853 research outputs found

    Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

    Get PDF
    New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented

    Improving Design Optimization and Optimization-based Design Knowledge Discovery

    Get PDF
    The use of design optimization in the early stages of architectural design process has attracted a high volume of research in recent years. However, traditional design optimization requires a significant amount of computing time, especially when there are multiple design objectives to achieve. What’s more, there is a lack of studies in the current research on automatic generation of architectural design knowledge from optimization results. This paper presents computational methods for creating and improving a closed loop of design optimization and knowledge discovery in architecture. It first introduces a design knowledge-assisted optimization improvement method with the techniques - offline simulation and Divide & Conquer (D&C) - to reduce the computing time and improve the efficiency of the design optimization process utilizing architectural domain knowledge. It then describes a new design knowledge discovery system where design knowledge can be discovered from optimization through an automatic data mining approach. The discovered knowledge has the potential to further help improve the efficiency of the optimization method, thus forming a closed loop of improving optimization and knowledge discovery. The validations of both methods are presented in the context of a case study with parametric form-finding for a nursing unit design with two design objectives: minimizing the nurses’ travel distance and maximizing daylighting performance in patient rooms

    Gaps and requirements for applying automatic architectural design to building renovation

    Get PDF
    The renovation of existing buildings provides an opportunity to change the layout to meet the needs of facilities and accomplish sustainability in the built environment at high utilisation rates and low cost. However, building renovation design is complex, and completing architectural design schemes manually needs more efficiency and overall robustness. With the use of computational optimisation, automatic architectural design (AAD) can efficiently assist in building renovation through decision-making based on performance evaluation. This paper comprehensively analyses AAD's current research status and provides a state-of-the-art overview of applying AAD technology to building renovation. Besides, gaps and requirements of using AAD for building renovation are explored from quantitative and qualitative aspects, providing ideas for future research. The research shows that there is still much work to be done to apply AAD to building renovation, including quickly obtaining input data, expanding optimisation topics, selecting design methods, and improving workflow and efficiency

    Computational approach for form-finding optimal design

    Get PDF
    In this paper, an optimization strategy for a canopy, based on computational modelling approaches is presented. The design approach is applied to a realistic roof structure of an ecological island (waste collection centre) and has been completely redesigned with the aid of a Genetic Algorithm and a Dynamic Relaxation Algorithm. The preliminary design of the roof structure can be formulated as a shape optimization problem, involving functional needs and constraints at different scales of the structure. The proposed hypothesis solution was studied by using an optimization procedure through algorithms in the software Rhinoceros3D®/Grasshopper®. The main aim of this work is to explore different modelling approaches for form-finding that can be built from the use of numerical simulations based on algorithms. To this aim, the need to meet various requirements (structural, functional, formal) involving a team of architects and engineers can be interpreted as a matter of structural optimizatio

    Parametric design and optimization of arched trusses under vertical and horizontal multi-load cases

    Get PDF
    This dissertation faces the problem of the optimum design of steel truss arches subject to multiple load cases. Arches are one of the most ancient shape-resistant structures, widely used in both civil engineering and architecture. For instance, arches can be considered as purely compressed structures, provided that their “line of thrust” coincides with the centre line of the arch. The “line of thrust” is the locus of the points of application of the thrusts (internal forces or stress resultants) that must be contained within the cross-section of the arch in such a way that the arch transfers loads to the foundations through axial compressive stresses only. As a matter of fact, the more the “line of thrust” differs from the centre line of the arch, the larger the unfavourable bending moments that arise in the arch. This is the reason why it is fundamental to pay close attention to the choice of the shape for an arch in order to minimize (or avoid when it is possible) unfavourable bending effects. Several analytical, graphical and physical methods are provided to find the optimal shape of a monolithic (single rib) arch subjected to a certain load case (i.e. the “funicular curve” for that load). However, if multiple load cases must be considered, it is not possible to find a proper optimal shape for an arch with single rib. In this case, the choice of truss arches with at least two chords becomes indispensable. Indeed, it has been demonstrated that structural optimization of in-plane truss arches with two chords subjected to a single load case leads to optimal solutions in which upper and lower chords tend to coincide with each other and with the “funicular curve” (i.e. the “line of thrust”) for that load. In light of the above, simultaneous shape and size optimization of steel truss arches with two arched chords linked each other through a bracing system (with variable Pratt-type pattern) has been performed for multiple load cases and different structural boundary conditions. Truss arches are effectively used in arch bridges, especially when the arch span exceeds 200 meters (five out of the six steel arch bridges with a span over 500 m are truss arch bridges). For this purpose, a hybrid optimization routine integrating a parametric definition of the design problem, a metaheuristic optimization algorithm and a code for Finite Element Analysis (FEA) has been developed through a MATLAB program. The proposed optimization method allows to simultaneously optimize a larger set of design variables, notwithstanding their large number and various nature (topology, shape and size, as well as continuous and discrete variables, have been concurrently considered). Third-degree Rational Bézier Curves have been chosen to optimize the shape of the arch chords because they can represent a wide family of curves (including conic curves), depending on a small number of parameters. In so doing, in-plane truss arches with different span lengths and structural boundary conditions have been optimized for multiple load cases, only considering vertical loads (acting on the same plane as the arch), since in-plane arches are not suited to withstand out-of-plane loads. On the other hand, spatial arched trusses with two arched chords lying on different planes have been optimally designed for multiple loadings acting in different directions. In particular, a steel arched truss with a lower arched chord variably inclined in the 3D-space and a horizontal upper arched chord linked each other through a bracing system has been designed and optimized for three vertical load cases and a horizontal seismic action parallel to the upper chord plane. Thus, analysing the obtained results, useful suggestions for steel truss arch design have been deduced and presented in this dissertation

    An Improvement of Load Flow Solution for Power System Networks using Evolutionary-Swarm Intelligence Optimizers

    Get PDF
    Load flow report which reveals the existing state of the power system network under steady operating conditions, subject to certain constraints is being bedeviled by issues of accuracy and convergence. In this research, five AI-based load flow solutions classified under evolutionary-swarm intelligence optimizers are deployed for power flow studies in the 330kV, 34-bus, 38-branch section of the Nigerian transmission grid. The evolutionary-swarm optimizers used in this research consist of one evolutionary algorithm and four swarm intelligence algorithms namely; biogeography-based optimization (BBO), particle swarm optimization (PSO), spider monkey optimization (SMO), artificial bee colony optimization (ABCO) and ant colony optimization (ACO). BBO as a sole evolutionary algorithm is being configured alongside four swarm intelligence optimizers for an optimal power flow solution with the aim of performance evaluation through physical and statistical means. Assessment report upon application of these standalone algorithms on the 330kV Nigerian grid under two (accuracy and convergence) metrics produced PSO and ACO as the best-performed algorithms. Three test cases (scenarios) were adopted based on the number of iterations (100, 500, and 1000) for proper assessment of the algorithms and the results produced were validated using mean average percentage error (MAPE) with values of voltage profile created by each solution algorithm in line with the IEEE voltage regulatory standards. All algorithms proved to be good load flow solvers with distinct levels of precision and speed. While PSO and SMO produced the best and worst results for accuracy with MAPE values of 3.11% and 36.62%, ACO and PSO produced the best and worst results for convergence (computational speed) after 65 and 530 average number of iterations. Since accuracy supersedes speed from scientific considerations, PSO is the overall winner and should be cascaded with ACO for an automated hybrid swarm intelligence load flow model in future studies. Future research should consider hybridizing ACO and PSO for a more computationally efficient solution model

    An Improved Binary Grey-Wolf Optimizer with Simulated Annealing for Feature Selection

    Get PDF
    This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms

    Multi-objective global optimization of grillage-type engineering structures using advanced metaheuristics

    Get PDF
    The purpose of the paper is to present the method implemented for a global optimization of grillage-type pile foundations introducing two advanced metaheuristics: AAGA and AGADS. The suggested new optimization algorithm including the synergy of AAGA and AGADS demonstrates improved results comparing with former AGA and GADS. Compromise objective function to be minimized involves the maximum reactive force in piles and maximum bending moment in the connecting beams. The feasibility of a simple weighting technique for the objective function is proved by numerical investigation of objective function domain for several different topologies of foundations. Sizing problem of connecting beams is solved together with the optimization problem. The original finite element program was employed for solution of direct problem
    • …
    corecore