Meta-Heuristics (Tabu Search, Genetic Algorithms, Simulated Annealing, Ant-Colony, Particle Swarm Optimization)

Genetic algorithm (GA) and differential evolution (DE) are metaheuristic algorithms that have shown a favorable performance in the optimization of complex problems. In recent years, only GA has been widely used for single-objective optimal design of reinforced concrete (RC) structures; however, it has been applied for multiobjective optimization of steel structures. In this article, the total structural cost and the roof displacement are considered as objective functions for the optimal design of the RC frames. Using the weighted sum method (WSM) approach, the two-objective optimization problem is converted to a single-objective optimization problem. The size of the beams and columns are considered as design variables, and the design requirements of the ACI-318 are employed as constraints. Five numerical models are studied to test the efficiency of the GA and DE algorithms. Pareto front curves are obtained for the building models using both algorithms. The detailed results show the accuracy and convergence speed of the algorithms.

We apply the simulated annealing as a combinatorial optimization heuristic in some multidimensional scaling (MDS) contexts for the minimization of Stress: metric MDS, MDS with restrictions in the configuration and INDSCAL parameter estimation. The application of this technique is based on a discretization of the representation space by a grid. Results obtained are compared to those of usual well-known algorithms and are shown to be better in most of the cases.

The properties of porous hydroxyapatite (HA) bioceramic produced by heat treatment (annealing) of bovine bone were evaluated over temperatures between 400 °C and 1200 °C. The annealed body was characterized by thermal analysis (thermogravimetric analysis (TG)/differential thermal analysis (DTA)), scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX) and Fourier transformed infrared (FTIR) spectroscopy. The XRD results showed that the annealing process enhanced the crystallinity of HA phase in the bone matrix particularly when annealed above 700 °C. There was no secondary phase formation in bones annealed between 600 °C and 1000 °C. However, decomposition of HA to β-tricalcium phosphate (TCP) was observed for samples heat-treated at 1100 °C and 1200 °C. The FTIR spectra and the TG/DTA thermogram of as-received bovine bone indicated the presence of organic compounds, which upon annealing at temperatures above 600 °C was completely removed from the matrices. Bovine bone annealed between 800 °C and 1000 °C revealed the characteristics of a natural bone with the interconnecting pore network being retained in the structure.

http://www.sciencedirect.com/science/article/pii/S0272884206001581

Complexity measures can be used to predict critical information about testability, reliability and maintainability of the software systems from automatic analysis of the source code. In this paper different software complexity metrics were applied to searching algorithms. Our intention is to match vigorous object oriented applications (Visual Basic, C#, C++, Java and Python languages) of linear and binary search algorithms using software complexity metrics such as (Line of Codes (LOC), McCabe cylomatic complexity metrics and Halstead complexity metrics) and measured the sample program using LOC, McCabe method, the program difficulty using Halstead method and degree of relationship using Pearson correlation Coefficient and Statistical Analysis of Variance (ANOVA) for these three metrics. The five (5) languages correlate and do not differ significantly, then any of the five (5) programming languages is good to code linear and binary search algorithms.

Creating an efficient user knowledge model is a crucial task for web-based adaptive learning environments in different domains. It is often a challenge to determine exactly what type of domain dependent data will be stored and how it will be evaluated by a user modeling system. The most important disadvantage of these models is that they classify the knowledge of users without taking into account the weight differences among the domain dependent data of users. For this purpose, both the probabilistic and the instance-based models have been developed and commonly used in the user modeling systems. In this study a powerful, efficient and simple ‘Intuitive Knowledge Classifier’ method is proposed and presented to model the domain dependent data of users. A domain independent object model, the user modeling approach and the weight-tuning method are combined with instance-based classification algorithm to improve classification performances of well-known the Bayes and the k-nearest neighbor-based methods. The proposed knowledge classifier intuitively explores the optimum weight values of students’ features on their knowledge class first. Then it measures the distances among the students depending on their data and the values of weights. Finally, it uses the dissimilarities in the classification process to determine their knowledge class. The experimental studies have shown that the weighting of domain dependent data of students and combination of user modeling algorithms and population-based searching approach play an essential role in classifying performance of user modeling system. The proposed system improves the classification accuracy of instance-based user modeling approach for all distance metrics and different k-values.

This thesis explores the theme of improving, in terms of speed and regularity, the road-operation of public transport systems, through control strategies in real time.
Specifically in this work are highlighted intervention strategies that falls in the two categories of "Vehicle Priority" and "Vehicle Holding", potentially conflicting the one with the other.
The analysis of scientific literature and best practice has shown that the two intervention strategies, leading the first at maximizing the commercial speed and the second to the maximization of regularity of service, are currently considered one independently from the other, both in the field of practical applications than in the one of theoretical speculation.
In this work is presented a new unified approach that reconciles both the strategies of intervention; moreover a method is introduced, implemented into a prototype software, for the calculation of the optimal strategy of the vehicle operating through regulation of fixed control points (eg. Signals road traffic lights) to allow a joint pursuit of speed maximization objectives and regularity of service.
The optimization method here formulated and tested is based on the combined use of (meta) heuristics (Genetic Algorythm, PSO and Hill Climbing) and traffic simulation methods able to reproduce and predict the temporal trajectories of individual public transport vehicles. The short-term prediction capabilities inherent in the use of simulation tools allows to generate solutions to the regulation problem which are extended "forward" both in the road space and time, thus allowing an approach to regulation that is both anticipatory and non-local. These characteristics allow in general to achieve road capacity relocation between neighboring intersections thus enabling, where possible, to reduce the competition between different modes of transport in the use of road capacity.
In this work are finally presented the results of a series of tests carried out both in the case of realizing the control through a normal signal pattern with ripetitive cycle and phases for each signal, both in the hypothesis less restrictive of red and green times generally variables during time for each signal.
The calculation effort of the prototype also suggest the possibility, through further developments, to use this method for real-time applications.
This PHD thesis is written in Italian Language.

Technological innovations in soft computing techniques have brought automation capabilities to new levels of applications. Process control is an important application of any industry for controlling the complex system parameters, which can greatly benefit from such advancements. Conventional control theory is based on mathematical models that describe the dynamic behaviour of process control systems. Due to lack in comprehensibility, conventional controllers are often inferior to the intelligent controllers. Soft computing techniques provide an ability to make decisions and learning from the reliable data or expert’s experience. Moreover, soft computing techniques can cope up with a variety of environmental and stability related uncertainties. This paper explores the different areas of soft computing techniques viz. Fuzzy logic,
genetic algorithms and hybridization of two and abridged the results of different process control case studies. It is inferred from the results that the soft computing controllers provide better control on errors than conventional controllers. Further, hybrid fuzzy genetic algorithm controllers have successfully
optimized the errors than standalone soft computing and conventional techniques.

Real world problems like Travelling Salesman
Problem (TSP) belong to NP-hard optimization problems
which are difficult to solve using classical mathematical
methods. Therefore, many alternate solutions have been
developed to find the optimal solution in shortest possible
time. Nature-inspired algorithms are one of the proposed
solutions which are successful in finding the solutions that
are very near to the optimal. In this paper, Classical TSP
(CTSP) along with its variant Random TSP (RTSP) are
solved using various meta-heuristic algorithms and their
performance is compared on the basis of tour length.
Results show that the Nature-inspired algorithms
outperform both Traditional and Evolutionary algorithms
and obtain optimal solutions.

A produção de grades horárias (Timetabling) escolares e acadêmicas é um problema complexo de alocação de um conjunto de eventos (exames, seminários, projetos ou aulas) e um conjunto de agentes (instrutores, monitores ou professores) em um conjunto finito de horários, sujeito a um amplo conjunto de restrições. O problema deve ser resolvido de forma a maximizar a possibilidade de alocação ou minimizar as violações de restrições. Em muitas instituições de ensino, este trabalho é realizado manualmente, necessitando de vários profissionais, gastando um longo tempo e nem sempre produzindo a melhor alocação de recursos para a instituição. Este trabalho aborda teoria e aplicações de Algoritmos Genéticos (AGs) na resolução de Problemas de
Timetabling em instituições de ensino fundamental, médio e universitário. Análises realizadas neste trabalho - de diversos artigos, dissertações e monografias publicados na literatura - mostram que os AGs utilizados na resolução do problema em questão, quando testados em diferentes situações, forneceram resultados satisfatórios - tanto referentes a qualidade das soluções obtidas, quanto ao tempo gasto para produzi-las - constituindo-se, portanto em uma ferramenta adequada para resolver problemas de Timetabling escolar e acadêmico.

Grid computing is a group of physically connected computers that performs dedicated tasks. And to use grid computing
efficiently it is necessary to schedule the jobs on correct resources. To serve this purpose several scheduling algorithms have
been developed specifically for grid computing environment. Some of these algorithms are traditional which can be applied to
other job scheduling environment too such as in operating system etc. But many algorithms are designed particularly for grid
environment and thus are too complex to be used in other cases. This survey paper presents few such algorithms that have been
developed long ago but are in constant research and thus proved to be more efficient than traditional ones. The paper presents
the theory behind these algorithms and various assumptions and steps involved.

Currently, most companies that act on both domestic and global markets experience a growing international competition and recognize the importance to reduce manufacturing lead time. One of the industries that has to face this globalization
challenges is the machining industry. In general, machining can be done manually or automatically, better known as Computer Numerical Control (CNC). It is reported that tool movement and tool switching time in machining process take most of the total time in a manufacturing process. Optimization of tool path operation can lead to significant reduction in non-productive machining time. This paper focuses on the development of
Ant Colony Optimization (ACO) model by using Matlab® software. In searching for the optimize tool path for machining operation in incremental positioning axis movement is based on the Travelling Salesman Problem (TSP) principles. Results
shows that with the increasing of the hole drilling operation and also its trail evaporation rate (ρ), will reduce the ACO accuracy. Overall, the objective of this study to produce the optimize tool path by ACO algorithm programming.

In this study, we investigate an adaptive decomposition strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics. Initially, the examinations are placed into the easy set. During the construction process, the examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment than the others for the subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set. Within the easy set, a new subset called the boundary set
is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach which incorporates different ordering and shuffling strategies is explored
on the Carter benchmark problems. The empirical results show that its performance is promising and comparable to existing constructive approaches.

Currently, most companies that conduct business in both domestic and global markets are facing
growing international competition and recognize the importance of reducing manufacturing lead time, for
example, companies in the machining industry. In general, machining can be performed manually, automatically
or by Computer Numerical Control (CNC). The optimization of tool routing path operation in machining can lead
to significant reduction in non-productive machining time. This paper focuses on the development of the Ant
Colony Optimization (ACO) algorithm for use in searching for the optimal tool routing path and the relationship
with its control parameter values. The simulation of a machining operation is conducted in MATLAB with
incremental positioning axis movement based on the Travelling Salesman Problem (TSP). The results indicate
that the relationship between the travelled distance of the ant and the ant’s control parameters are significant
in increasing the hole drilling operation efficiencies. This result indicates that ACO algorithm programming can
be used to optimize the tool routing path.

HEURISTICS and PROBLEM-SOLVING‭ (‬VOLUME‭ ‬2‭)
This section or chapter two.‭ ‬Because of its length I decided to create a second Volume‭ ‬2.‭ ‬HEURISTICS AND PROBLEMSOLVING‭ (‬Volume‭ ‬2‭)

This volume deals with details of heuristic approaches and the infinite aspects and features of‭ ‘‬problem-solving‭’ ‬and related issues.

The author of the first article I quote suggests that the heuristic tools or devices he mentions will enable individuals to produce philosophy.‭ ‬He seems to think that this idea is one of the major factors that leads to the creation of philosophy.

I wish to indicate,‭ ‬by citations,‭ ‬that there is much,‭ ‬much more to heuristics then the list of heuristics he suggests.

I place the use of heuristic devices in the larger context of problem-solving.‭ ‬The solving of problems is of course merely one aspect of a much larger process that consist of many other features,‭ ‬steps and stages.



The aim of that section and citations are to to make individuals aware of the many aspects of the process of problem conceptualization,‭ ‬investigation and solving or dissolving.‭ ‬I think it is is essential to be aware of these features of problem investigation because without such knowledge and understanding philosophers will suffer from an even greate lack of meta-cognition of the socio-cultural practice of philosophy and the doing of philosophy and of self-‭ ‬metacognition.

Introduction The variety and abundance of qualitative characteristics of agricultural products have been the main reason for the development of different types of Non-Destructive Methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. The need to intensify the quality and quantity of a product leads to the use of advanced automated machines. Machine tools are increasingly becoming more automated and less reliant on human factors. Nowadays, visual machine technology and image processing techniques are widely used in the industry, particularly common in product quality control, robot guidance and self-guided mechanisms.

Hybrid Metaheuristics Optimization have emerged along the paradigm itself. Now, they are very famous because the hybrid metaheuristics methods give best results for combinatorial optimization problems compared to exact methods. In this paper, we will propose Metaheuristic method which are applied to difficult problems. This method is based on hybridization between population based solution methods like Ant colony optimization (ACO) and standard Particle swarm optimization (SPSO) algorithms and single based solution methods like 2-Opt algorithm. Our developed approach is called " Standard Ant Supervised by PSO " (SAS-PSO-2Opt) applied to routing problem like Traveling Salesman Problem (TSP), which is considered as NP-complete problem. Therefore, the ACO algorithm can explore the search space, PSO algorithm is used to optimize the ACO parameters (α, β, ρ) and the 2-Opt algorithm improves the obtained solution and reduce the probability of falling into a local minimum. To evaluate our proposed hybrid approach, we have used several standard tests benches from TSPLIB and we have compared the results with other hybrid metaheuristics approaches from litterature.

Scheduling is an important problem in manufacturing environment. Many techniques have been generated for these kinds of problems, and in recent years, modern heuristic techniques are the mostly used of them. In this study, scheduling problems are examined in terms of modern heuristic techniques, and a genetic algorithm (GA) was used for developing a model. This model is a group-based scheduling problem on single machine - rotational molding process of plastic parts. Since the products have different shapes and volumes, being processed on the same machine, the major loss of the system is the set up times. Furthermore, the machine utilization is the other critical measure, because of differences in dimensions that have an impact on the number of parts produced in the same group. The scheduling
algorithm developed especially focuses on these two critical objectives; therefore the problem can be defined as a multi-objective scheduling. The most important part of a genetic algorithm is the objective function, which is called “evaluation function” in GA terminology; the other properties of GAs are similar to each other. In literature, most evaluation functions are based upon only one objective and include relatively less information. In this study, discriminant analysis on normalized rank values generates the evaluation function. First of all, terms in objectives (variation of setup time within the group, total volume, process time/ setup time) are ranked according
to the appropriate sequence, and to eliminate the effect of size of ranks, ranks are normalized by their own mean rank value. At the end of the analysis, sequence weights of each term of objectives are obtained, then these weights are used as coefficients of terms in the objective function. Finally, by using this function as priority, and the operators “crossover” and “mutation”, the best sequences of groups
are obtained and a successful schedule is developed. MS-Access and its Visual Basic Module are used for creation of the initial population and execution of the algorithm.

Keywords: Scheduling, heuristic techniques, genetic algorithm.

The present research reports comparative analysis of effects of cold working (CW) and annealing on tensile and impact-toughness behavior of 7075 Al alloy. Cold-rolled samples were annealed at various temperatures in the range of 225-345 °C for 5 min. A remarkable increase in ductility and impact toughness was observed when specimens were annealed at temperatures above 265 °C for 5 min. It was also found that cold rolling has a profound effect on strength anisotropy that enhances with amount of % CW. The maximum strengths were observed in the transverse direction in the investigated alloy. Cold rolling has been found to impart a significant effect on decreasing the impact toughness of alloy that enhance with amount of % CW; this loss in impact energy could not be compensated by recrystallization process. It has also been shown that impact test can be considered as a simple method for measurement of toughness and plastic anisotropy in sheet and plate. The analysis of the fracture surfaces with the scanning electron microscope presented dimpled morphology for the failure ductile mechanism in starting material and fibrous structure with some quasi-cleavage regions in cold-rolled samples, corresponding to the ductile to brittle fracture mechanism. © 2009 Elsevier Ltd. All rights reserved.

http://www.sciencedirect.com/science/article/pii/S0734743X09001626 
http://ac.els-cdn.com/S0734743X09001626/1-s2.0-S0734743X09001626-main.pdf?_tid=fe34f71c-a9c5-11e3-8713-00000aab0f27&acdnat=1394615539_21f4c672d114900d495327e1b3a6de00

Esta tesis aborda el Problema Árbol de Expansión con incertidumbre intervalar en los costos,utilizando el criterio de Min-Max Regret. Varios algoritmos son implementados, tanto exactos como heurísticos. Con respecto a los algoritmos exactos, se implementan Descomposición de Benders y Branch and Cut , ambos incluyen variantes. Branch and Cut logra superar al resto de los algoritmos, incluso obteniendo gaps menores a un 10%, para 100 nodos, en un conjunto de instancias. En relación a las heurísticas, se desarrolla una heurística constructiva, la que utiliza la información de los intervalos,a diferencia de todas las aproximaciones de la literatura. Se proponen metaheurísticas basadas enBúsqueda Local (Mejora iterativa, Simulated Annealing
y GRASP), se obtienen gaps de calidad,incluso para las instancias más complejas.Se realiza una comparación desde un punto de vista experimental, mostrando un buen desempeño, pues se igualan o mejoran los resultados existentes.

"This paper deals about the methodology of a plate structural optimization problem. Several results are also showed. In the last decades this topic has been studied with different approaches: e.g. euristic methods, fuzzy methods etc. Most of these works analyze one dimension structure and rarely two dimensional one. For this reason in this work R.C. bending plates are considered.
The analysis uses the yielding lines method, principle of virtual work, the Simulated Annealing (SA) algorithm and an original procedure implemented in MATLAB environment.
The objective is the optimal arrangement of reinforcing bars and the optimal value of the plate thickness in order to have the minimal cost in accordance with the Ultimate Limit State (SLU). The best solution is studied with different market prices.
The methodology has shown its greatest application in the design of many different systems of plates, but in particular for projects with components in series where the choice to follow a certain arrangement of reinforcing bars or another can lead to significant cost differences.
In conclusion, the results show that the optimal choice is not affected by changes in market prices or by the characteristics of the materials revealing a remarkable stability of the solution"

In literature, benchmark test functions have been used for evaluating performance of metaheuristic algorithms. Algorithms that perform well on a set of numerical optimization problems are considered as effective methods for solving real-world problems. Different researchers choose different set of functions with varying configurations, as there exists no standard or universally agreed test-bed. This makes hard for researchers to select functions that can truly gauge the robustness of a metaheuristic algorithm which is being proposed. This review paper is an attempt to provide researchers with commonly used experimental settings, including selection of test functions with different modalities, dimensions, the number of experimental runs, and evaluation criteria. Hence, the proposed list of functions, based on existing literature, can be handily employed as an effective test-bed for evaluating either a new or modified variant of any existing metaheuristic algorithm. For embedding more complexity in the problems, these functions can be shifted or rotated for enhanced robustness.

A Constraint Satisfaction Problem (CSP) is a powerful framework for representing and solving constraint problems. When solving a CSP using a backtrack search method, one important factor that reduces the size of the search space drastically is the order in which variables and values are examined. Many heuristics for static and dynamic variable ordering have been proposed and the most popular and powerful are those that gather information about the failures during the constraint propagation phase, in the form of constraint weights. These later heuristics are called conflict driven heuristics. In this paper, we propose two of these heuristics respectively based on Hill Climbing (HC) and Ant Colony Optimization (ACO) for weighing constraints. In addition, we propose two new value ordering techniques, respectively based on HC and ACO, that rank the values based on their ability to satisfy the constraints attached to their corresponding variables. Several experiments were conducted on various types of problems including random, quasi random and patterned problems. The results show that the proposed variable ordering heuristics, are successful especially in the case of hard random problems. Also, when using the proposed value and variable ordering together, we can improve the performance particularly in the case of random problems.

The optimization of a system is the quest for its best performance. Thus physical systems tend to reach minimum energy states, while biological systems optimize their own genetic code and behaviour to better cope with the environment. If an artificial system or the model of a natural system can be parameterized, its optimization consists of seeking the best combination of feasible values of those parameters which results in its best performance. This thesis deals with the ‘particle swarm optimization’ method, which differs from traditional methods in that it poses no restriction to the functions involved. The method was inspired by social behaviour observed in nature, and hence its robustness lies in that it is not deterministically implemented to optimize so that there is no problem-specific implementation that may be inadequate for a different problem.
An extensive study of the coefficients at the core of the method is carried out partly theoretically, partly heuristically, and partly visualizing trajectories. The influence of their settings on the form and speed of convergence is analyzed, and guidelines as to how to obtain the desired behaviour are provided. Different structures of the social network and additional heuristics are studied and tested on unconstrained benchmark problems. Finally, a robust pseudo adaptive constraint-handling mechanism is proposed. The fully working algorithm is tested on a classical benchmark suite of constrained problems and successfully applied to well-known engineering problems. Results reported by other authors are also provided for reference.
The ‘particle swarm optimizer’ developed in this thesis is a global, single-solution, single-objective, gradient-free, population-based method, which is able to handle continuous –exceptionally, discrete variables by rounding-off–, constrained and unconstrained, single-objective problems regardless of whether the functions involved are or are not linear, convex, unimodal, differentiable, smooth, continuous, or even explicit.

The power system blackout history of last two decades is presented.Conventional load shedding techniques, their types and limitations are presented.Applications of intelligent techniques in load shedding are presented.Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.The discussion and comparison between these techniques are provided.Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.

"The research described in this thesis is focused on the design, analysis, and optimization
of ultra-wideband (UWB) CMOS Low Noise Amplifier (LNA). First basic principles of
LNA are discussed. Then based on a literature review of LNA design, the proposed LNAs
are presented.
In the first work, a 18 GHz LNA is proposed with using 0.13 μm technology. In the
designed LNA, the structure of one-stage cascode amplifier with source inductive
degeneration is used. After that, two evolutionary and statistical approaches are used for
optimizing LNA. Based on genetic algorithm (GA), simulated annealing (SA), and circuit
simulator, the simulation-based methods simultaneously optimize the electrical
specifications such as S-parameters and the noise figure (NF). It also operates with a 1.2V
power supply drawing a current of 12 mA. The LNA demonstrates 17.62 dB for S21 at the
peak gain frequency of 17.49 GHz. The optimized GA simulation results show that the
proposed LNA has a noise figure of 1.8 dB in the 17.49 GHz frequency. For large input
signal level, P1-dB, OIP3, and IIP3 are: -8 dBm, +23 dBm and +11 dBm, respectively.
In the second work, a novel source degenerated UWB LNA is designed based on the
current-reused topology. With two stages modified current-reused cascode structure, Like
the previous design, GA and SA are used for optimizing. The optimized GA simulation
results show that the proposed LNA has more than a 25 dB S21 and 2-3 dB noise figure
from 3.1 GHz to 10.6 GHz. The power dissipation of this circuit is approximately 18
mW. They also show that this LNA has a good linearity performance with an IIP3, OIP3,
and P1-dB are +2 dBm, +30 dBm and -18 dBm, respectively. For this design, S11 and S22
are lower than -10 dB. Meanwhile, an excellent S12 of less than -100 dB is obtained."
Keywords: ultra-wideband (UWB) – CMOS Low Noise Amplifier (LNA) – genetic
algorithm (GA) – simulated annealing (SA) – circuit simulator.

ISO 26262, titled Road Vehicles–Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to accomplish the goal of designing and developing dependable automotive systems , ISO 26262 uses the concept of Automotive Safety Integrity Levels (ASILs), the adaptation of Safety Integrity Levels. ASILs are allocated to the components and subsystems that can cause system failure and malfunctions that lead to hazards. ASILs allocation is a hard problem consists of finding the optimal allocation of safety levels to the system architecture which must guarantee that the highest safety requirements are met while development cost of the automotive system is kept minimum. There were many successful attempts to solve this problem using different techniques. However, it is worth pointing out that there is an absence of a review that provides an in-depth study of all the existing methods and highlights their merits and demerits. This paper presents an overview of different approaches that were used to solve ASILs allocation problem. The review provides an overview of safety requirements including the related standards followed by a study of the resolution methods of the existing approaches. The study of each approach provides a detailed explanation of the used methodology and a discussion of its strength and weaknesses including the main open challenges.

The numbers countries in the world vulnerable to natural hazards because of its geographic location. It also lacks an efficient disaster management system that will help in times of need. One common scenario during disasters is that the activity of rescue and relief is not well-coordinated. For this reason, there is a need for a system that will help in the efficient provision of rescue and relief to disaster-affected areas. Since the use of smart phones is gaining interest in people, the disaster management system was implemented as a smart phone application using Google’s Android operating system. The disaster management system Android application known as MyDisasterDroid determines the optimum route along different geographical locations that the volunteers and rescuers need to take in order to serve the most number of people and provide maximum coverage of the area in the shortest possible time. Genetic algorithm was applied for optimization and different parameters were varied to determine the most optimum route.

Nowadays, transformers are made of conventional magnetic cores which are constructed of a single grainoriented or amorphous, magnetic steel. Even though, the transformer is the most efficient of electrical machines, with efficiencies typically above 90%, it is possible to improve transformer performance by using composite magnetic cores. Patents related to this simple and effective technique can be traced back to 1929. The specific technique can be applied to wound core distribution transformers. By using wound cores constructed with a combination of conventional and high permeability grain-oriented steel the total owing cost (TOC) of the transformer can be reduced effectively. This paper presents a brief review of patents on wound and composite magnetic cores and introduces a generalized technique for the determination of the optimum design variables of a new composite wound core design.

In this investigation, anatase TiO 2 thin films were grown by radio frequency magnetron sputtering on glass substrates at a high sputtering pressure and room temperature. The anatase films were then annealed at 300-600 °C in air for a period of 1 hour. To examine the structure and morphology of the films, X-ray diffraction (XRD) and atomic force microscopy (AFM) methods were used respectively. From X-ray diffraction patterns of the TiO 2 films, it was found that the as-deposited film showed some differences compared with the annealed films and the intensities of the peaks of the crystalline phase increased with the increase of annealing temperature. From AFM images, the distinct variations in the morphology of the thin films were also observed. The optical constants were characterized using the transmission spectra of the films obtained by UV-VIS-IR spectrophotometer. Besides, optical thickness of the film deposited at room temperature was calculated and cross-checked by taking a cross-sectional image through SEM. The optical band gaps were evaluated through Tauc model. It was observed that TiO 2 films produced at room temperatures exhibited high visible transmittance and transmittance decreased slightly with the increase of annealing temperatures. The films were found to be crystalline having anatase phase. The refractive index of the films was found from 2.31-2.35 in the visible range. The extinction coefficient was nearly zero in the visible range and was found to increase with annealing temperature. The allowed indirect optical band gap of the films was estimated to be in the range from 3.39 to 3.42 eV which showed a small variation. The allowed direct band gap was found to increase from 3.67 to 3.72 eV. The porosity was also found to decrease at a higher annealing temperature making the film compact and dense.

This paper has analyzed the optimal location of TDs, for which the study has focused in [2], which has developed a methodology, based on genetic algorithms to determine the size, location, number of distribution transformers that supply the necessary power to the voltage levels established for low voltage electrical networks, in addition to finding the radial topology associated with each distribution transformer. The aim of this work is to minimize the total cost of investment includes the unit cost of distribution transformers, the cost of power losses and power lines and transformers, and the cost of construction of medium voltage lines feeding the transformer. We have developed a method for single-objective optimization based on genetic algorithms that is able to find the best solution to the problem, or at least as close as possible to a global optimum.