A novel crossover operator for genetic algorithm: Stas crossover

The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-period aggregate production planning (APP) problem, which was used to test the algorithm, which provides optimal levels of inventory, backorders, overtime and regular production rates, and other controllable variables. According to the findings of this study, the benefit of stable crossover is that it allows for more variety in the way offspring are created and increases the opportunity for offspring to obtain good genetic information directly

A shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization

This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ε-global minimizer is proved. At each iteration k, the algorithm requires the ε(k)-global minimization of a bound constrained optimization subproblem, where ε(k) → ε. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder–Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.This work was supported by COMPETE [POCI-01-0145-FEDER-007043]; FCT-Fundacao para a Ciencia e Tecnologia within the Project Scope [UID/CEC/00319/2013]; and partially supported by CMAT-Centre of Mathematics of the University of Minho

Development of a spectral unmixing procedure using a genetic algorithm and spectral shape

xvi, 85 leaves : ill. (chiefly col.) ; 29 cmSpectral unmixing produces spatial abundance maps of endmembers or ‘pure’ materials using sub-pixel scale decomposition. It is particularly well suited to extracting a greater portion of the rich information content in hyperspectral data in support of real-world issues such as mineral exploration, resource management, agriculture and food security, pollution detection, and climate change. However, illumination or shading effects, signature variability, and the noise are problematic. The Least Square (LS) based spectral unmixing technique such as Non-Negative Sum Less or Equal to One (NNSLO) depends on “shade” endmembers to deal with the amplitude errors. Furthermore, the LS-based method does not consider amplitude errors in abundance constraint calculations, thus, often leads to abundance errors. The Spectral Angle Constraint (SAC) reduces the amplitude errors, but the abundance errors remain because of using fully constrained condition. In this study, a Genetic Algorithm (GA) was adapted to resolve these issues using a series of iterative computations based on the Darwinian strategy of ‘survival of the fittest’ to improve the accuracy of abundance estimates. The developed GA uses a Spectral Angle Mapper (SAM) based fitness function to calculate abundances by satisfying a SAC-based weakly constrained condition. This was validated using two hyperspectral data sets: (i) a simulated hyperspectral dataset with embedded noise and illumination effects and (ii) AVIRIS data acquired over Cuprite, Nevada, USA. Results showed that the new GA-based unmixing method improved the abundance estimation accuracies and was less sensitive to illumination effects and noise compared to existing spectral unmixing methods, such as the SAC and NNSLO. In case of synthetic data, the GA increased the average index of agreement between true and estimated abundances by 19.83% and 30.10% compared to the SAC and the NNSLO, respectively. Furthermore, in case of real data, GA improved the overall accuracy by 43.1% and 9.4% compared to the SAC and NNSLO, respectively

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