Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

Particle swarm optimization using dimension selection methods

a b s t r a c t Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the PSO, and then gives a PSO variant without randomness to show that traditional PSO cannot work without randomness. Based on our analysis of the randomness, another way of using randomness is proposed in PSO with random dimension selection (PSORDS) algorithm, which utilizes random dimension selection instead of stochastic coefficients. Finally, deterministic methods to do the dimension selection are proposed, and the resultant PSO with distance based dimension selection (PSODDS) algorithm is greatly superior to the traditional PSO and PSO with heuristic dimension selection (PSOHDS) algorithm is comparable to traditional PSO algorithm. In addition, using our dimension selection method to a newly proposed modified particle swarm optimization (MPSO) algorithm also gets improved results. The experiment results demonstrate that our analysis about the randomness is correct and the usage of deterministic dimension selection method is very helpful

Identification of optimal values of worm gearboxes efficiency

U disertaciji su prikazani rezultati eksperimentalnih istraživanja i optimizacije stepena iskorišćenja jednostepenog pužnog prenosnika koji je posebno konstruisan i izrađen za tu namenu. Ispitivanja su izvršena na uređaju AT200 uz variranje relevantnih faktora (ulaznih brojeva obrtaja, opterećenja i ulja različitih viskoznosti). Za ispitivanje su korišćena tri pužna para sa istim geometrijskim karakteristikama sa puževima izrađenim od istog materijala (kaljeni i brušeni čelik 42CrMo4) i pužnim zupčanicima izrađenim od kalajne bronze CuSn12, cinkaluminijumske legure ZA12 i aluminijumske legure A356. Vrednosti stepena iskorišćenja su određene na osnovu izmerenih vrednosti ulaznih i izlaznih obrtnih momenata za različite uslove ispitivanja. Za date radne uslove izvršen je proračun gubitaka snage u prenosniku na osnovu kojih su određene vrednosti stepena iskorišćenja pužnih parova, kao i vrednosti koeficijenta trenja između spregnutih bokova zubaca zupčanika. Takođe, nakon određenog perioda ispitivanja vršena je demontaža pužnih parova, merenje njihove mase i ponovna montaža, kako bi se utvrdila veličina habanja u realnom vremenu ispitivanja. Pored ispitivanja stepena iskorišćenja izvršena su i tribološka ispitivanja na kompjuterski podržanom tribometru TPD 95 sa kontaktnom geometrijom blok na disku. Pri tome su određeni uslovi ispitivanja koji odgovaraju režimima rada pužnog prenosnika definisanjem normalnog opetrećenja, obimne brzine i puta klizanja. U ovom slučaju su praćene vrednosti koeficijenta trenja kontaktnih elemenata sa ciljem izbora materijala sa najboljim tribološkim karakteristikama. Na kraju je izvršena optimizacija stepena iskorišćenja prenosnika primenom genetskog algoritma (GA) i algoritma za optimizaciju rojeva čestica (PSO). Kao funkcija cilja korišćen je matematički model koji je kreiran na osnovu eksperimentalno dobijenih podataka kojim se najbolje aproksimiraju vrednosti stepena iskorišćenja. Takođe, izvršeno je predviđanje stepena iskorišćenja primenom veštačke neuronske mreže (ANN). Procesom optimizacije i predviđanja određene su kombinacije ulaznih faktora prema kojima se dobijaju minimalna i maksimalna vrednost stepena iskorišćenja. Sveobuhvatnom analizom rezultata eksperimentalnih istraživanja određen je uticaj pojedinih faktora na stepen iskorišćenja prenosnika, gubitke snage, kao i na tribološke karakteristike pužnih parova i kontaktnih elemenata. Pri tome je utvrđeno da vrsta materijala pužnog zupčanika ima najveći uticaj na posmatranu karakteristiku. Sa druge strane, rezultati optimizacije pokazuju da se za iste vrednosti promenljivih dobija maksimalna (optimalna) vrednost stepena iskorišćenja čime se potvrđuju rezultati eksperimentalnih istraživanja.This dissertation presents the results of experimental research and optimisation of the degree of utilization of a single-stage worm gearbox that was specially designed and made for that purpose. Tests were performed on the AT200 device with varying relevant factors (input revolutions, load and oils of different viscosities). Three worm pairs with the same geometric characteristics were used for testing, with worms made of the same material (hardened and ground steel 42CrMo4) and worm gears made of tin bronze CuSn12, zinc-aluminium alloy ZA12 and aluminium alloy A356. The efficiency values were determined based on the measured values of input and output torques for different test conditions. For given operating conditions, the power losses in gearbox were calculated, based on which the efficiency values of worm pairs as well as the values of coefficient of friction between meshed flanks of teeth gear were determined. Furthermore, in order to determine the wear size in real test time after a certain period of testing, dismantling of worm pairs were conducted, the measurement of their mass and their reassembling. In addition to efficiency testing, tribological tests were performed on a computersupported TPD 95 tribometer with block-on-disk contact geometry. Thereby, the test conditions corresponding to the operating modes of the worm gearbox were determined by defining the normal load, circumferential velocity and slide path. In this case, the values of the friction coefficient of the contact elements were monitored with the aim of selecting the material with the best tribological characteristics. Finally, the optimisation of the gearbox efficiency was carried out using genetic algorithm (GA) and particle swarm optimisation algorithm (PSO). As the objective function a mathematical model was used, which was created according to experimentally obtained data that best approximates the values of the degree of utilization. Furthermore, the degree of utilization was predicted using an artificial neural network (ANN). Through the process of optimization and forecasting, combinations of input factors are determined according to which the minimum and maximum value of the degree of utilization is obtained. The influence of certain factors on the gearboxes efficiency, power losses, as well as on the tribological characteristics of worm pairs and contact elements were determined through a comprehensive analysis of the results of experimental research. Thereby, it was determined that the type of material of the worm gear has the greatest influence on the observed characteristic. On the other hand, the optimisation results show that for the same values of the variables the maximum (optimal) efficiency value is obtained, which confirms the results of experimental research

Development and optimization of aluminum nanocomposites for production of tribological elements

Rezime: Pri razvoju i proizvodnji novih materijala i elemenata eksperiment ima značajnu ulogu. Pristup eksperimentalnom istraživanju ne može se zamisliti bez upotrebe dizajna eksperimenta čijom primenom se pristupa sistematskom načinu planiranja eksperimenta, izvođenju i interpretaciji rezultata eksperimenata. Za potrebe ovog rada su razvijeni, a u radu su analizirani novi nanokompoziti sa A356 osnovom ojačani SiC i Al2O3 nanočesticama različitih veličina i sadržaja. U okviru ispitivanja određene su i prikazane fizičke i mehaničke karakteristike nanokompozita. U radu su sprovedena tribološka ispitivanja primenom dizajna eksperimenta za prvu seriju materijala koji do trenutka proizvodnje nisu bili predmet ranijih istraživanja. Nanokompoziti su proizvedeni sa malim masenim sadržajem ojačavajućih nanočestica primenom modifikovanog kompokasting procesa. Ostvareni eksperimentalni rezultati prvom serijom materijala ukazali su na pravac i tok razvoja nanokompozita sa novim sadržajem ojačavača. Druga faza istraživanja nanokompozita je usmerena na tribološka ispitivanja jer se prvom serijom materijala dokazalo da nije ostvareno značajno poboljšanje u mehaničkim i tribološkim karakteristikama nanokompozita. Izvršena je analiza pohabanih površina nanokompozita što je od velikog značaja za praktičnu primenu ovih materijala. Primenom optimizacionih metoda izvršena je višekriterijumska optimizacija i određena optimalna kombinacija faktora kojom se postižu najbolje karakteristike nanokompozita. Na osnovu eksperimentalnih istraživanja ostvarenih u ovoj disertaciji može se zaključiti da su ostvarena poboljšanja u mehaničkim i tribološkim karakteristikama nanokompozita u poređenju sa osnovnom legurom. Područje primene aluminijumskih nanokompozita neprekidno se širi s obzirom na kombinaciju svojstava koja se mogu postići dodavanjem različitih ojačavača. Dobijene karakteristike razvijenih nanokompozita omogućavaju njihovo korišćenje pri modeliranju i naponsku analizu različitih mašinskih elemenata u CAD softveru. Izvršena je numerička analiza zupčastih parova i ustanovljeno je da se maksimalne vrednosti ekvivalentnog napona javljaju u podnožju zubaca spregnutih zupčanika. Primenom nanokompozita za izradu zupčastog para može se postići veći prenos snage u odnosu na zupčasti par izrađen od osnovne legure, zatim smanjuje se pojava inicijalnih prslina, masa prenosnika, i nivo buke i vibracije u zupčastim prenosnicima manjih snaga, a povećava se njihova otpornost na habanje.Abstract: The experiment has a significant role in the development and production of new materials and machine elements. An approach to experimental research cannot be imagined without design of experiment usage, which repesents a systematic way of planning an experiment, performing and interpreting the experiments results. For the purposes of this thesis, new nanocomposites with A356 base reinforced with SiC and Al2O3 nanoparticles of different sizes and contents were developed and analyzed. Within this research, the physical and mechanical characteristics of nanocomposites were determined and presented. Tribological tests were performed using the design of experiment for the first series of materials that were not the subject of previous research, in today’s literature sources, until the time of production. Nanocomposites were produced with a low mass content of reinforcing nanoparticles using a modified compocasting process. The achieved experimental results with the first series of materials indicated the direction and course of development of nanocomposites with a new content of reinforcements. The second phase of nanocomposite research is focused on tribological tests because the first series of materials didn’t proved the significant improvement in the mechanical and tribological characteristics of nanocomposites is achieved. The analysis of worn surfaces of nanocomposites was performed, which is of great importance for the practical application of these materials. By applying optimization methods, multicriteria optimization was performed and the optimal combination of factors was determined for which gives the nanocomposites of the best characteristics. Based on the experimental research achieved in this dissertation, it can be concluded that improvements have been made both in the mechanical and tribological characteristics of nanocomposites compared to the base alloy. Application field of aluminum nanocomposites is constantly expanding due to the combination of properties that can be achieved by adding different reinforcements. The obtained characteristics of the developed nanocomposites enable their usage in modeling and stress analysis of various machine elements in CAD software. Stress analysis of gear pairs was performed and it was concluded that the maximum values of equivalent stress occur at the base of the teeth of the coupled gears. The use of nanocomposites for the production of gear pair can achieve a higher power transmission compared to the gear pair made of base alloy, then reduces the occurrence of initial cracks, gear mass, and noise and vibration levels in gears of lower power, and increases their wear resistance