Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Automating control system design via a multiobjective evolutionary algorithm

This chapter presents a performance-prioritized computer aided control system design (CACSD) methodology using a multi-objective evolutionary algorithm. The evolutionary CACSD approach unifies different control laws in both the time and frequency domains based upon performance satisfactions, without the need of aggregating different design criteria into a compromise function. It is shown that control engineers' expertise as well as settings on goal or priority for different preference on each performance requirement can be easily included and modified on-line according to the evolving trade-offs, which makes the controller design interactive, transparent and simple for real-time implementation. Advantages of the evolutionary CACSD methodology are illustrated upon a non-minimal phase plant control system, which offer a set of low-order Pareto optimal controllers satisfying all the conflicting performance requirements in the face of system constraints

Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions

The goal of population spectral synthesis (PSS) is to decipher from the spectrum of a galaxy the mass, age and metallicity of its constituent stellar populations. This technique has been established as a fundamental tool in extragalactic research. It has been extensively applied to large spectroscopic data sets, notably the SDSS, leading to important insights into the galaxy assembly history. However, despite significant improvements over the past decade, all current PSS codes suffer from two major deficiencies that inhibit us from gaining sharp insights into the star-formation history (SFH) of galaxies and potentially introduce substantial biases in studies of their physical properties (e.g., stellar mass, mass-weighted stellar age and specific star formation rate). These are i) the neglect of nebular emission in spectral fits, consequently, ii) the lack of a mechanism that ensures consistency between the best-fitting SFH and the observed nebular emission characteristics of a star-forming (SF) galaxy. In this article, we present FADO (Fitting Analysis using Differential evolution Optimization): a conceptually novel, publicly available PSS tool with the distinctive capability of permitting identification of the SFH that reproduces the observed nebular characteristics of a SF galaxy. This so-far unique self-consistency concept allows us to significantly alleviate degeneracies in current spectral synthesis. The innovative character of FADO is further augmented by its mathematical foundation: FADO is the first PSS code employing genetic differential evolution optimization. This, in conjunction with other unique elements in its mathematical concept (e.g., optimization of the spectral library using artificial intelligence, convergence test, quasi-parallelization) results in key improvements with respect to computational efficiency and uniqueness of the best-fitting SFHs.Comment: 25 pages, 12 figures, A&A accepte

Multi-disciplinary shape optimization of an entry capsule integrated with custom neural network approximation and multi-delity approach

This paper describes a new integrated approach for the multi-disciplinary optimization of a entry capsule’s shape. Aerothermodynamics, Flight Mechanics and Thermal Protection System behaviour of a reference spaceship when crossing Martian atmosphere are considered, and several analytical, semi-empirical and numerical models are used. The multi-objective and multi-disciplinary optimization process implemented in Isight software environment allows finding a Pareto front of best shapes. The optimization process is integrated with a set of artificial neural networks, trained and updated by a multi-fidelity evolution control approach, to approximate the objective and constraint functions. Results obtained by means of the integrated approach with neural networks approximators are described and compared to the results obtained by a different optimization process, not using the approximators. The comparison highlights advantages and possible drawbacks of the proposed method, mainly in terms of calls to the true model and precision of the obtained Pareto front

Online on-board optimization of cutting parameter for energy efficient CNC milling

Energy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a workshop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parameters to reduce the overall machining time while at the same time minimizing the peak energy consumption

Multi-disciplinary robust design of variable speed wind turbines

This paper addresses the preliminary robust multi-disciplinary design of small wind turbines. The turbine to be designed is assumed to be connected to the grid by means of power electronic converters. The main input parameter is the yearly wind distribution at the selected site, and it is represented by means of a Weibull distribution. The objective function is the electrical energy delivered yearly to the grid. Aerodynamic and electrical characteristics are fully coupled and modelled by means of low- and medium-fidelity models. Uncertainty affecting the blade geometry is considered, and a multi-objective hybrid evolutionary algorithm code is used to maximise the mean value of the yearly energy production and minimise its variance

Optimization of Discrete-parameter Multiprocessor Systems using a Novel Ergodic Interpolation Technique

Modern multi-core systems have a large number of design parameters, most of which are discrete-valued, and this number is likely to keep increasing as chip complexity rises. Further, the accurate evaluation of a potential design choice is computationally expensive because it requires detailed cycle-accurate system simulation. If the discrete parameter space can be embedded into a larger continuous parameter space, then continuous space techniques can, in principle, be applied to the system optimization problem. Such continuous space techniques often scale well with the number of parameters. We propose a novel technique for embedding the discrete parameter space into an extended continuous space so that continuous space techniques can be applied to the embedded problem using cycle accurate simulation for evaluating the objective function. This embedding is implemented using simulation-based ergodic interpolation, which, unlike spatial interpolation, produces the interpolated value within a single simulation run irrespective of the number of parameters. We have implemented this interpolation scheme in a cycle-based system simulator. In a characterization study, we observe that the interpolated performance curves are continuous, piece-wise smooth, and have low statistical error. We use the ergodic interpolation-based approach to solve a large multi-core design optimization problem with 31 design parameters. Our results indicate that continuous space optimization using ergodic interpolation-based embedding can be a viable approach for large multi-core design optimization problems.Comment: A short version of this paper will be published in the proceedings of IEEE MASCOTS 2015 conferenc