Abstract: Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Therefore, this paper provides a model which supports the selection of measures by applying multi-objective analysis to find an optimal solution. Additionally, a list of measures is also provided to make the selection easier and more effective without any leakage of measures.
Abstract: Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.
Abstract: There are a number of different cars for transferring hundreds of close contacts of swine influenza patients to hospital, and we need to carefully assign the passengers to those cars in order to minimize the risk of influenza spreading during transportation. The paper presents an approach to straightforward obtain the optimal solution of the relaxed problems, and develops two iterative improvement algorithms to effectively tackle the general problem.
Abstract: Graph coloring is an important problem in computer
science and many algorithms are known for obtaining reasonably
good solutions in polynomial time. One method of comparing
different algorithms is to test them on a set of standard graphs where
the optimal solution is already known. This investigation analyzes a
set of 50 well known graph coloring instances according to a set of
complexity measures. These instances come from a variety of
sources some representing actual applications of graph coloring
(register allocation) and others (mycieleski and leighton graphs) that
are theoretically designed to be difficult to solve. The size of the
graphs ranged from ranged from a low of 11 variables to a high of
864 variables. The method used to solve the coloring problem was
the square of the adjacency (i.e., correlation) matrix. The results
show that the most difficult graphs to solve were the leighton and the
queen graphs. Complexity measures such as density, mobility,
deviation from uniform color class size and number of block
diagonal zeros are calculated for each graph. The results showed that
the most difficult problems have low mobility (in the range of .2-.5)
and relatively little deviation from uniform color class size.
Abstract: This paper presents the solution of power economic dispatch (PED) problem of generating units with valve point effects and multiple fuel options using Self-Adaptive Differential Evolution (SDE) algorithm. The global optimal solution by mathematical approaches becomes difficult for the realistic PED problem in power systems. The Differential Evolution (DE) algorithm is found to be a powerful evolutionary algorithm for global optimization in many real problems. In this paper the key parameters of control in DE algorithm such as the crossover constant CR and weight applied to random differential F are self-adapted. The PED problem formulation takes into consideration of nonsmooth fuel cost function due to valve point effects and multi fuel options of generator. The proposed approach has been examined and tested with the numerical results of PED problems with thirteen-generation units including valve-point effects, ten-generation units with multiple fuel options neglecting valve-point effects and ten-generation units including valve-point effects and multiple fuel options. The test results are promising and show the effectiveness of proposed approach for solving PED problems.
Abstract: Project managers are the ultimate responsible for the
overall characteristics of a project, i.e. they should deliver the project
on time with minimum cost and with maximum quality. It is vital for
any manager to decide a trade-off between these conflicting
objectives and they will be benefited of any scientific decision
support tool. Our work will try to determine optimal solutions (rather
than a single optimal solution) from which the project manager will
select his desirable choice to run the project. In this paper, the
problem in project scheduling notated as
(1,T|cpm,disc,mu|curve:quality,time,cost) will be studied. The
problem is multi-objective and the purpose is finding the Pareto
optimal front of time, cost and quality of a project
(curve:quality,time,cost), whose activities belong to a start to finish
activity relationship network (cpm) and they can be done in different
possible modes (mu) which are non-continuous or discrete (disc), and
each mode has a different cost, time and quality . The project is
constrained to a non-renewable resource i.e. money (1,T). Because
the problem is NP-Hard, to solve the problem, a meta-heuristic is
developed based on a version of genetic algorithm specially adapted
to solve multi-objective problems namely FastPGA. A sample project
with 30 activities is generated and then solved by the proposed
method.
Abstract: There are two common types of operational research techniques, optimisation and metaheuristic methods. The latter may be defined as a sequential process that intelligently performs the exploration and exploitation adopted by natural intelligence and strong inspiration to form several iterative searches. An aim is to effectively determine near optimal solutions in a solution space. In this work, a type of metaheuristics called Ant Colonies Optimisation, ACO, inspired by a foraging behaviour of ants was adapted to find optimal solutions of eight non-linear continuous mathematical models. Under a consideration of a solution space in a specified region on each model, sub-solutions may contain global or multiple local optimum. Moreover, the algorithm has several common parameters; number of ants, moves, and iterations, which act as the algorithm-s driver. A series of computational experiments for initialising parameters were conducted through methods of Rigid Simplex, RS, and Modified Simplex, MSM. Experimental results were analysed in terms of the best so far solutions, mean and standard deviation. Finally, they stated a recommendation of proper level settings of ACO parameters for all eight functions. These parameter settings can be applied as a guideline for future uses of ACO. This is to promote an ease of use of ACO in real industrial processes. It was found that the results obtained from MSM were pretty similar to those gained from RS. However, if these results with noise standard deviations of 1 and 3 are compared, MSM will reach optimal solutions more efficiently than RS, in terms of speed of convergence.
Abstract: In this contribution, the use of a new genetic operator is proposed. The main advantage of using this operator is that it is able to assist the evolution procedure to converge faster towards the optimal solution of a problem. This new genetic operator is called ''intuition'' operator. Generally speaking, one can claim that this operator is a way to include any heuristic or any other local knowledge, concerning the problem, that cannot be embedded in the fitness function. Simulation results show that the use of this operator increases significantly the performance of the classic Genetic Algorithm by increasing the convergence speed of its population.
Abstract: The Sphere Method is a flexible interior point algorithm for linear programming problems. This was developed mainly by Professor Katta G. Murty. It consists of two steps, the centering step and the descent step. The centering step is the most expensive part of the algorithm. In this centering step we proposed some improvements such as introducing two or more initial feasible solutions as we solve for the more favorable new solution by objective value while working with the rigorous updates of the feasible region along with some ideas integrated in the descent step. An illustration is given confirming the advantage of using the proposed procedure.
Abstract: We investigated statistical performance of Bayesian inference using maximum entropy and MAP estimation for several models which approximated wave-fronts in remote sensing using SAR interferometry. Using Monte Carlo simulation for a set of wave-fronts generated by assumed true prior, we found that the method of maximum entropy realized the optimal performance around the Bayes-optimal conditions by using model of the true prior and the likelihood representing optical measurement due to the interferometer. Also, we found that the MAP estimation regarded as a deterministic limit of maximum entropy almost achieved the same performance as the Bayes-optimal solution for the set of wave-fronts. Then, we clarified that the MAP estimation perfectly carried out phase unwrapping without using prior information, and also that the MAP estimation realized accurate phase unwrapping using conjugate gradient (CG) method, if we assumed the model of the true prior appropriately.
Abstract: Feature selection is an important step in many pattern
classification problems. It is applied to select a subset of features,
from a much larger set, such that the selected subset is sufficient to
perform the classification task. Due to its importance, the problem of
feature selection has been investigated by many researchers. In this
paper, a novel feature subset search procedure that utilizes the Ant
Colony Optimization (ACO) is presented. The ACO is a
metaheuristic inspired by the behavior of real ants in their search for
the shortest paths to food sources. It looks for optimal solutions by
considering both local heuristics and previous knowledge. When
applied to two different classification problems, the proposed
algorithm achieved very promising results.
Abstract: QoS Routing aims to find paths between senders and
receivers satisfying the QoS requirements of the application which
efficiently using the network resources and underlying routing
algorithm to be able to find low-cost paths that satisfy given QoS
constraints. The problem of finding least-cost routing is known to be
NP hard or complete and some algorithms have been proposed to
find a near optimal solution. But these heuristics or algorithms either
impose relationships among the link metrics to reduce the complexity
of the problem which may limit the general applicability of the
heuristic, or are too costly in terms of execution time to be applicable
to large networks. In this paper, we analyzed two algorithms namely
Characterized Delay Constrained Routing (CDCR) and Optimized
Delay Constrained Routing (ODCR). The CDCR algorithm dealt an
approach for delay constrained routing that captures the trade-off
between cost minimization and risk level regarding the delay
constraint. The ODCR which uses an adaptive path weight function
together with an additional constraint imposed on the path cost, to
restrict search space and hence ODCR finds near optimal solution in
much quicker time.
Abstract: Predictions of flow and heat transfer characteristics and shape optimization in internally finned circular tubes have been performed on three-dimensional periodically fully developed turbulent flow and thermal fields. For a trapezoidal fin profile, the effects of fin height h, upper fin widths d1, lower fin widths d2, and helix angle of fin ? on transport phenomena are investigated for the condition of fin number of N = 30. The CFD and mathematical optimization technique are coupled in order to optimize the shape of internally finned tube. The optimal solutions of the design variables (i.e., upper and lower fin widths, fin height and helix angle) are numerically obtained by minimizing the pressure loss and maximizing the heat transfer rate, simultaneously, for the limiting conditions of d1 = 0.5~1.5 mm, d2 = 0.5~1.5 mm, h= 0.5~1.5mm, ? = 10~30 degrees. The fully developed flow and thermal fields are predicted using the finite volume method and the optimization is carried out by means of the multi-objective genetic algorithm that is widely used in the constrained nonlinear optimization problem.
Abstract: The wireless link can be unreliable in realistic wireless
sensor networks (WSNs). Energy efficient and reliable data
forwarding is important because each node has limited resources.
Therefore, we must suggest an optimal solution that considers using
the information of the node-s characteristics. Previous routing
protocols were unsuited to realistic asymmetric WSNs. In this paper,
we propose a Protocol that considers Both sides of Link-quality and
Energy (PBLE), an optimal routing protocol that balances modified
link-quality, distance and energy. Additionally, we propose a node
scheduling method. PBLE achieves a longer lifetime than previous
routing protocols and is more energy-efficient. PBLE uses energy,
local information and both sides of PRR in a 1-hop distance. We
explain how to send data packets to the destination node using the
node's information. Simulation shows PBLE improves delivery rate
and network lifetime compared to previous schemes. Moreover, we
show the improvement in various WSN environments.
Abstract: This paper presents an optimal design of linear phase
digital high pass finite impulse response (FIR) filter using Improved
Particle Swarm Optimization (IPSO). In the design process, the filter
length, pass band and stop band frequencies, feasible pass band and
stop band ripple sizes are specified. FIR filter design is a multi-modal
optimization problem. An iterative method is introduced to find the
optimal solution of FIR filter design problem. Evolutionary
algorithms like real code genetic algorithm (RGA), particle swarm
optimization (PSO), improved particle swarm optimization (IPSO)
have been used in this work for the design of linear phase high pass
FIR filter. IPSO is an improved PSO that proposes a new definition
for the velocity vector and swarm updating and hence the solution
quality is improved. A comparison of simulation results reveals the
optimization efficacy of the algorithm over the prevailing
optimization techniques for the solution of the multimodal, nondifferentiable,
highly non-linear, and constrained FIR filter design
problems.
Abstract: This paper presents a heuristic approach to solve the Generalized Assignment Problem (GAP) which is NP-hard. It is worth mentioning that many researches used to develop algorithms for identifying the redundant constraints and variables in linear programming model. Some of the algorithms are presented using intercept matrix of the constraints to identify redundant constraints and variables prior to the start of the solution process. Here a new heuristic approach based on the dominance property of the intercept matrix to find optimal or near optimal solution of the GAP is proposed. In this heuristic, redundant variables of the GAP are identified by applying the dominance property of the intercept matrix repeatedly. This heuristic approach is tested for 90 benchmark problems of sizes upto 4000, taken from OR-library and the results are compared with optimum solutions. Computational complexity is proved to be O(mn2) of solving GAP using this approach. The performance of our heuristic is compared with the best state-ofthe- art heuristic algorithms with respect to both the quality of the solutions. The encouraging results especially for relatively large size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems.
Abstract: This paper deals with a periodic-review substitutable
inventory system for a finite and an infinite number of periods. Here
an upward substitution structure, a substitution of a more costly item
by a less costly one, is assumed, with two products. At the beginning
of each period, a stochastic demand comes for the first item only,
which is quality-wise better and hence costlier. Whenever an arriving
demand finds zero inventory of this product, a fraction of unsatisfied
customers goes for its substitutable second item. An optimal ordering
policy has been derived for each period. The results are illustrated
with numerical examples. A sensitivity analysis has been done to
examine how sensitive the optimal solution and the maximum profit
are to the values of the discount factor, when there is a large number
of periods.
Abstract: Decisions are regularly made during a project or
daily life. Some decisions are critical and have a direct impact on
project or human success. Formal evaluation is thus required,
especially for crucial decisions, to arrive at the optimal solution
among alternatives to address issues. According to microeconomic
theory, all people-s decisions can be modeled as indifference curves.
The proposed approach supports formal analysis and decision by
constructing indifference curve model from the previous experts-
decision criteria. These knowledge embedded in the system can be
reused or help naïve users select alternative solution of the similar
problem. Moreover, the method is flexible to cope with unlimited
number of factors influencing the decision-making. The preliminary
experimental results of the alternative selection are accurately
matched with the expert-s decisions.
Abstract: An edge based local search algorithm, called ELS, is proposed for the maximum clique problem (MCP), a well-known combinatorial optimization problem. ELS is a two phased local search method effectively £nds the near optimal solutions for the MCP. A parameter ’support’ of vertices de£ned in the ELS greatly reduces the more number of random selections among vertices and also the number of iterations and running times. Computational results on BHOSLIB and DIMACS benchmark graphs indicate that ELS is capable of achieving state-of-the-art-performance for the maximum clique with reasonable average running times.
Abstract: Both the minimum energy consumption and
smoothness, which is quantified as a function of jerk, are generally
needed in many dynamic systems such as the automobile and the
pick-and-place robot manipulator that handles fragile equipments.
Nevertheless, many researchers come up with either solely
concerning on the minimum energy consumption or minimum jerk
trajectory. This research paper proposes a simple yet very interesting
when combining the minimum energy and jerk of indirect jerks
approaches in designing the time-dependent system yielding an
alternative optimal solution. Extremal solutions for the cost functions
of the minimum energy, the minimum jerk and combining them
together are found using the dynamic optimization methods together
with the numerical approximation. This is to allow us to simulate
and compare visually and statistically the time history of state inputs
employed by combining minimum energy and jerk designs. The
numerical solution of minimum direct jerk and energy problem are
exactly the same solution; however, the solutions from problem of
minimum energy yield the similar solution especially in term of
tendency.