metaheuristic algorithms can be used to solve optimal control problems, but near-optimal or optimal solutions can be attained depending on the problem formulation

A Simple Brain Storm Optimization Algorithm with a Periodic Quantum Learning Strategy

Brain storm optimization (BSO) is a young and promising population-based swarm intelligence algorithm inspired by the human process of brainstorming. The BSO algorithm has been successfully applied to both science and engineering issues. However, thus far, most BSO algorithms are prone to fall into local optima when solving complicated optimization problems. In addition, these algorithms adopt complicated clustering strategies such as K-means clustering, resulting in large computational burdens. The paper proposes a simple BSO algorithm with a periodic quantum learning strategy (SBSO-PQLS), which includes three new strategies developed to improve the defects described above. First, we develop a simple individual clustering (SIC) strategy that sorts individuals according to their fitness values and then allocates all individuals into different clusters. This reduces computational burdens and resists premature convergence. Second, we present a simple individual updating (SIU) strategy by simplifying the individual combinations and improving the step size function to enrich the diversity of newly generated individuals and reduces redundancy in the pattern for generating individuals. Third, a quantum-behaved individual updating with periodic learning (QBIU-PL) strategy is developed by introducing a quantum-behaved mechanism into SBSO-PQLS. QBIU-PL provides new momentum, enabling individuals to escape local optima. With the support of these three strategies, SBSO-PQLS effectively improves its global search capability and computational burdens. SBSO-PQLS is compared with seven other BSO variants, Particle Swarm Optimization (PSO), and Differential Evolution (DE) on CEC2013 benchmark functions. The results show that SBSO-PQLS achieves a better global search performance than do the other nine algorithms

The Boston University Photonics Center annual report 2015-2016

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2015-2016 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that this year the Center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.9M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and cooperated in supporting National Science Foundation sponsored Sites for Research Experiences for Undergraduates and for Research Experiences for Teachers. As a community, we emphasized the theme of “Frontiers in Plasmonics as Enabling Science in Photonics and Beyond” at our annual symposium, hosted by Bjoern Reinhard. We continued to support the National Photonics Initiative, and contributed as a cooperating site in the American Institute for Manufacturing Integrated Photonics (AIM Photonics) which began this year as a new photonics-themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Development of Less Toxic Treatment Strategies for Metastatic and Drug Resistant Breast Cancer Using Noninvasive Optical Monitoring led by Professor Darren Roblyer, continued support of our NIH-sponsored, Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and an exciting confluence of new grant awards in the area of Neurophotonics led by Professors Christopher Gabel, Timothy Gardner, Xue Han, Jerome Mertz, Siddharth Ramachandran, Jason Ritt, and John White. Neurophotonics is fast becoming a leading area of strength of the Photonics Center. The Industry/University Collaborative Research Center, which has become the centerpiece of our translational biophotonics program, continues to focus onadvancing the health care and medical device industries, and has entered its sixth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base

A Brain Storm Optimization with Multiinformation Interactions for Global Optimization Problems

The original BSO fails to consider some potential information interactions in its individual update pattern, causing the premature convergence for complex problems. To address this problem, we propose a BSO algorithm with multi-information interactions (MIIBSO). First, a multi-information interaction (MII) strategy is developed, thoroughly considering various information interactions among individuals. Specially, this strategy contains three new MII patterns. The first two patterns aim to reinforce information interaction capability between individuals. The third pattern provides interactions between the corresponding dimensions of different individuals. The collaboration of the above three patterns is established by an individual stagnation feedback (ISF) mechanism, contributing to preserve the diversity of the population and enhance the global search capability for MIIBSO. Second, a random grouping (RG) strategy is introduced to replace both the K-means algorithm and cluster center disruption of the original BSO algorithm, further enhancing the information interaction capability and reducing the computational cost of MIIBSO. Finally, a dynamic difference step-size (DDS), which can offer individual feedback information and improve search range, is designed to achieve an effective balance between global and local search capability for MIIBSO. By combining the MII strategy, RG, and DDS, MIIBSO achieves the effective improvement in the global search ability, convergence speed, and computational cost. MIIBSO is compared with 11 BSO algorithms and five other algorithms on the CEC2013 test suit. The results confirm that MIIBSO obtains the best global search capability and convergence speed amongst the 17 algorithms

A Vector Grouping Learning Brain Storm Optimization Algorithm for Global Optimization Problems

The original brain storm optimization (BSO) method does not rationally compromise global exploration and local exploitation capability, which results in the premature convergence when solving complicated optimization problems like the shifted or shifted rotated functions. To address this problem, the paper develops a vector grouping learning BSO (VGLBSO) method. In VGLBSO, the individuals’ creation based on vector grouping learning (IC-VGL) scheme is first developed to improve the population diversity and compromise the global exploration and local exploitation capability. Moreover, a hybrid individuals’ update (H-IU) scheme is established by reasonably combing two different individuals’ update schemes, which further compromises the global exploration and local exploitation capability. Finally, the random grouping (RG) scheme, instead of K-means grouping is allowed to shrink the computational cost and maintain the diversity of the information exchange between different individuals. Twenty-eight popular benchmark functions are used to compare VGLBSO with 12 BSO and nine swarm intelligence methods. Experimental results present that VGLBSO achieves the best overall performance including the global search ability, convergence speed, and scalability amongst all the compared algorithms

Research and technology annual report, 1982

Various research and technology activities are described. Highlights of these accomplishments indicate varied and highly productive reseach efforts

Air Force Institute of Technology Research Report 2017

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)