Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially co-located double-delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents' solutions. We follow this up with a characterization of solution quality on a suite of 23 single-objective functions and carry out a comparative analysis with eight other related nature-inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen's d and Hedge's g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all function evaluations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.Comment: 27 pages, 4 figures, 19 table

Low Carbon Logistics Optimization for Multi-depot CVRP with Backhauls - Model and Solution

CVRP (Capacitated Vehicle Routing Problems) is the integrated optimization of VRP and Bin Packing Problem (BPP), which has far-reaching practical significance, because only by taking both loading and routing into consideration can we make sure the delivery route is the most economic and the items are completely and reasonably loaded into the vehicles. In this paper, the CVRP with backhauls from multiple depots is addressed from the low carbon perspective. The problem calls for the minimization of the carbon emissions of a fleet of vehicles needed for the delivery of the items demanded by the clients. The overall problem, denoted as 2L-MDCVRPB, is NP-hard and it is very difficult to get a good performance solution in practice. We propose a quantum-behaved particle swarm optimization (QPSO) and exploration heuristic local search algorithm (EHLSA) in order to solve this model. In addition, three groups of computational experiments based on well-known benchmark instances are carried out to test the efficiency and effectiveness of the proposed model and algorithm, thereby demonstrating that the proposed method takes a short computing time to generate high quality solutions. For some instances, our algorithm can obtain new better solutions

Metaheuristic Algorithms in Artificial Intelligence with Applications to Bioinformatics, Biostatistics, Ecology and, the Manufacturing Industries

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. We apply a newly proposed nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA) and demonstrate its flexibility and out-performance relative to its competitors in a variety of optimization problems in the statistical sciences. In particular, we show the algorithm is efficient and can incorporate various cost structures or multiple user-specified nonlinear constraints. Our applications include (i) finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, (ii) estimating parameters in a commonly used Rasch model in education research, (iii) finding M-estimates for a Cox regression in a Markov renewal model and (iv) matrix completion to impute missing values in a two compartment model. In addition we discuss applications to (v) select variables optimally in an ecology problem and (vi) design a car refueling experiment for the auto industry using a logistic model with multiple interacting factors

Recent tendencies in the use of optimization techniques in geotechnics:a review

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics

Industrial machine structural components’ optimization and redesign

Tese de doutoramento em Líderes para as Indústrias TecnológicasO corte por laser é um processo altamente flexível com numerosas vantagens sobre tecnologias concorrentes. O crescimento do mercado é revelador do seu potencial, totalizando 4300 milhões de dólares americanos em 2020. O processo é utilizado em muitas indústrias e as tendências atuais passam por melhorias ao nível do tempo de ciclo, qualidade, custos e exatidão. Os materiais compósitos (nomeadamente polímeros reforçados por fibras) apresentam propriedades mecânicas atrativas para várias aplicações, incluindo a que se relaciona com o presente trabalho: componentes de máquinas industriais. A utilização de compósitos resulta tipicamente em máquinas mais eficientes, exatidão dimensional acrescida, melhor qualidade superficial, melhor eficiência energética e menor impacto ambiental. O principal objetivo deste trabalho é aumentar a produtividade de uma máquina de corte laser, através do redesign de um componente crítico (o pórtico), grande influenciador da exatidão da máquina. Pretende-se com isto criar uma metodologia genérica capaz de auxiliar no processo de redesign de componentes industriais. Dado que o problema lida com dois objetivos concorrentes (redução de peso e aumento de rigidez) e com um elevado número de variáveis, a implementação de uma rotina de otimização é um aspeto central. É crucial demonstrar que o processo de otimização proposto resulta em soluções efetivas. Estas foram validadas através de análise de elementos finitos e de validação experimental, com recurso a um protótipo à escala. O algoritmo de otimização usado é uma metaheurística, inspirado no comportamento de grupos de animais. Algoritmos Particle Swarm são sugeridos com sucesso para problemas de otimização semelhantes. A otimização focou-se na espessura de cada laminado, para diferentes orientações. A rotina de otimização resultou na definição de uma solução quase-ótima para os laminados analisados e permitiu a redução do peso da peça em 43% relativamente à solução atual, bem como um aumento de 25% na aceleração máxima permitida, o que se reflete na produtividade da máquina, enquanto a mesma exatidão é garantida. A comparação entre os resultados numéricos e experimentais para os protótipos mostra uma boa concordância, com divergências pontuais, mas que ainda assim resultam na validação do modelo de elementos finitos no qual se baseia a otimização.Laser cutting is a highly flexible process with numerous advantages over competing technologies. These have ensured the growth of its market, totalling 4300 million United States dollars in 2020. Being used in many industries, the current trends are focused on reduced lead time, increased quality standards and competitive costs, while ensuring accuracy. Composite materials (namely fibre reinforced polymers) present attractive mechanical properties that poses them as advantageous for several applications, including the matter of this thesis: industrial machine components. The use of these materials leads to machines with higher efficiency, dimensional accuracy, surface quality, energy efficiency, and environmental impact. The main goal of this work is to increase the productivity of a laser cutting machine through the redesign of a critical component (gantry), also key for the overall machine accuracy. Beyond that, it is intended that this work lays out a methodology capable of assisting in the redesign of other machine critical components. As the problem leads with two opposing objectives (reducing weight and increasing stiffness), and with many variables, the implementation of an optimization routine is a central aspect of the present work. It is of major importance that the proposed optimization method leads to reliable results, demonstrated in this work by a finite element analysis and through experimental validation, by means of a scale prototype. The optimization algorithm selected is a metaheuristic inspired by the behaviour of swarms of animals. Particle swarm algorithms are proven to provide good and fast results in similar optimization problems. The optimization was performed focusing on the thickness of each laminate and on the orientations present in these. The optimization routine resulted in a definition of a near-optimal solution for the laminates analysed and allowed a weight reduction of 43% regarding the current solution, as well as an increase of 25% in the maximum allowed acceleration, which reflects on the productivity of the machine, while ensuring the same accuracy. The comparison between numeric and experimental testing of the prototypes shows a good agreement, with punctual divergences, but that still validates the Finite elements upon which the optimization process is supported.Portuguese Foundation for Science and Technology - SFRH/BD/51106/2010

Agricultural Structures and Mechanization

In our globalized world, the need to produce quality and safe food has increased exponentially in recent decades to meet the growing demands of the world population. This expectation is being met by acting at multiple levels, but mainly through the introduction of new technologies in the agricultural and agri-food sectors. In this context, agricultural, livestock, agro-industrial buildings, and agrarian infrastructure are being built on the basis of a sophisticated design that integrates environmental, landscape, and occupational safety, new construction materials, new facilities, and mechanization with state-of-the-art automatic systems, using calculation models and computer programs. It is necessary to promote research and dissemination of results in the field of mechanization and agricultural structures, specifically with regard to farm building and rural landscape, land and water use and environment, power and machinery, information systems and precision farming, processing and post-harvest technology and logistics, energy and non-food production technology, systems engineering and management, and fruit and vegetable cultivation systems. This Special Issue focuses on the role that mechanization and agricultural structures play in the production of high-quality food and continuously over time. For this reason, it publishes highly interdisciplinary quality studies from disparate research fields including agriculture, engineering design, calculation and modeling, landscaping, environmentalism, and even ergonomics and occupational risk prevention

Foundations of Trusted Autonomy

Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems