-*- mode: org -*-
Focus on optimization or DM methodologies instead of learning per se.

* 117(<- 73): A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization
method, proposed originally by Kennedy and Eberhart in 1995. It is now
one of the most commonly used optimization techniques. This survey
presented a comprehensive investigation of PSO. On one hand, we
provided advances with PSO, including its modifications (including
quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO),
population topology (as fully connected, von Neumann, ring, star,
random, etc.), hybridization (with genetic algorithm, simulated
annealing, Tabu search, artificial immune system, ant colony
algorithm, artificial bee colony, differential evolution, harmonic
search, and biogeography-based optimization), extensions (to
multiobjective, constrained, discrete, and binary optimization),
theoretical analysis (parameter selection and tuning, and convergence
analysis), and parallel implementation (in multicore, multiprocessor,
GPU, and cloud computing forms). On the other hand, we offered a
survey on applications of PSO to the following eight fields:
electrical and electronic engineering, automation control systems,
communication theory, operations research, mechanical engineering,
fuel and energy, medicine, chemistry, and biology. It is hoped that
this survey would be beneficial for the researchers studying PSO
algorithms.

2015




* 133(<-135): Nonadditive similarity-based single-layer perceptron for multi-criteria collaborative filtering

The main aim of the popular collaborative filtering approaches for
recommender systems is to recommend items that users with similar
preferences have liked in the past. Although single-criterion
recommender systems have been successfully used in several
applications, multi-criteria rating systems that allow users to
specify ratings for various content attributes for individual items
are gaining in importance. To measure the overall similarity between
any two users for multi-criteria collaborative filtering, the
indifference relation in outranking relation theory, which can justify
discrimination between any two patterns, is suitable for
multi-criteria decision making (MCDM). However, nonadditive
indifference indices that address interactions among criteria should
be taken into account. This paper proposes a novel similarity-based
perceptron using nonadditive indifference indices to estimate an
overall rating that a user would give to a specific item. The
applicability of the proposed model to recommendation of initiators on
a group-buying website was examined. Experimental results demonstrate
that the proposed model performs well in terms of generalization
ability compared to other multi-criteria collaborative filtering
approaches. (C) 2013 Elsevier B.V. All rights reserved.

2014





* 141(<-398): Brain-Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker

The centrality of the decision maker (DM) is widely recognized in the
multiple criteria decision-making community. This translates into
emphasis on seamless human-computer interaction, and adaptation of the
solution technique to the knowledge which is progressively acquired
from the DM. This paper adopts the methodology of reactive search
optimization (RSO) for evolutionary interactive multiobjective
optimization. RSO follows to the paradigm of "learning while
optimizing," through the use of online machine learning techniques as
an integral part of a self-tuning optimization scheme. User judgments
of couples of solutions are used to build robust incremental models of
the user utility function, with the objective to reduce the cognitive
burden required from the DM to identify a satisficing solution. The
technique of support vector ranking is used together with a k-fold
cross-validation procedure to select the best kernel for the problem
at hand, during the utility function training procedure. Experimental
results are presented for a series of benchmark problems.

2010





* 144(<-  7): Multiple criteria decision aiding for finance: An updated bibliographic survey

Finance is a popular field for applied and methodological research
involving multiple criteria decision aiding (MCDA) techniques. In this
study we present an up-to-date bibliographic survey of the
contributions of MCDA in financial decision making, focusing on the
developments during the past decade. The survey covers all main areas
of financial modeling as well as the different methodological
approaches in MCDA and its connections with other analytical
fields. On the basis of the survey results, we discuss the
contributions of MCDA in different areas of financial decision making
and identify established and emerging research topics, as well as
future opportunities and challenges. (C) 2015 Elsevier B.V. and
Association of European Operational Research Societies (EURO) within
the International Federation of Operational Research Societies
(IFORS). All rights reserved.

2015





* 146(<-365): Preference disaggregation and statistical learning for multicriteria decision support: A review

Disaggregation methods have become popular in multicriteria decision
aiding (MCDA) for eliciting preferential information and constructing
decision models from decision examples. From a statistical point of
view, data mining and machine learning are also involved with similar
problems, mainly with regard to identifying patterns and extracting
knowledge from data. Recent research has also focused on the
introduction of specific domain knowledge in machine learning
algorithms. Thus, the connections between disaggregation methods in
MCDA and traditional machine learning tools are becoming stronger. In
this paper the relationships between the two fields are explored. The
differences and similarities between the two approaches are
identified, and a review is given regarding the integration of the two
fields. (C) 2010 Elsevier B.V. All rights reserved.

2011





* 149(<-478): A memetic model of evolutionary PSO for computational finance applications

Motivated by the compensatory property of EA and PSO, where the latter
can enhance solutions generated from the evolutionary operations by
exploiting their individual memory and social knowledge of the swarm,
this paper examines the implementation of PSO as a local optimizer for
fine tuning in evolutionary search. The proposed approach is evaluated
on applications from the field of computational finance, namely
portfolio optimization and time series forecasting. Exploiting the
structural similarity between these two problems and the non-linear
fractional knapsack problem. an instance of the latter is generalized
and implemented as the preliminary test platform for the proposed
EA-PSO hybrid model. The experimental results demonstrate the positive
effects of this memetic synergy and reveal general design guidelines
for the implementation of PSO as a local optimizer. Algorithmic
performance improvements are similarly evident when extending to the
real-world optimization problems under the appropriate integration of
PSO with EA. (C) 2008 Elsevier Ltd. All rights reserved.

2009





* 156(<-206): Genetic Algorithms, a Nature-Inspired Tool: A Survey of Applications in Materials Science and Related Fields: Part II

Genetic algorithms (GAs) are a helpful tool in optimization,
simulation, modelling, design, and prediction purposes in various
domains of science including materials science, medicine, technology,
economy, industry, environment protection, etc. Reported uses of GAs
led to solving of numerous complex computational tasks. In materials
science and related fields of science and technology, GAs are
routinely used for materials modeling and design, for optimization of
material properties, the method is also useful in organizing the
material or device production at the industrial scale. Here, the most
recent (years 2008-2012) applications of GAs in materials science and
in related fields (solid state physics and chemistry, crystallography,
production, and engineering) are reviewed. The representative examples
selected from recent literature show how broad is the usefulness of
this computational method.

2013





* 161(<-625): Developing sorting models using preference disaggregation analysis: An experimental investigation

Within the field of multicriteria decision aid, sorting refers to the
assignment of a set of alternatives into predefined homogenous groups
defined in an ordinal way. The real-world applications of this type of
problem extend to a wide range of decision-making fields. Preference
disaggregation analysis provides the framework for developing sorting
models through the analysis of the global judgment of the
decision-maker using mathematical programming techniques. However, the
automatic elicitation of preferential information through the
preference disaggregation analysis raises several issues regarding the
impact of the parameters involved in the model development process on
the performance and the stability of the developed models. The
objective of this paper is to shed light on this issue. For this
purpose the UTADIS preference disaggregation sorting method (UTilites
Additives DIScriminantes) is considered. The conducted analysis is
based on an extensive Monte Carlo simulation and useful findings are
obtained on the aforementioned issues. (C) 2003 Elsevier B.V. All
rights reserved.

2004






* 163(<- 88): Pareto Front Estimation for Decision Making

The set of available multi-objective optimisation algorithms continues
to grow. This fact can be partially attributed to their widespread use
and applicability. However, this increase also suggests several issues
remain to be addressed satisfactorily. One such issue is the diversity
and the number of solutions available to the decision maker (DM). Even
for algorithms very well suited for a particular problem, it is
difficult-mainly due to the computational cost-to use a population
large enough to ensure the likelihood of obtaining a solution close to
the DM's preferences. In this paper we present a novel methodology
that produces additional Pareto optimal solutions from a Pareto
optimal set obtained at the end run of any multi-objective
optimisation algorithm for two-objective and three-objective problem
instances.

2014





* 164(<-306): Memetic algorithms and memetic computing optimization: A literature review

Memetic computing is a subject in computer science which considers
complex structures such as the combination of simple agents and memes,
whose evolutionary interactions lead to intelligent complexes capable
of problem-solving. The founding cornerstone of this subject has been
the concept of memetic algorithms, that is a class of optimization
algorithms whose structure is characterized by an evolutionary
framework and a list of local search components. This article presents
a broad literature review on this subject focused on optimization
problems. Several classes of optimization problems, such as discrete,
continuous, constrained, multi-objective and characterized by
uncertainties, are addressed by indicating the memetic "recipes"
proposed in the literature. In addition, this article focuses on
implementation aspects and especially the coordination of memes which
is the most important and characterizing aspect of a memetic
structure. Finally, some considerations about future trends in the
subject are given. (C) 2011 Elsevier B.V. All rights reserved.

2012





* 167(<-126): Parameter identification and calibration of the Xin'anjiang model using the surrogate modeling approach

Practical experience has demonstrated that single objective functions,
no matter how carefully chosen, prove to be inadequate in providing
proper measurements for all of the characteristics of the observed
data. One strategy to circumvent this problem is to define multiple
fitting criteria that measure different aspects of system behavior,
and to use multi-criteria optimization to identify non-dominated
optimal solutions. Unfortunately, these analyses require running
original simulation models thousands of times. As such, they demand
prohibitively large computational budgets. As a result, surrogate
models have been used in combination with a variety of multi-objective
optimization algorithms to approximate the true Pareto-front within
limited evaluations for the original model. In this study,
multi-objective optimization based on surrogate modeling (multivariate
adaptive regression splines, MARS) for a conceptual rainfall-runoff
model (Xin'anjiang model, XAJ) was proposed. Taking the Yanduhe basin
of Three Gorges in the upper stream of the Yangtze River in China as a
case study, three evaluation criteria were selected to quantify the
goodness-of-fit of observations against calculated values from the
simulation model. The three criteria chosen were the Nash-Sutcliffe
efficiency coefficient, the relative error of peak flow, and runoff
volume (REPF and RERV). The efficacy of this method is demonstrated on
the calibration of the XAJ model. Compared to the single objective
optimization results, it was indicated that the multi-objective
optimization method can infer the most probable parameter set. The
results also demonstrate that the use of surrogate-modeling enables
optimization that is much more efficient; and the total computational
cost is reduced by about 92.5%, compared to optimization without using
surrogate modeling. The results obtained with the proposed method
support the feasibility of applying parameter optimization to
computationally intensive simulation models, via reducing the number
of simulation runs required in the numerical model considerably.

2014




* 169(<-268): Multiresponse Metamodeling in Simulation-Based Design Applications

The optimal design of complex systems in engineering requires the
availability of mathematical models of system's behavior as a function
of a set of design variables; such models allow the designer to search
for the best solution to the design problem. However, system models
(e.g., computational fluid dynamics (CFD) analysis, physical
prototypes) are usually time-consuming and expensive to evaluate, and
thus unsuited for systematic use during design. Approximate models of
system behavior based on limited data, also known as metamodels, allow
significant savings by reducing the resources devoted to modeling
during the design process. In this work in engineering design based on
multiple performance criteria, we propose the use of multi-response
Bayesian surrogate models (MR-BSM) to model several aspects of system
behavior jointly, instead of modeling each individually. To this end,
we formulated a family of multiresponse correlation functions,
suitable for prediction of several response variables that are
observed simultaneously from the same computer simulation. Using a set
of test functions with varying degrees of correlation, we compared the
performance of MR-BSM against metamodels built individually for each
response. Our results indicate that MR-BSM outperforms individual
metamodels in 53% to 75% of the test cases, though the relative
performance depends on the sample size, sampling scheme and the actual
correlation among the observed response values. In addition, the
relative performance of MR-BSM versus individual metamodels was
contingent upon the ability to select an appropriate
covariance/correlation function for each application, a task for which
a modified version of Akaike's Information Criterion was observed to
be inadequate. [DOI: 10.1115/1.4006996]

2012




* 170(<-428): Multiobjective global surrogate modeling, dealing with the 5-percent problem

When dealing with computationally expensive simulation codes or
process measurement data, surrogate modeling methods are firmly
established as facilitators for design space exploration, sensitivity
analysis, visualization, prototyping and optimization. Typically the
model parameter (=hyperparameter) optimization problem as part of
global surrogate modeling is formulated in a single objective
way. Models are generated according to a single objective
(accuracy). However, this requires an engineer to determine a single
accuracy target and measure upfront, which is hard to do if the
behavior of the response is unknown. Likewise, the different outputs
of a multi-output system are typically modeled separately by
independent models. Again, a multiobjective approach would benefit the
domain expert by giving information about output correlation and
enabling automatic model type selection for each output
dynamically. With this paper the authors attempt to increase awareness
of the subtleties involved and discuss a number of solutions and
applications. In particular, we present a multiobjective framework for
global surrogate model generation to help tackle both problems and
that is applicable in both the static and sequential design (adaptive
sampling) case.

2010





* 218(<-472): Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

This paper presents a novel multi-objective genetic algorithm (MOGA)
based on the NSGA-II algorithm, which uses metamodels to determine
optimal sampling locations for installing pressure loggers in a water
distribution system (WDS) when parameter uncertainty is
considered. The new algorithm combines the multi-objective genetic
algorithm with adaptive neural networks (MOGA-ANN) to locate pressure
loggers. The purpose of pressure logger installation is to collect
data for hydraulic model calibration. Sampling design is formulated as
a two-objective optimization problem in this study. The objectives are
to maximize the calibrated model accuracy and to minimize the number
of sampling devices as a surrogate of sampling design cost. Calibrated
model accuracy is defined as the average of normalized traces of model
prediction covariance matrices, each of which is constructed from a
randomly generated sampling set of calibration parameter values. This
method of calculating model accuracy is called the 'full' fitness
model. Within the genetic algorithm search process, the full fitness
model is progressively replaced with the periodically (re)trained
adaptive neural network metamodel where (re)training is done using the
data collected by calling the full model. The methodology was first
tested on a hypothetical (benchmark) problem to configure the setting
requirement. Then the model was applied to a real case study. The
results show that significant computational savings can be achieved by
using the MOGA-ANN when compared to the approach where MOGA is linked
to the full fitness model. When applied to the real case study,
optimal solutions identified by MOGA-ANN are obtained 25 times faster
than those identified by the full model without significant decrease
in the accuracy of the final solution. (C) 2008 Elsevier Ltd. All
rights reserved.

2009





* 257(<-125): Robust parameter design optimization using Kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques

The dual response surface methodology is one of the most commonly used
approaches in robust parameter design to simultaneously optimize the
mean value and keep the variance minimum. The commonly used meta-model
is the quadratic polynomial regression. For highly nonlinear
input/output relationship, the accuracy of the fitted model is
limited. Many researchers recommended to use more complicated
surrogate models. In this study, three surrogate models will replace
the second order polynomial regression, namely, ordinary Kriging,
radial basis function approximation (RBF) and radial basis function
artificial neural network (RBFNN). The results show that the three
surrogate model present superior accuracy in comparison with the
quadratic polynomial regression. The mean squared error (MSE) approach
is widely used to link the mean and variance in one cost function. In
this study, a new approach has been proposed using multi-objective
optimization. The new approach has two main advantages over the
classical method. First, the conflicting nature of the two objectives
can be efficiently handled. Second, the decision maker will have a set
of Pareto-front design points to select from. (C) 2014 Elsevier
Inc. All rights reserved.

2014





* 293(<-170): MULTI-OBJECTIVE OPTIMIZATION BY MEANS OF MULTI-DIMENSIONAL MLP NEURAL NETWORKS

In this paper, a multi-layer perceptron (MLP) neural network (NN) is
put forward as an efficient tool for performing two tasks: 1)
optimization of multi-objective problems and 2) solving a non-linear
system of equations. In both cases, mathematical functions which are
continuous and partially bounded are involved. Previously, these two
tasks were performed by recurrent neural networks and also strong
algorithms like evolutionary ones. In this study, multi-dimensional
structure in the output layer of the MLP-NN, as an innovative method,
is utilized to implicitly optimize the multivariate functions under
the network energy optimization mechanism. To this end, the activation
functions in the output layer are replaced with the multivariate
functions intended to be optimized. The effective training parameters
in the global search are surveyed. Also, it is demonstrated that the
MLP-NN with proper dynamic learning rate is able to find globally
optimal solutions. Finally, the efficiency of the MLP-NN in both
aspects of speed and power is investigated by some well-known
experimental examples. In some of these examples, the proposed method
gives explicitly better globally optimal solutions compared to that of
the other references and also shows completely satisfactory results in
other experiments.

2014







* 301(<-536): Learning multicriteria fuzzy classification method PROAFTN from data

In this paper, we present a new methodology for learning parameters of
multiple criteria classification method PROAFTN from data. There are
numerous representations and techniques available for data mining, for
example decision trees, rule bases, artificial neural networks,
density estimation, regression and clustering. The PROAFTN method
constitutes another approach for data mining. It belongs to the class
of supervised learning algorithms and assigns membership degree of the
alternatives to the classes. The PROAFTN method requires the
elicitation of its parameters for the purpose of
classification. Therefore, we need an automatic method that helps us
to establish these parameters from the given data with minimum
classification errors. Here, we propose variable neighborhood search
metaheuristic for getting these parameters. The performances of the
newly proposed method were evaluated using 10 cross validation
technique. The results are compared with those obtained by other
classification methods previously reported on the same data. It
appears that the solutions of substantially better quality are
obtained with proposed method than with these former ones. Crown
Copyright (c) 2005 Published by Elsevier Ltd. All rights reserved.

2007





* 315(<-685): ARTIFICIAL NEURAL NETWORKS VERSUS NATURAL NEURAL NETWORKS - A CONNECTIONIST PARADIGM FOR PREFERENCE ASSESSMENT

Preference is an essential ingredient in all decision processes. This
paper presents a new connectionist paradigm for preference assessment
in a general multicriteria decision setting. A general structure of an
artificial neural network for representing two specified prototypes of
preference structures is discussed. An interactive preference
assessment procedure and an autonomous learning algorithm based on a
novel scheme of supervised learning are proposed. Operating
characteristics of the proposed paradigm are also illustrated through
detailed results of numerical simulations.

1994





* 332(<-597): Intelligent interactive multiobjective optimization method and its application to reliability optimization

In most practical situations involving reliability optimization, there
are several mutually conflicting goals such as maximizing the system
reliability and minimizing the cost, weight and volume. This paper
develops an effective multiobjective optimization method, the
Intelligent Interactive Multiobjective Optimization Method (IIMOM). In
IIMOM, the general concept of the model parameter vector is
proposed. From a practical point of view, a designer's preference
structure model is built using Artificial Neural Networks (ANNs) with
the model parameter vector as the input and the preference information
articulated by the designer over representative samples from the
Pareto frontier as the desired output. Then with the ANN model of the
designer's preference structure as the objective, an optimization
problem is solved to search for improved solutions and guide the
interactive optimization process intelligently. IIMOM is applied to
the reliability optimization problem of a multi-stage mixed system
with five different value functions simulating the designer in the
solution evaluation process. The results illustrate that IIMOM is
effective in capturing different kinds of preference structures of the
designer, and it provides a complete and effective solution for
medium- and small-scale multiobjective optimization problems.

2005






* 336(<-644): Simulation metamodeling through artificial neural networks

Simulation metamodeling has been a major research field during the
last decade. The main objective has been to provide robust, fast
decision support aids to enhance the overall effectiveness of
decision-making processes. This paper discusses the importance of
simulation metamodeling through artificial neural networks (ANNs), and
provides general guidelines for the development of ANN-based
simulation metamodels. Such guidelines were successfully applied in
the development of two ANNs trained to estimate the manufacturing lead
times (MLT) for orders simultaneously processed in a four-machine job
shop. The design of intelligent systems such as ANNs may help to avoid
some of the drawbacks of traditional computer simulation. Metamodels
offer significant advantages regarding time consumption and simplicity
to evaluate multi-criteria situations. Their operation is notoriously
fast compared to the time required to operate conventional simulation
packages. (C) 2003 Elsevier Ltd. All rights reserved.

2003





* 374(<-621): A neural network approach to multiobjective and multilevel programming problems

This study aims at utilizing the dynamic behavior of artificial neural
networks (ANNs) to solve multiobjective programming (MOP) and
multilevel programming (MLP) problems. The traditional and
nontraditional approaches to the MLP are first classified into five
categories. Then, based on the approach proposed by Hopfield and
Tank [1], the optimization problem is converted into a system of
nonlinear differential equations through the use of an energy function
and Lagrange multipliers. Finally, the procedure is extended to MOP
and MLP problems. To solve the resulting differential equations, a
steepest descent search technique is used. This proposed
nontraditional algorithm is efficient for solving complex problems,
and is especially useful for implementation on a large-scale VLSI, in
which the MOP and MLP problems can be solved on a real time basis. To
illustrate the approach, several numerical examples are solved and
compared. (C) 2004 Elsevier Ltd. All rights reserved.

2004






* 377(<-513): Soft computing in engineering design - A review

The present paper surveys the application of soft computing (SC)
techniques in engineering design. Within this context, fuzzy logic
(FL), genetic algorithms (GA) and artificial neural networks (ANN), as
well as their fusion are reviewed in order to examine the capability
of soft computing methods and techniques to effectively address
various hard-to-solve design tasks and issues. Both these tasks and
issues are studied in the first part of the paper accompanied by
references to some results extracted from a survey performed for in
some industrial enterprises. The second part of the paper makes an
extensive review of the literature regarding the application of soft
computing (SC) techniques in engineering design. Although this review
cannot be collectively exhaustive, it may be considered as a valuable
guide for researchers who are interested in the domain of engineering
design and wish to explore the opportunities offered by fuzzy logic,
artificial neural networks and genetic algorithms for further
improvement of both the design outcome and the design process
itself. An arithmetic method is used in order to evaluate the review
results, to locate the research areas where SC has already given
considerable results and to reveal new research opportunities. (C)
2007 Elsevier Ltd. All rights reserved.

2008







* 391(<-579): Nonessential objectives within network approaches for MCDM

In Gal and Hanne [Eur. J. Oper. Res. 119 (1999) 373] the problem of
using several methods to solve a multiple criteria decision making
(MCDM) problem with linear objective functions after dropping
nonessential objectives is analyzed. It turned out that the solution
does not need be the same when using various methods for solving the
system containing the nonessential objectives or not. In this paper we
consider the application of network approaches for multicriteria
decision making such as neural networks and an approach for combining
MCDM methods (called MCDM networks). We discuss questions of comparing
the results obtained with several methods as applied to the problem
with or without nonessential objectives. Especially, we argue for
considering redundancies such as nonessential objectives as a native
feature in complex information processing. In contrast to previous
results on nonessential objectives, the current paper focuses on
discrete MCDM problems which are also denoted as multiple attribute
decision making (MADM). (c) 2004 Elsevier B.V. All rights reserved.

2006





* 392(<-666): Clustering and selection of multiple criteria alternatives using unsupervised and supervised neural networks

There are decision-making problems that involve grouping and selecting
a set of alternatives. Traditional decision-making approaches treat
different sets of alternatives with the same method of analysis and
selection. In this paper, we propose clustering alternatives into
different sets so that different methods of analysis, selection, and
implementation for each set can be applied. We consider multiple
criteria decision-making alternatives where the decision-maker is
faced with several conflicting and non-commensurate objectives (or
criteria). For example, consider buying a set of computers for a
company that vary in terms of their functions, prices, and computing
powers. In this paper, we develop theories and procedures for
clustering and selecting discrete multiple criteria alternatives. The
sets of alternatives clustered are mutually exclusive and are based on
(1) similar features among alternatives, and (2) preferential
structure of the decision-maker. The decision-making process can be
broken down into three steps: (1) generating alternatives; (2)
grouping or clustering alternatives based on similarity of their
features; and (3) choosing one or more alternatives from each cluster
of alternatives. We utilize unsupervised learning clustering
artificial neural networks (ANN) with variable weights for clustering
of alternatives, and we use feedforward ANN for the selection of the
best alternatives for each cluster of alternatives. The decision-maker
is interactively involved by comparing and contrasting alternatives
within each group so that the best alternative can be selected from
each group. For the learning mechanism of ANN, we proposed using a
generalized Euclidean distance where by changing its coefficients new
formation of clusters of alternatives can be achieved. The algorithm
is interactive and the results are independent of the initial set-up
information. Some examples and computational results are presented.

2000





* 438(<-226): Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Optimization algorithms are important tools for the solution of
combinatorial management problems. Nowadays, many of those problems
are addressed by using evolutionary algorithms (EAs) that move toward
a near-optimal solution by repetitive simulations. Sometimes, such
extensive simulations are not possible or are costly and
time-consuming. Thus, in this study a method based on artificial
neural networks (ANN) is proposed to reduce the number of simulations
required in EAs. Specifically, an ANN simulator is used to reduce the
number of simulations by the main simulator. The ANN is trained and
updated only for required areas in the decision space. Performance of
the proposed method is examined by integrating it with the
non-dominated sorting genetic algorithm (NSGAII) in multi-objective
problems. In terms of density and optimality of the Pareto front, the
hybrid NSGAII-ANN is able to extract the Pareto front with much less
simulation time compared to the sole use of the NSGAII algorithm. The
proposed NSGAII-ANN methodology was examined using three standard test
problems (FON, KUR, and ZDT1) and one real-world problem. The latter
addresses the operation of a reservoir with two objectives (meeting
demand and flood control). Thus, based on this study, use of the
NSGAII-ANN integrative algorithm in problems with time-consuming
simulators reduces the required time for optimization up to 50
times. Results of the real-world problem, despite lower
computational-time requirements, show a performance similar to that
achieved in the aforementioned test problems.

2013





* 502(<-235): COMBINING EVOLUTION STRATEGY WITH ORDINAL OPTIMIZATION

In this paper, we combine evolution strategy (ES) with ordinal
optimization (OO), abbreviated as ES+OO, to solve real-time
combinatorial stochastic simulation optimization problems with huge
discrete solution space. The first step of ES+OO is to use an
artificial neural network (ANN) to construct a surrogate model to
roughly evaluate the objective value of a solution. In the second
step, we apply ES assisted by the ANN-based surrogate model to the
considered problem to obtain a subset of good enough solutions. In the
last step, we use the exact model to evaluate each solution in the
good enough subset, and the best one is the final good enough
solution. We apply the proposed algorithm to a wafer testing problem,
which is formulated as a combinatorial stochastic simulation
optimization problem that consists of a huge discrete solution space
formed by the vector of threshold values in the testing process. We
demonstrate that (a) ES+OO outperforms the combination of genetic
algorithm (GA) with OO using extensive simulations in the wafer
testing problem, and its computational efficiency is suitable for
real-time application, (b) the merit of using OO approach in solving
the considered problem and (c) ES+OO can obtain the approximate Pareto
optimal solution of the multi-objective function resided in the
considered problem. Above all, we propose a systematic procedure to
evaluate the performance of ES+OO by providing a quantitative result.

2013





* 505(<-678): Artificial neural network representations for hierarchical preference structures

In this paper, we introduce two artificial neural network formulations
that can be used to assess the preference ratings from the pairwise
comparison matrices of the Analytic Hierarchy Process. First, we
introduce a modified Hopfield network that can determine the vector of
preference ratings associated with a positive reciprocal comparison
matrix. The dynamics of this network are mathematically equivalent to
the power method, a widely used numerical method for computing the
principal eigenvectors of square matrices. However, this Hopfield
network representation is incapable of generalizing the preference
patterns, and consequently is not suitable for approximating the
preference ratings if the pairwise comparison judgments are
imprecise. Second, we present a feed-forward neural network
formulation that does have the ability to accurately approximate the
preference ratings. We use a simulation experiment to verify the
robustness of the feed-forward neural network formulation with respect
to imprecise pairwise judgments. From the results of this experiment,
we conclude that the feed-forward neural network formulation appears
to be a powerful tool for analyzing discrete alternative multicriteria
decision problems with imprecise or fuzzy ratio-scale preference
judgments. Copyright (C) 1996 Elsevier Science Ltd

1996





* 519(<-366): Multi-objective memetic algorithm: comparing artificial neural networks and pattern search filter method approaches

In this work, two methodologies to reduce the computation time of
expensive multi-objective optimization problems are compared. These
methodologies consist of the hybridization of a multi-objective
evolutionary algorithm (MOEA) with local search procedures. First, an
inverse artificial neural network proposed previously, consisting of
mapping the decision variables into the multiple objectives to be
optimized in order to generate improved solutions on certain
generations of the MOEA, is presented. Second, a new approach based on
a pattern search filter method is proposed in order to perform a local
search around certain solutions selected previously from the Pareto
frontier. The results obtained, by the application of both
methodologies to difficult test problems, indicate a good performance
of the approaches proposed.

2011





* 653(<-664): An artificial neural network approach to multicriteria model selection

This paper presents an intelligent decision support system based on
neural network technology for multicriteria model selection. This
paper categorizes the problem as simple, utility / value, interactive
and outranking type of problem according to six basic features. The
classification of the problem is realized based on a two-step neural
network analysis applying back-propagation algorithm. The first
Artificial Neural Network (ANN) model that is used for the selection
of an appropriate solving method cluster consists of one hidden
layer. The six input neurons of the model represent the MCDM problem
features while the two output neurons represent the four MCDM
categories. The second ANN model is used for the selection of a
specific method within the selected cluster.

2001