Multiobjective optimization of electromagnetic structures based on self-organizing migration

Práce se zabývá popisem nového stochastického vícekriteriálního optimalizačního algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukázáno, že algoritmus je schopen řešit nejrůznější typy optimalizačních úloh (s jakýmkoli počtem kritérií, s i bez omezujících podmínek, se spojitým i diskrétním stavovým prostorem). Výsledky algoritmu jsou srovnány s dalšími běžně používanými metodami pro vícekriteriální optimalizaci na velké sadě testovacích úloh. Uvedli jsme novou techniku pro výpočet metriky rozprostření (spread) založené na hledání minimální kostry grafu (Minimum Spanning Tree) pro problémy mající více než dvě kritéria. Doporučené hodnoty pro parametry řídící běh algoritmu byly určeny na základě výsledků jejich citlivostní analýzy. Algoritmus MOSOMA je dále úspěšně použit pro řešení různých návrhových úloh z oblasti elektromagnetismu (návrh Yagi-Uda antény a dielektrických filtrů, adaptivní řízení vyzařovaného svazku v časové oblasti…).This thesis describes a novel stochastic multi-objective optimization algorithm called MOSOMA (Multi-Objective Self-Organizing Migrating Algorithm). It is shown that MOSOMA is able to solve various types of multi-objective optimization problems (with any number of objectives, unconstrained or constrained problems, with continuous or discrete decision space). The efficiency of MOSOMA is compared with other commonly used optimization techniques on a large suite of test problems. The new procedure based on finding of minimum spanning tree for computing the spread metric for problems with more than two objectives is proposed. Recommended values of parameters controlling the run of MOSOMA are derived according to their sensitivity analysis. The ability of MOSOMA to solve real-life problems from electromagnetics is shown in a few examples (Yagi-Uda and dielectric filters design, adaptive beam forming in time domain…).

A Novel Multi-Objective Self-Organizing Migrating Algorithm

In the paper, a novel stochastic Multi-Objective Self Organizing Migrating Algorithm (MOSOMA) is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA). In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MOSOMA performance was tested on benchmark problems and selected electromagnetic structures. MOSOMA performance was compared with the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). MOSOMA excels in the uniform distribution of solutions and their completeness

Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem

This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric

Self-organizing migrating algorithm with clustering-aided migration and adaptive perturbation vector control

The paper proposes the Self-organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The SOMA-CLP is the next iteration of the SOMA-CL algorithm, further enhanced by the linear adaptation of the prt control parameter used to generate a perturbation vector. The latest CEC 2021 benchmark set on a single objective bound-constrained optimization was used for the performance measurement of the improved variant. The proposed algorithm SOMA-CLP results were compared and tested for statistical significance against four other SOMA variants. © 2021 ACM.IGA/CebiaTech/2021/00

Explaining SOMA: The relation of stochastic perturbation to population diversity and parameter space coverage

The Self-Organizing Migrating Algorithm (SOMA) is enjoying a renewed interest of the research community, following recent achievements in various application areas and renowned performance competitions. In this paper, we focus on the importance and effect of the perturbation operator in SOMA as the perturbation is one of the fundamental inner principles of SOMA. In this in-depth study, we present data, visualizations, and analysis of the effect of the perturbation on the population, its diversity and average movement patterns. We provide evidence that there is a direct relation between the perturbation intensity (set by control parameter prt) and the rate of diversity loss. The perturbation setting further affects the exploratory ability of the algorithm, as is demonstrated here by analysing the parameter space coverage of the population. We aim to provide insight and explanation of the impact of perturbation in SOMA for future researchers and practitioners. © 2021 ACM.IGA/CebiaTech/2021/00

Energy-aware scheduling in virtualized datacenters

The reduction of energy consumption in large-scale datacenters is being accomplished through an extensive use of virtualization, which enables the consolidation of multiple workloads in a smaller number of machines. Nevertheless, virtualization also incurs some additional overheads (e.g. virtual machine creation and migration) that can influence what is the best consolidated configuration, and thus, they must be taken into account. In this paper, we present a dynamic job scheduling policy for power-aware resource allocation in a virtualized datacenter. Our policy tries to consolidate workloads from separate machines into a smaller number of nodes, while fulfilling the amount of hardware resources needed to preserve the quality of service of each job. This allows turning off the spare servers, thus reducing the overall datacenter power consumption. As a novelty, this policy incorporates all the virtualization overheads in the decision process. In addition, our policy is prepared to consider other important parameters for a datacenter, such as reliability or dynamic SLA enforcement, in a synergistic way with power consumption. The introduced policy is evaluated comparing it against common policies in a simulated environment that accurately models HPC jobs execution in a virtualized datacenter including power consumption modeling and obtains a power consumption reduction of 15% with respect to typical policies.Peer ReviewedPostprint (published version

Design and Optimization of High-Torque Ferrite Assisted Synchronous Reluctance Motor

Vysokomomentový asistovaný synchronní reluktanční motor může být, soudě podle nízkého počtu publikovaných článků, stále považován za relativně málo prozkoumané téma výzkumu. Tato ale i další výhody, jako nízká výrobní cena a vysoká hustota výkonu poutají pozornost výzkumných pracovníků. Navzdory tomu, že tento druh motoru je zajímavější z pohledu konvenčních nebo vysokootáčkových aplikací, tak se i trakční aplikace dostávají do popředí s tím, jak jsou objevovány vlastnosti tohoto motoru. Tato práce se zaměřuje na návrh tohoto typu motoru pro pohon lodi, který je navržen aby dosahoval vysokého momentu při nízkých otáčkách. Aplikace je definována výkonem 55 kW při 150 otáčkách za minutu a použitím levných feritových magnetů s cílem nízké ceny motoru. Návrh motoru je úzce propojen s optimalizačními algoritmy aby bylo dosaženo co nejlepšího výkonu v daném objemu stroje. Navzdory tomu, že návrh samotný je velice zajímavým tématem, tak práce deklaruje další teze, které jsou rovněž zajímavé a důležité. Vzhledem k tomu, že je práce zaměřena i na optimalizaci, tak prvním cílem práce je porovnání různých optimalizačních metod. V této práci jsou nejenom že různé druhy optimalizačních algoritmů, samoorganizující migrující algoritmus a genetický algoritmus, porovnány, ale jsou zde porovnány i různé optimalizační metody. Metoda založená na definování preferenčního vektoru a ideální multi-objektivní metody jsou v rovněž v této práci srovnány. Tyto algoritmy jsou srovnány v případě více optimalizovaných parametrů. Dalším scénářem pro porovnání ideálních multi-objektivních algoritmů je ten s menším počtem parametrů. Posledním cílem práce je laboratorní měření navrženého optimalizovaného stroje, které rovněž představuje další set výzev v této práci, které jsou diskutovány v poslední kapitole této práce.The high-torque assisted synchronous reluctance machine could be still considered, based on the relatively low amount of publications, as a rather unknown area of research. This and other main advantages, such as low manufacturing cost and a higher torque density of this machine type are driving researchers interest. Even though this machine type has become more interesting in the conventional or high-speed applications, the area of traction applications is slowly getting forward as the machine capabilities are discovered. This thesis is serving just this purpose of developing the ship propulsion driving motor, that is capable of sustaining the high-torque at low-speed. The application is defined by the 55 kW at 150 rpm using the low- cost ferrite magnets aiming to lower the cost. The design will be closely tied with optimization algorithms to deliver the best possible performance in the given volume. However the design challenge being difficult task on its own, the thesis is declaring other goals within, that are still very interesting and important. Since the optimization is included in the design process, the first goal, concluding from the given topic is to compare various optimization methods. Not only the two different optimization algorithms, self-organizing migrating algorithm and genetic algorithm, will be compared in the thesis, but even two multi-objective optimization approaches will be compared as well. The preference based vector and ideal multi-objective optimization techniques comparison will be demonstrated in one optimization scenario with a higher amount of optimized parameters. Other demonstrated goal within the thesis is the comparison of ideal multi-objective optimization with a lower number of parameters. The last goal will be the measurement of the designed and optimized machine, that introduced variety of challenges itself and all of them will be discussed within the last chapter.

An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

During general anesthesia, anesthesiologists who provide anesthetic dosage traditionally play a fundamental role to regulate Bispectral Index (BIS). However, in this paper, an optimized type-2 Self-Organizing Fuzzy Logic Controller (SOFLC) is designed for Target Controlled Infusion (TCI) pump related to propofol dosing guided by BIS, to realize automatic control of general anesthesia. The type-2 SOFLC combines a type-2 fuzzy logic controller with a self-organizing (SO) mechanism to facilitate online training while able to contend with operational uncertainties. A novel data driven Surrogate Model (SM) and Genetic Programming (GP) based strategy is introduced for optimizing the type-2 SOFLC parameters offline to handle inter-patient variability. A pharmacological model is built for simulation in which different optimization strategies are tested and compared. Simulation results are presented to demonstrate the applicability of our approach and show that the proposed optimization strategy can achieve better control performance in terms of steady state error and robustness

09081 Abstracts Collection -- Similarity-based learning on structures

From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available