Abstract: The backpropagation algorithm in general employs quadratic error function. In fact, most of the problems that involve minimization employ the Quadratic error function. With alternative error functions the performance of the optimization scheme can be improved. The new error functions help in suppressing the ill-effects of the outliers and have shown good performance to noise. In this paper we have tried to evaluate and compare the relative performance of complex valued neural network using different error functions. During first simulation for complex XOR gate it is observed that some error functions like Absolute error, Cauchy error function can replace Quadratic error function. In the second simulation it is observed that for some error functions the performance of the complex valued neural network depends on the architecture of the network whereas with few other error functions convergence speed of the network is independent of architecture of the neural network.
Abstract: In this paper, the optimum weight and cost of a laminated composite plate is seeked, while it undergoes the heaviest load prior to a complete failure. Various failure criteria are defined for such structures in the literature. In this work, the Tsai-Hill theory is used as the failure criterion. The theory of analysis was based on the Classical Lamination Theory (CLT). A newly type of Genetic Algorithm (GA) as an optimization technique with a direct use of real variables was employed. Yet, since the optimization via GAs is a long process, and the major time is consumed through the analysis, Radial Basis Function Neural Networks (RBFNN) was employed in predicting the output from the analysis. Thus, the process of optimization will be carried out through a hybrid neuro-GA environment, and the procedure will be carried out until a predicted optimum solution is achieved.
Abstract: Simulation is a very powerful method used for highperformance
and high-quality design in distributed system, and now
maybe the only one, considering the heterogeneity, complexity and
cost of distributed systems. In Grid environments, foe example, it is
hard and even impossible to perform scheduler performance
evaluation in a repeatable and controllable manner as resources and
users are distributed across multiple organizations with their own
policies. In addition, Grid test-beds are limited and creating an
adequately-sized test-bed is expensive and time consuming.
Scalability, reliability and fault-tolerance become important
requirements for distributed systems in order to support distributed
computation. A distributed system with such characteristics is called
dependable. Large environments, like Cloud, offer unique
advantages, such as low cost, dependability and satisfy QoS for all
users. Resource management in large environments address
performant scheduling algorithm guided by QoS constrains. This
paper presents the performance evaluation of scheduling heuristics
guided by different optimization criteria. The algorithms for
distributed scheduling are analyzed in order to satisfy users
constrains considering in the same time independent capabilities of
resources. This analysis acts like a profiling step for algorithm
calibration. The performance evaluation is based on simulation. The
simulator is MONARC, a powerful tool for large scale distributed
systems simulation. The novelty of this paper consists in synthetic
analysis results that offer guidelines for scheduler service
configuration and sustain the empirical-based decision. The results
could be used in decisions regarding optimizations to existing Grid
DAG Scheduling and for selecting the proper algorithm for DAG
scheduling in various actual situations.
Abstract: This paper presents a Reliability-Based Topology
Optimization (RBTO) based on Evolutionary Structural Optimization
(ESO). An actual design involves uncertain conditions such as
material property, operational load and dimensional variation.
Deterministic Topology Optimization (DTO) is obtained without
considering of the uncertainties related to the uncertainty parameters.
However, RBTO involves evaluation of probabilistic constraints,
which can be done in two different ways, the reliability index
approach (RIA) and the performance measure approach (PMA). Limit
state function is approximated using Monte Carlo Simulation and
Central Composite Design for reliability analysis. ESO, one of the
topology optimization techniques, is adopted for topology
optimization. Numerical examples are presented to compare the DTO
with RBTO.
Abstract: DG application has received increasing attention during
recent years. The impact of DG on various aspects of distribution system
operation, such as reliability and energy loss, depend highly on DG
location in distribution feeder. Optimal DG placement is an important
subject which has not been fully discussed yet.
This paper presents an optimization method to determine optimal DG
placement, based on a cost/worth analysis approach. This method
considers technical and economical factors such as energy loss, load point
reliability indices and DG costs, and particularly, portability of DG. The
proposed method is applied to a test system and the impacts of different
parameters such as load growth rate and load forecast uncertainty (LFU)
on optimum DG location are studied.
Abstract: This paper proposes a delay-dependent leader-following consensus condition of multi-agent systems with both communication delay and probabilistic self-delay. The proposed methods employ a suitable piecewise Lyapunov-Krasovskii functional and the average dwell time approach. New consensus criterion for the systems are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Numerical example showed that the proposed method is effective.
Abstract: In this paper, a simulated annealing algorithm has been developed to optimize machining parameters in turning operation on cylindrical workpieces. The turning operation usually includes several passes of rough machining and a final pass of finishing. Seven different constraints are considered in a non-linear model where the goal is to achieve minimum total cost. The weighted total cost consists of machining cost, tool cost and tool replacement cost. The computational results clearly show that the proposed optimization procedure has considerably improved total operation cost by optimally determining machining parameters.
Abstract: Shape optimization of the airfoil with high aspect ratio
of long endurance unmanned aerial vehicle (UAV) is performed by the
multi-objective optimization technology coupled with computational
fluid dynamics (CFD). For predicting the aerodynamic characteristics
around the airfoil the high-fidelity Navier-Stokes solver is employed
and SMOGA (Simple Multi-Objective Genetic Algorithm), which is
developed by authors, is used for solving the multi-objective
optimization problem. To obtain the optimal solutions of the design
variable (i.e., sectional airfoil profile, wing taper ratio and sweep) for
high performance of UAVs, both the lift and lift-to-drag ratio are
maximized whereas the pitching moment should be minimized,
simultaneously. It is found that the lift force and lift-to-drag ratio are
linearly dependent and a unique and dominant solution are existed.
However, a trade-off phenomenon is observed between the lift-to-drag
ratio and pitching moment. As the result of optimization, sixty-five
(65) non-dominated Pareto individuals at the cutting edge of design
spaces that is decided by airfoil shapes can be obtained.
Abstract: This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.
Abstract: In this paper, a novel method using Bees Algorithm is proposed to determine the optimal allocation of FACTS devices for maximizing the Available Transfer Capability (ATC) of power transactions between source and sink areas in the deregulated power system. The algorithm simultaneously searches the FACTS location, FACTS parameters and FACTS types. Two types of FACTS are simulated in this study namely Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC). A Repeated Power Flow with FACTS devices including ATC is used to evaluate the feasible ATC value within real and reactive power generation limits, line thermal limits, voltage limits and FACTS operation limits. An IEEE30 bus system is used to demonstrate the effectiveness of the algorithm as an optimization tool to enhance ATC. A Genetic Algorithm technique is used for validation purposes. The results clearly indicate that the introduction of FACTS devices in a right combination of location and parameters could enhance ATC and Bees Algorithm can be efficiently used for this kind of nonlinear integer optimization.
Abstract: Routing in MANET is extremely challenging because
of MANETs dynamic features, its limited bandwidth, frequent
topology changes caused by node mobility and power energy
consumption. In order to efficiently transmit data to destinations, the
applicable routing algorithms must be implemented in mobile ad-hoc
networks. Thus we can increase the efficiency of the routing by
satisfying the Quality of Service (QoS) parameters by developing
routing algorithms for MANETs. The algorithms that are inspired by
the principles of natural biological evolution and distributed
collective behavior of social colonies have shown excellence in
dealing with complex optimization problems and are becoming more
popular. This paper presents a survey on few meta-heuristic
algorithms and naturally-inspired algorithms.
Abstract: A highly optimized implementation of binary mixture
diffusion with no initial bulk velocity on graphics processors is
presented. The lattice Boltzmann model is employed for simulating
the binary diffusion of oxygen and nitrogen into each other with
different initial concentration distributions. Simulations have been
performed using the latest proposed lattice Boltzmann model that
satisfies both the indifferentiability principle and the H-theorem for
multi-component gas mixtures. Contemporary numerical
optimization techniques such as memory alignment and increasing
the multiprocessor occupancy are exploited along with some novel
optimization strategies to enhance the computational performance on
graphics processors using the C for CUDA programming language.
Speedup of more than two orders of magnitude over single-core
processors is achieved on a variety of Graphical Processing Unit
(GPU) devices ranging from conventional graphics cards to
advanced, high-end GPUs, while the numerical results are in
excellent agreement with the available analytical and numerical data
in the literature.
Abstract: In this paper, multi-processors job shop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. By using the suitable hybrid of priority dispatching rules, the process of finding the best solution will be improved. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. Thus, the similar work has the same production lines. Other advantage of this algorithm is that the similar machines (not the same) can be considered. So, these machines are able to process a job with different processing and setup times. According to this capability and from this algorithm evaluation point of view, a number of test problems are solved and the associated results are analyzed. The results show a significant decrease in throughput time. It also shows that, this algorithm is able to recognize the bottleneck machine and to schedule jobs in an efficient way.
Abstract: Economic Load Dispatch (ELD) is a method of determining
the most efficient, low-cost and reliable operation of a power
system by dispatching available electricity generation resources to
supply load on the system. The primary objective of economic
dispatch is to minimize total cost of generation while honoring
operational constraints of available generation resources. In this paper
an intelligent water drop (IWD) algorithm has been proposed to
solve ELD problem with an objective of minimizing the total cost of
generation. Intelligent water drop algorithm is a swarm-based natureinspired
optimization algorithm, which has been inspired from natural
rivers. A natural river often finds good paths among lots of possible
paths in its ways from source to destination and finally find almost
optimal path to their destination. These ideas are embedded into
the proposed algorithm for solving economic load dispatch problem.
The main advantage of the proposed technique is easy is implement
and capable of finding feasible near global optimal solution with
less computational effort. In order to illustrate the effectiveness of
the proposed method, it has been tested on 6-unit and 20-unit test
systems with incremental fuel cost functions taking into account the
valve point-point loading effects. Numerical results shows that the
proposed method has good convergence property and better in quality
of solution than other algorithms reported in recent literature.
Abstract: In this paper an ant colony optimization algorithm is
developed to solve the permutation flow shop scheduling problem. In
the permutation flow shop scheduling problem which has been vastly
studied in the literature, there are a set of m machines and a set of n
jobs. All the jobs are processed on all the machines and the sequence
of jobs being processed is the same on all the machines. Here this
problem is optimized considering two criteria, makespan and total
flow time. Then the results are compared with the ones obtained by
previously developed algorithms. Finally it is visible that our
proposed approach performs best among all other algorithms in the
literature.
Abstract: The contact width is important design parameter for
optimizing the design of new metal gasket for asbestos substitution
gasket. The contact width is found have relationship with the helium
leak quantity. In the increasing of axial load value, the helium leak
quantity is decreasing and the contact width is increasing. This study
provides validity method using simulation analysis and the result is
compared to experimental using pressure sensitive paper. The results
denote similar trend data between simulation and experimental result.
Final evaluation is determined by helium leak quantity to check
leakage performance of gasket design. Considering the phenomena of
position change on the convex contact, it can be developed the
optimization of gasket design by increasing contact width.
Abstract: This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.
Abstract: Stock portfolio selection is a classic problem in finance,
and it involves deciding how to allocate an institution-s or an individual-s
wealth to a number of stocks, with certain investment objectives
(return and risk). In this paper, we adopt the classical Markowitz
mean-variance model and consider an additional common realistic
constraint, namely, the cardinality constraint. Thus, stock portfolio
optimization becomes a mixed-integer quadratic programming problem
and it is difficult to be solved by exact optimization algorithms.
Chemical Reaction Optimization (CRO), which mimics the molecular
interactions in a chemical reaction process, is a population-based
metaheuristic method. Two different types of CRO, named canonical
CRO and Super Molecule-based CRO (S-CRO), are proposed to solve
the stock portfolio selection problem. We test both canonical CRO
and S-CRO on a benchmark and compare their performance under
two criteria: Markowitz efficient frontier (Pareto frontier) and Sharpe
ratio. Computational experiments suggest that S-CRO is promising
in handling the stock portfolio optimization problem.
Abstract: This paper objects to extend Jon Kleinberg-s research. He introduced the structure of small-world in a grid and shows with a greedy algorithm using only local information able to find route between source and target in delivery time O(log2n). His fundamental model for distributed system uses a two-dimensional grid with longrange random links added between any two node u and v with a probability proportional to distance d(u,v)-2. We propose with an additional information of the long link nearby, we can find the shorter path. We apply the ant colony system as a messenger distributed their pheromone, the long-link details, in surrounding area. The subsequence forwarding decision has more option to move to, select among local neighbors or send to node has long link closer to its target. Our experiment results sustain our approach, the average routing time by Color Pheromone faster than greedy method.
Abstract: In this paper the optimality of the solution of an
existing real word assignment problem known as the seat assignment
problem using Seat Assignment Method (SAM) is discussed. SAM is
the newly driven method from three existing methods, Hungarian
Method, Northwest Corner Method and Least Cost Method in a
special way that produces the easiness & fairness among all methods
that solve the seat assignment problem.