Particle swarm optimization for linear support vector machines based classifier selection

Particle swarm optimization is a metaheuristic technique widely applied to solve various optimization problems as well as parameter selection problems for various classification techniques. This paper presents an approach for linear support vector machines classifier optimization combining its selection from a family of similar classifiers with parameter optimization. Experimental results indicate that proposed heuristics can help obtain competitive or even better results compared to similar techniques and approaches and can be used as a solver for various classification tasks

Security-Constrained Unit Commitment Based on a Realizable Energy Delivery Formulation

Security-constrained unit commitment (SCUC) is an important tool for independent system operators in the day-ahead electric power market. A serious issue arises that the energy realizability of the staircase generation schedules obtained in traditional SCUC cannot be guaranteed. This paper focuses on addressing this issue, and the basic idea is to formulate the power output of thermal units as piecewise-linear function. All individual unit constraints and systemwide constraints are then reformulated. The new SCUC formulation is solved within the Lagrangian relaxation (LR) framework, in which a double dynamic programming method is developed to solve individual unit subproblems. Numerical testing is performed for a 6-bus system and an IEEE 118-bus system on Microsoft Visual C# .NET platform. It is shown that the energy realizability of generation schedules obtained from the new formulation is guaranteed. Comparative case study is conducted between LR and mixed integer linear programming (MILP) in solving the new formulation. Numerical results show that the near-optimal solution can be obtained efficiently by the proposed LR-based method

MILP-Based Short-Term Thermal Unit Commitment and Hydrothermal Scheduling Including Cascaded Reservoirs and Fuel Constraints

Reservoirs are often built in cascade on the same river system, introducing inexorable constraints. It is therefore strategically important to scheme out an efficient commitment of thermal generation units along with the scheduling of hydro generation units for better operational efficiency, considering practical system conditions. This paper develops a comprehensive, unit-wise hydraulic model with reservoir and river system constraints, as well as gas constraints, with head effects, to commit thermal generation units and schedule hydro ones in the short-term. A mixed integer linear programming (MILP) methodology, using the branch and bound & cut (BB&C) algorithm, is employed to solve the resultant problem. Due to the detailed modelling of individual hydro units and cascaded dependent reservoirs, the problem size is substantially swollen. Multithread computing is invoked to accelerate the solution process. Simulation results, conducted on various test systems, reiterate that the developed MILP-based hydrothermal scheduling approach outperforms other techniques in terms of cost efficiency

A parallel implementation on a multi-core architecture of a dynamic programming algorithm applied in cognitive radio ad hoc networks

Spectral resources allocation is a major problem in cognitive radio ad hoc networks and currently most of the research papers use meta-heuristics to solve it. On the other side, the term parallelism refers to techniques to make programs faster by performing several computations in parallel. Parallelism would be very interesting to increase the performance of real-time systems, especially for the cognitive radio ad hoc networks that interest us in this work. In this paper, we present a parallel implementation on a multi-core architecture of dynamic programming algorithm applied in cognitive radio ad hoc networks. Our simulations approve the desired results, showing significant gain in terms of execution time. The main objective is to allow a cognitive engine to use an exact method and to have better results compared to the use of meta-heuristics

Reservoir Flooding Optimization by Control Polynomial Approximations

In this dissertation, we provide novel parametrization procedures for water-flooding production optimization problems, using polynomial approximation techniques. The methods project the original infinite dimensional controls space into a polynomial subspace. Our contribution includes new parameterization formulations using natural polynomials, orthogonal Chebyshev polynomials and Cubic spline interpolation. We show that the proposed methods are well suited for black-box approach with stochastic global-search method as they tend to produce smooth control trajectories, while reducing the solution space size. We demonstrate their efficiency on synthetic two-dimensional problems and on a realistic 3-dimensional problem. By contributing with a new adjoint method formulation for polynomial approximation, we implemented the methods also with gradient-based algorithms. In addition to fine-scale simulation, we also performed reduced order modeling, where we demonstrated a synergistic effect when combining polynomial approximation with model order reduction, that leads to faster optimization with higher gains in terms of Net Present Value. Finally, we performed gradient-based optimization under uncertainty. We proposed a new multi-objective function with three components, one that maximizes the expected value of all realizations, and two that maximize the averages of distribution tails from both sides. The new objective provides decision makers with the flexibility to choose the amount of risk they are willing to take, while deciding on production strategy or performing reserves estimation (P10;P50;P90)

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD

A Comparative Study of Fuzzy Logic, Genetic Algorithm, and Gradient-Genetic Algorithm Optimization Methods for Solving the Unit Commitment Problem

Due to the continuous increase of the population and the perpetual progress of industry, the energy management presents nowadays a relevant topic that concerns researchers in electrical engineering. Indeed, in order to establish a good exploitation of the electrical grid, it is necessary to solve technical and economic problems. This can only be done through the resolution of the Unit Commitment Problem. Unit Commitment Problem allows optimizing the combination of the production units’ states and determining their production planning, in order to satisfy the expected consumption with minimal cost during a specified period which varies usually from 24 hours to one week. However, each production unit has some constraints that make this problem complex, combinatorial, and nonlinear. This paper presents a comparative study between a strategy based on hybrid gradient-genetic algorithm method and two strategies based on metaheuristic methods, fuzzy logic, and genetic algorithm, in order to predict the combinations and the unit commitment scheduling of each production unit in one side and to minimize the total production cost in the other side. To test the performance of the optimization proposed strategies, strategies have been applied to the IEEE electrical network 14 busses and the obtained results are very promising

Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool

Model predictive control (MPC) is an optimization-based strategy for high-performance control that is attracting increasing interest. While MPC requires the online solution of an optimization problem, its ability to handle multivariable systems and constraints makes it a very powerful control strategy specially for MPC of embedded systems, which have an ever increasing amount of sensing and computation capabilities. We argue that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems. The main burden for the implementation of MPC on FPGAs is the challenging design of the necessary algorithms. We outline an approach to achieve a software-supported optimized implementation of MPC on FPGAs using high-level synthesis tools and automatic code generation. The proposed strategy exploits the arithmetic operations necessaries to solve optimization problems to tailor an FPGA design, which allows a tradeoff between energy, memory requirements, cost, and achievable speed. We show the capabilities and the simplicity of use of the proposed methodology on two different examples and illustrate its advantages over a microcontroller implementation

Automated optimization of reconfigurable designs

Currently, the optimization of reconfigurable design parameters is typically done manually and often involves substantial amount effort. The main focus of this thesis is to reduce this effort. The designer can focus on the implementation and design correctness, leaving the tools to carry out optimization. To address this, this thesis makes three main contributions. First, we present initial investigation of reconfigurable design optimization with the Machine Learning Optimizer (MLO) algorithm. The algorithm is based on surrogate model technology and particle swarm optimization. By using surrogate models the long hardware generation time is mitigated and automatic optimization is possible. For the first time, to the best of our knowledge, we show how those models can both predict when hardware generation will fail and how well will the design perform. Second, we introduce a new algorithm called Automatic Reconfigurable Design Efficient Global Optimization (ARDEGO), which is based on the Efficient Global Optimization (EGO) algorithm. Compared to MLO, it supports parallelism and uses a simpler optimization loop. As the ARDEGO algorithm uses multiple optimization compute nodes, its optimization speed is greatly improved relative to MLO. Hardware generation time is random in nature, two similar configurations can take vastly different amount of time to generate making parallelization complicated. The novelty is efficient use of the optimization compute nodes achieved through extension of the asynchronous parallel EGO algorithm to constrained problems. Third, we show how results of design synthesis and benchmarking can be reused when a design is ported to a different platform or when its code is revised. This is achieved through the new Auto-Transfer algorithm. A methodology to make the best use of available synthesis and benchmarking results is a novel contribution to design automation of reconfigurable systems.Open Acces

Optimization Methods and Algorithms for Classes of Black-Box and Grey-Box Problems

There are many optimization problems in physics, chemistry, finance, computer science, engineering and operations research for which the analytical expressions of the objective and/or the constraints are unavailable. These are black-box problems where the derivative information are often not available or too expensive to approximate numerically. When the derivative information is absent, it becomes challenging to optimize and guarantee optimality of the solution. The objective of this Ph.D. work is to propose methods and algorithms to address some of the challenges of blackbox optimization (BBO). A top-down approach is taken by first addressing an easier class of black-box and then the difficulty and complexity of the problems is gradually increased. In the first part of the dissertation, a class of grey-box problems is considered for which the closed form of the objective and/or constraints are unknown, but it is possible to obtain a global upper bound on the diagonal Hessian elements. This allows the construction of an edge-concave underestimator with vertex polyhedral solution. This lower bounding technique is implemented within a branch-and-bound framework with guaranteed convergence to global optimality. The technique is applied for the optimization of problems with embedded system of ordinary differential equations (ODEs). Time dependent bounds on the state variables and the diagonal elements of the Hessian are computed by solving auxiliary set of ODEs that are derived using differential inequalities. In the second part of the dissertation, general box-constrained black-box problems are addressed for which only simulations can be performed. A novel optimization method, UNIPOPT (Univariate Projection-based Optimization) based on projection onto a univariate space is proposed. A special function is identified in this space that also contains the global minima of the original function. Computational experiments suggest that UNIPOPT often have better space exploration features compared to other approaches. The third part of the dissertation addresses general black-box problems with constraints of both known and unknown algebraic forms. An efficient two-phase algorithm based on trust-region framework is proposed for problems particularly involving high function evaluation cost. The performance of the approach is illustrated through computational experiments which evaluate its ability to reduce a merit function and find the optima