Nonlinear mixed integer based optimization technique for space applications

In this thesis a new algorithm for mixed integer nonlinear programming (MINLP) is developed and applied to several real world applications with special focus on space applications. The algorithm is based on two main components, which are an extension of the Ant Colony Optimization metaheuristic and the Oracle Penalty Method for constraint handling. A sophisticated implementation (named MIDACO) of the algorithm is used to numerically demonstrate the usefulness and performance capabilities of the here developed novel approach on MINLP. An extensive amount of numerical results on both, comprehensive sets of benchmark problems (with up to 100 test instances) and several real world applications, are presented and compared to results obtained by concurrent methods. It can be shown, that the here developed approach is not only fully competitive with established MINLP algorithms, but is even able to outperform those regarding global optimization capabilities and cpu runtime performance. Furthermore, the algorithm is able to solve challenging space applications, that are considered here as mixed integer problems for the very first time

Allocation of dump load in islanded microgrid using the mixed-integer distributed ant colony optimization with robust backward\forward sweep load flow

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonReliable planning and operation of droop-controlled islanded microgrids (DCIMGs) is fundamental to expand microgrids (MGs) scalability and maximize renewable energy potential. Employing dump loads (DLs) is a promising solution to absorb excess generation during off-peak hours while keeping voltage and frequency within acceptable limits to meet international standards. Considering wind power and demand forecast uncertainties in DCIMG during off-peak hours, the allocation of DL problem was modelled as two problems, viz., deterministic and stochastic. The former problem was tackled using four highly probable deterministic generation and demand mismatch scenarios, while the latter problem was formulated within scenario based stochastic framework for uncertainty modelling. The mixed-integer distributed ant colony optimization (MIDACO) was introduced as a novel application in microgrids to find the optimal location and size of DL as well as the optimal droop setting for distributed generation (DG). Furthermore, to enhance the convergence of the proposed optimization technique, three robust and derivative free load flow methods were developed as novel extensions of the original backward\forward sweep (BFS) for grid-connected MGs. The three load flow methods are called special BFS, improved special BFS, and general BFS. The first two methods rely on one global voltage variable distributed among all DGs, while the latter has more general approach by adopting local voltage at each generating bus. The deterministic multi-objective optimization problem was formulated to minimize voltage and frequency deviation as well as power losses. Inversely, the stochastic multi-objective problem with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The proposed method was applied to the IEEE 33-, 69-, and 118-test systems as modelled in MATLAB environment and further validated against competitive swarm and evolutionary metaheuristics. Various convergence tests were considered to demonstrate the efficacy of the proposed load flow methods with MIDACO’s non-dominated solution. Likewise, different optimization parameters were utilized to investigate their impact on the solution. Moreover, the advantage of multi-objective optimization against single objective was provided for the deterministic optimization problem, while the effect of load model and droop response were also investigated. The obtained results in chapter 5 and 6 further demonstrate the fundamental role of DL in voltage and frequency regulation while minimizing costs and energy losses associated with DCIMG operation. Accordingly, an improved voltage and frequency profiles for the system after DL inclusion were attained in Figure 6.9 and Figure 6.10, respectively. To demonstrate the competitiveness of DL-based energy management system (EMS) against storage-based EMS, a brief cost benefit analysis considering hot water demand was also provided

Aplicación de algoritmos de optimización multiobjetivo a la mezcla de distintas fuentes de minerales en el largo plazo

Se presentan en este trabajo estrategias basadas en optimización multiobjetivo y supervisión predictiva destinadas a lograr una mezcla óptima de las distintas fuentes de mineral que ingresan a una planta de tratamiento, para las minas Cerro Vanguardia (Santa Cruz) y Casposo (Calingasta, San Juan). El planteo tiene una concepción Multiobjetivo, debido a las diversas variables que influyen en este caso de problemas y una concepción predictiva debido a que se desea modelar el comportamiento de las variables a lo largo del tiempo. Este algoritmo se plantea con la suficiente flexibilidad como para ser adaptados fácilmente a otras situaciones semejantes.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Aplicación de algoritmos de optimización multiobjetivo a la mezcla de distintas fuentes de minerales en el largo plazo

Se presentan en este trabajo estrategias basadas en optimización multiobjetivo y supervisión predictiva destinadas a lograr una mezcla óptima de las distintas fuentes de mineral que ingresan a una planta de tratamiento, para las minas Cerro Vanguardia (Santa Cruz) y Casposo (Calingasta, San Juan). El planteo tiene una concepción Multiobjetivo, debido a las diversas variables que influyen en este caso de problemas y una concepción predictiva debido a que se desea modelar el comportamiento de las variables a lo largo del tiempo. Este algoritmo se plantea con la suficiente flexibilidad como para ser adaptados fácilmente a otras situaciones semejantes.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

MIDACO parallelization scalability on 200 minlp benchmarks

This contribution presents a numerical evaluation of the impact of parallelization on the performance of an evolutionary algorithm for mixed-integer nonlinear programming (MINLP). On a set of 200 MINLP benchmarks the performance of the MIDACO solver is assessed with gradually increasing parallelization factor from one to three hundred. The results demonstrate that the efficiency of the algorithm can be significantly improved by parallelized function evaluation. Furthermore, the results indicate that the scale-up behaviour on the efficiency resembles a linear nature, which implies that this approach will even be promising for very large parallelization factors. The presented research is especially relevant to CPU-time consuming real-world applications, where only a low number of serial processed function evaluation can be calculated in reasonable time

XXI Workshop de Investigadores en Ciencias de la Computación - WICC 2019: libro de actas

Trabajos presentados en el XXI Workshop de Investigadores en Ciencias de la Computación (WICC), celebrado en la provincia de San Juan los días 25 y 26 de abril 2019, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Ciencias Exactas, Físicas y Naturales de la Universidad Nacional de San Juan.Red de Universidades con Carreras en Informátic

XXI Workshop de Investigadores en Ciencias de la Computación - WICC 2019: libro de actas

Trabajos presentados en el XXI Workshop de Investigadores en Ciencias de la Computación (WICC), celebrado en la provincia de San Juan los días 25 y 26 de abril 2019, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Ciencias Exactas, Físicas y Naturales de la Universidad Nacional de San Juan.Red de Universidades con Carreras en Informátic