Abstract: Nowadays, more engineering systems are using some
kind of Artificial Intelligence (AI) for the development of their
processes. Some well-known AI techniques include artificial neural
nets, fuzzy inference systems, and neuro-fuzzy inference systems
among others. Furthermore, many decision-making applications base
their intelligent processes on Fuzzy Logic; due to the Fuzzy
Inference Systems (FIS) capability to deal with problems that are
based on user knowledge and experience. Also, knowing that users
have a wide variety of distinctiveness, and generally, provide
uncertain data, this information can be used and properly processed
by a FIS. To properly consider uncertainty and inexact system input
values, FIS normally use Membership Functions (MF) that represent
a degree of user satisfaction on certain conditions and/or constraints.
In order to define the parameters of the MFs, the knowledge from
experts in the field is very important. This knowledge defines the MF
shape to process the user inputs and through fuzzy reasoning and
inference mechanisms, the FIS can provide an “appropriate" output.
However an important issue immediately arises: How can it be
assured that the obtained output is the optimum solution? How can it
be guaranteed that each MF has an optimum shape? A viable solution
to these questions is through the MFs parameter optimization. In this
Paper a novel parameter optimization process is presented. The
process for FIS parameter optimization consists of the five simple
steps that can be easily realized off-line. Here the proposed process
of FIS parameter optimization it is demonstrated by its
implementation on an Intelligent Interface section dealing with the
on-line customization / personalization of internet portals applied to
E-commerce.
Abstract: In the present study the efficiency of Big Bang-Big
Crunch (BB-BC) algorithm is investigated in discrete structural
design optimization. It is shown that a standard version of the BB-BC
algorithm is sometimes unable to produce reasonable solutions to
problems from discrete structural design optimization. Two
reformulations of the algorithm, which are referred to as modified
BB-BC (MBB-BC) and exponential BB-BC (EBB-BC), are
introduced to enhance the capability of the standard algorithm in
locating good solutions for steel truss and frame type structures,
respectively. The performances of the proposed algorithms are
experimented and compared to its standard version as well as some
other algorithms over several practical design examples. In these
examples, steel structures are sized for minimum weight subject to
stress, stability and displacement limitations according to the
provisions of AISC-ASD.
Abstract: One of the main objectives of order reduction is to
design a controller of lower order which can effectively control the
original high order system so that the overall system is of lower
order and easy to understand. In this paper, a simple method is
presented for controller design of a higher order discrete system.
First the original higher order discrete system in reduced to a lower
order model. Then a Proportional Integral Derivative (PID)
controller is designed for lower order model. An error minimization
technique is employed for both order reduction and controller
design. For the error minimization purpose, Differential Evolution
(DE) optimization algorithm has been employed. DE method is
based on the minimization of the Integral Squared Error (ISE)
between the desired response and actual response pertaining to a
unit step input. Finally the designed PID controller is connected to
the original higher order discrete system to get the desired
specification. The validity of the proposed method is illustrated
through a numerical example.
Abstract: The Genetic Algorithm (GA) is one of the most important methods used to solve many combinatorial optimization problems. Therefore, many researchers have tried to improve the GA by using different methods and operations in order to find the optimal solution within reasonable time. This paper proposes an improved GA (IGA), where the new crossover operation, population reformulates operation, multi mutation operation, partial local optimal mutation operation, and rearrangement operation are used to solve the Traveling Salesman Problem. The proposed IGA was then compared with three GAs, which use different crossover operations and mutations. The results of this comparison show that the IGA can achieve better results for the solutions in a faster time.
Abstract: In this paper we propose a simple adaptive algorithm
iteratively solving the unit-norm constrained optimization problem.
Instead of conventional parameter norm based normalization,
the proposed algorithm incorporates scalar normalization which is
computationally much simpler. The analysis of stationary point is
presented to show that the proposed algorithm indeed solves the
constrained optimization problem. The simulation results illustrate
that the proposed algorithm performs as good as conventional ones
while being computationally simpler.
Abstract: While to minimize the overall project cost is always
one of the objectives of construction managers, to obtain the
maximum economic return is definitely one the ultimate goals of the
project investors. As there is a trade-off relationship between the
project time and cost, and the project delivery time directly affects the
timing of economic recovery of an investment project, to provide a
method that can quantify the relationship between the project delivery
time and cost, and identify the optimal delivery time to maximize
economic return has always been the focus of researchers and
industrial practitioners. Using genetic algorithms, this study
introduces an optimization model that can quantify the relationship
between the project delivery time and cost and furthermore, determine
the optimal delivery time to maximize the economic return of the
project. The results provide objective quantification for accurately
evaluating the project delivery time and cost, and facilitate the
analysis of the economic return of a project.
Abstract: In deregulated operating regime power system security is an issue that needs due thoughtfulness from researchers in the horizon of unbundling of generation and transmission. Electric power systems are exposed to various contingencies. Network contingencies often contribute to overloading of branches, violation of voltages and also leading to problems of security/stability. To maintain the security of the systems, it is desirable to estimate the effect of contingencies and pertinent control measurement can be taken on to improve the system security. This paper presents the application of particle swarm optimization algorithm to find the optimal location of multi type FACTS devices in a power system in order to eliminate or alleviate the line over loads. The optimizations are performed on the parameters, namely the location of the devices, their types, their settings and installation cost of FACTS devices for single and multiple contingencies. TCSC, SVC and UPFC are considered and modeled for steady state analysis. The selection of UPFC and TCSC suitable location uses the criteria on the basis of improved system security. The effectiveness of the proposed method is tested for IEEE 6 bus and IEEE 30 bus test systems.
Abstract: The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an Advanced Nelder Mead (ANM) algorithm. Nelder Mead (NM) method is a method designed for optimization process. In Nelder Mead method, the vertices of a triangle are considered as the solutions. Many operations are performed on this triangle to obtain a better result. In the proposed work, the operations like reflection and expansion is eliminated and a new operation called spread-out is introduced. The spread-out operation will increase the global search area and thus provides a better result on optimization. The spread-out operation will give three points and the best among these three points will be used to replace the worst point. The experiment results are analyzed with optimization benchmark test functions and gene expression benchmark datasets. The results show that ANM outperforms NM in both benchmarks.
Abstract: Time-Cost Optimization "TCO" is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Since there is a hidden trade-off relationship between project and cost, it might be difficult to predict whether the total cost would increase or decrease as a result of the schedule compression. Recently third dimension in trade-off analysis is taken into consideration that is quality of the projects. Few of the existing algorithms are applied in a case of construction project with threedimensional trade-off analysis, Time-Cost-Quality relationships. The objective of this paper is to presents the development of a practical software system; that named Automatic Multi-objective Typical Construction Resource Optimization System "AMTCROS". This system incorporates the basic concepts of Line Of Balance "LOB" and Critical Path Method "CPM" in a multi-objective Genetic Algorithms "GAs" model. The main objective of this system is to provide a practical support for typical construction planners who need to optimize resource utilization in order to minimize project cost and duration while maximizing its quality simultaneously. The application of these research developments in planning the typical construction projects holds a strong promise to: 1) Increase the efficiency of resource use in typical construction projects; 2) Reduce construction duration period; 3) Minimize construction cost (direct cost plus indirect cost); and 4) Improve the quality of newly construction projects. A general description of the proposed software for the Time-Cost-Quality Trade-Off "TCQTO" is presented. The main inputs and outputs of the proposed software are outlined. The main subroutines and the inference engine of this software are detailed. The complexity analysis of the software is discussed. In addition, the verification, and complexity of the proposed software are proved and tested using a real case study.
Abstract: Optimization of a microwave-assisted extraction of cherry laurel (Prunus laurocerasus) fruit using methanol was studied. The influence of process parameters (microwave power, plant material-to-solvent ratio and the extraction time) on the extraction efficiency were optimized by using response surface methodology. The predicted maximum yield of extractive substances (41.85 g/100 g fresh plant material) was obtained at microwave power of 600 W and plant material to solvent ratio of 0.2 g/cm3 after 26 minutes of extraction, while a mean value of 40.80±0.41 g/100 g fresh plant material was obtained from laboratory experiments. This proves applicability of the model in predicting optimal extraction conditions with minimal laborious and time consuming. The results indicated that all process parameters were effective on the extraction efficiency, while the most important factor was extraction time. In order to rationalize production the optimal economical condition which gave a large total extract yield with minimal energy and solvent consumption was found.
Abstract: In this work, sorption of nickel from aqueous solution on hypnea valentiae, red macro algae, was investigated. Batch experiments have been carried out to find the effect of various parameters such as pH, temperature, sorbent dosage, metal concentration and contact time on the sorption of nickel using hypnea valentiae. Response surface methodology (RSM) is employed to optimize the process parameters. Based on the central composite design, quadratic model was developed to correlate the process variables to the response. The most influential factor on each experimental design response was identified from the analysis of variance (ANOVA). The optimum conditions for the sorption of nickel were found to be: pH – 5.1, temperature – 36.8oC, sorbent dosage – 5.1 g/L, metal concentration – 100 mg/L and contact time – 30 min. At these optimized conditions the maximum removal of nickel was found to be 91.97%. A coefficient of determination R2 value 0.9548 shows the fitness of response surface methodology in this work.
Abstract: Total weighted tardiness is a measure of customer
satisfaction. Minimizing it represents satisfying the general
requirement of on-time delivery. In this research, we consider an ant
colony optimization (ACO) algorithm to solve the problem of
scheduling unrelated parallel machines to minimize total weighted
tardiness. The problem is NP-hard in the strong sense. Computational
results show that the proposed ACO algorithm is giving promising
results compared to other existing algorithms.
Abstract: Nano fibers produced by electrospinning are of industrial and scientific attention due to their special characteristics such as long length, small diameter and high surface area. Applications of electrospun structures in nanotechnology are included tissue scaffolds, fibers for drug delivery, composite reinforcement, chemical sensing, enzyme immobilization, membrane-based filtration, protective clothing, catalysis, solar cells, electronic devices and others. Many polymer and ceramic precursor nano fibers have been successfully electrospun with diameters in the range from 1 nm to several microns. The process is complex so that fiber diameter is influenced by various material, design and operating parameters. The objective of this work is to apply genetic algorithm on the parameters of electrospinning which have the most significant effect on the nano fiber diameter to determine the optimum parameter values before doing experimental set up. Effective factors including initial polymer concentration, initial jet radius, electrical potential, relaxation time, initial elongation, viscosity and distance between nozzle and collector are considered to determine finest diameter which is selected by user.
Abstract: Unmanned Aerial Vehicles (UAVs) have gained tremendous importance, in both Military and Civil, during first decade of this century. In a UAV, onboard computer (autopilot) autonomously controls the flight and navigation of the aircraft. Based on the aircraft role and flight envelope, basic to complex and sophisticated controllers are used to stabilize the aircraft flight parameters. These controllers constitute the autopilot system for UAVs. The autopilot systems, most commonly, provide lateral and longitudinal control through Proportional-Integral-Derivative (PID) controllers or Phase-lead or Lag Compensators. Various techniques are commonly used to ‘tune’ gains of these controllers. Some techniques used are, in-flight step-by-step tuning, software-in-loop or hardware-in-loop tuning methods. Subsequently, numerous in-flight tests are required to actually ‘fine-tune’ these gains. However, an optimization-based tuning of these PID controllers or compensators, as presented in this paper, can greatly minimize the requirement of in-flight ‘tuning’ and substantially reduce the risks and cost involved in flight-testing.
Abstract: Ants are fascinating creatures that demonstrate the
ability to find food and bring it back to their nest. Their ability as a
colony, to find paths to food sources has inspired the development of
algorithms known as Ant Colony Systems (ACS). The principle of
cooperation forms the backbone of such algorithms, commonly used
to find solutions to problems such as the Traveling Salesman
Problem (TSP). Ants communicate to each other through chemical
substances called pheromones. Modeling individual ants- ability to
manipulate this substance can help an ACS find the best solution.
This paper introduces a Dynamic Ant Colony System with threelevel
updates (DACS3) that enhance an existing ACS. Experiments
were conducted to observe single ant behavior in a colony of
Malaysian House Red Ants. Such behavior was incorporated into the
DACS3 algorithm. We benchmark the performance of DACS3 versus
DACS on TSP instances ranging from 14 to 100 cities. The result
shows that the DACS3 algorithm can achieve shorter distance in
most cases and also performs considerably faster than DACS.
Abstract: A statistical optimization of the saccharification
process of EFB was studied. The statistical analysis was done by
applying faced centered central composite design (FCCCD) under
response surface methodology (RSM). In this investigation, EFB
dose, enzyme dose and saccharification period was examined, and the
maximum 53.45% (w/w) yield of reducing sugar was found with 4%
(w/v) of EFB, 10% (v/v) of enzyme after 120 hours of incubation. It
can be calculated that the conversion rate of cellulose content of the
substrate is more than 75% (w/w) which can be considered as a
remarkable achievement. All the variables, linear, quadratic and
interaction coefficient, were found to be highly significant, other than
two coefficients, one quadratic and another interaction coefficient.
The coefficient of determination (R2) is 0.9898 that confirms a
satisfactory data and indicated that approximately 98.98% of the
variability in the dependent variable, saccharification of EFB, could
be explained by this model.
Abstract: This paper presents an algorithm based on the
wavelet decomposition, for feature extraction from the ECG signal
and recognition of three types of Ventricular Arrhythmias using
neural networks. A set of Discrete Wavelet Transform (DWT)
coefficients, which contain the maximum information about the
arrhythmias, is selected from the wavelet decomposition. After that a
novel clustering algorithm based on nature inspired algorithm (Ant
Colony Optimization) is developed for classifying arrhythmia types.
The algorithm is applied on the ECG registrations from the MIT-BIH
arrhythmia and malignant ventricular arrhythmia databases. We
applied Daubechies 4 wavelet in our algorithm. The wavelet
decomposition enabled us to perform the task efficiently and
produced reliable results.
Abstract: The aim of this work is to present a multi-objective optimization method to find maximum efficiency kinematics for a flapping wing unmanned aerial vehicle. We restrained our study to rectangular wings with the same profile along the span and to harmonic dihedral motion. It is assumed that the birdlike aerial vehicle (whose span and surface area were fixed respectively to 1m and 0.15m2) is in horizontal mechanically balanced motion at fixed speed. We used two flight physics models to describe the vehicle aerodynamic performances, namely DeLaurier-s model, which has been used in many studies dealing with flapping wings, and the model proposed by Dae-Kwan et al. Then, a constrained multi-objective optimization of the propulsive efficiency is performed using a recent evolutionary multi-objective algorithm called є-MOEA. Firstly, we show that feasible solutions (i.e. solutions that fulfil the imposed constraints) can be obtained using Dae-Kwan et al.-s model. Secondly, we highlight that a single objective optimization approach (weighted sum method for example) can also give optimal solutions as good as the multi-objective one which nevertheless offers the advantage of directly generating the set of the best trade-offs. Finally, we show that the DeLaurier-s model does not yield feasible solutions.
Abstract: In this paper, we present a matrix game-theoretic cross-layer optimization formulation to maximize the network lifetime in wireless ad hoc networks with network coding. To this end, we introduce a cross-layer formulation of general NUM (network utility maximization) that accommodates routing, scheduling, and stream control from different layers in the coded networks. Specifically, for the scheduling problem and then the objective function involved, we develop a matrix game with the strategy sets of the players corresponding to hyperlinks and transmission modes, and design the payoffs specific to the lifetime. In particular, with the inherit merit that matrix game can be solved with linear programming, our cross-layer programming formulation can benefit from both game-based and NUM-based approaches at the same time by cooperating the programming model for the matrix game with that for the other layers in a consistent framework. Finally, our numerical example demonstrates its performance results on a well-known wireless butterfly network to verify the cross-layer optimization scheme.
Abstract: Particle swarm optimization (PSO) technique is applied to design the water distribution pipeline network. A simulation-optimization model is formulated with the objective of minimizing cost and is applied to a benchmark water distribution system optimization problem. The benchmark problem taken for the application of PSO technique to optimize the pipe size of the water distribution network is New York City water supply system problem. The results from the analysis infer that PSO is a potential alternative optimization technique when compared to other heuristic techniques for optimal sizing of water distribution systems.