-*- mode: org -*-
Applications in business, ERP, and finance
*  11(<-400): Assessing bank soundness with classification techniques

The recent crisis highlighted, once again, the importance of early
warning models to assess the soundness of individual banks. In the
present study, we use six quantitative techniques originating from
various disciplines to classify banks in three groups. The first group
includes very strong and strong banks: the second one includes
adequate banks, while the third group includes banks with weaknesses
or serious problems. We compare models developed with financial
variables only, with models that incorporate additional information in
relation to the regulatory environment, institutional development, and
macroeconomic conditions. The accuracy of classification of the models
that include only financial variables is rather poor. We observe a
substantial improvement in accuracy when we consider the country-level
variables, with five out of the six models achieving out-of-sample
classification accuracy above 70% on average. The models developed
with multi-criteria decision aid and artificial neural networks
achieve the highest accuracies. We also explore the development of
stacked models that combine the predictions of the individual models
at a higher level. While the stacked models outperform the
corresponding individual models in most cases, we found no evidence
that the best stacked model can outperform the best individual
model. (C) 2009 Published by Elsevier Ltd.

2010



*  28(<-518): A Multi-criteria Convex Quadratic Programming model for credit data analysis

Speed and scalability are two essential issues in data mining and
knowledge discovery. This paper proposed a mathematical programming
model that addresses these two issues and applied the model to Credit
Classification Problems. The proposed Multicriteria Convex Quadric
Programming (MCQP) model is highly efficient (computing time
complexity O(n(1.5-2))) and scalable to massive problems (size of
O(10(9))) because it only needs to solve linear equations to find the
global optimal solution. Kernel functions were introduced to the model
to solve nonlinear problems. In addition, the theoretical relationship
between the proposed MCQP model and SVM was discussed. (c) 2007
Elsevier B.V. All rights reserved.

2008



*  29(<-587): A new multi-criteria Convex Quadratic Programming model for credit analysis

Mathematical programming based methods have been applied to credit
risk analysis and have proven to be powerful tools. One challenging
issue in mathematical programming is the computation complexity in
finding optimal solutions. To overcome this difficulty, this paper
proposes a Multi-criteria Convex Quadratic Programming model
(MCCQP). Instead of looking for the global optimal solution, the
proposed model only needs to solve a set of linear equations. We test
the model using three credit risk analysis datasets and compare MCCQP
results with four well-known classification methods: LDA, Decision
Tree, SVMLight, and LibSVM. The experimental results indicate that the
proposed MCCQP model achieves as good as or even better classification
accuracies than other methods.

2006





*  49(<- 83): LG-Trader: Stock trading decision support based on feature selection by weighted localized generalization error model

Stock trading is an important financial activity of human society. Machine learning techniques are adopted to provide trading decision support by predicting the stock price or trading signals of the next day. Decisions are made by analyzing technical indices and fundamental analysis of companies. There are two major machine learning research problems for stock trading decision support: classifier architecture selection and feature selection. In this work, we propose the LG-Trader which will deal with these two problems simultaneously using a genetic algorithm minimizing a new Weighted Localized Generalization Error (wL-GEM). An issue being ignored in current machine learning based stock trading researches is the imbalance among buy, hold and sell decisions. Usually hold decision is the majority in comparison to both buy and sell decisions. So, the wL-GEM is proposed to balance classes by penalizing heavier for generalization error being made in minority classes. The feature selection based on wL-GEM helps to select most useful technical indices among choices for each stock. Experimental results demonstrate that the LG-Trader yields higher profits and rates of return in both stock and index trading. (C) 2014 Elsevier B.V. All rights reserved.

2014





*  65(<-363): Multi-criteria ABC analysis using artificial-intelligence-based classification techniques

ABC analysis is a popular and effective method used to classify
inventory items into specific categories that can be managed and
controlled separately. Conventional ABC analysis classifies inventory
items three categories: A, B, or C based on annual dollar usage of an
inventory item. Multi-criteria inventory classification has been
proposed by a number of researchers in order to take other important
criteria into consideration. These researchers have compared
artificial-intelligence (AI)-based classification techniques with
traditional multiple discriminant analysis (MDA). Examples of these
AI-based techniques include support vector machines (SVMs),
backpropagation networks (BPNs), and the k-nearest neighbor (k-NN)
algorithm. To test the effectiveness of these techniques,
classification results based on four benchmark techniques are
compared. The results show that AI-based techniques demonstrate
superior accuracy to MDA. Statistical analysis reveals that SVM
enables more accurate classification than other AI-based
techniques. This finding suggests the possibility of implementing
AI-based techniques for multi-criteria ABC analysis in enterprise
resource planning (ERP) systems. (C) 2010 Elsevier Ltd. All rights
reserved.

2011



*  73(<-343): Building a qualitative recruitment system via SVM with MCDM approach

Advances in information technology have led to behavioral changes in
people and submission of curriculum vitae (CV) via the Internet has
become an often-seen phenomenon. Without any technological support for
the filtering process, recruitment can be difficult. In this research,
a method combining five-factor personality inventory, support vector
machine (SVM), and multi-criteria decision-making (MCDM) method was
proposed to improve the quality of recruiting appropriate
candidates. The online questionnaire personality testing developed by
the International Personality Item Pool (IPIP) was utilized to
identify the personal traits of candidates and both SVM and MCDM were
employed to predict and support the decision of personnel choice. SVM
was utilized to predict the fitness of candidates, while MCDM was
employed to estimate the performance for a job placement. The results
show the proposed system provides a qualified matching according to
the results collected from enterprise managers.

2011





*  80(<-108): Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors

With the fast development of financial products and services, bank's credit departments collected large amounts of data, which risk analysts use to build appropriate credit scoring models to evaluate an applicant's credit risk accurately. One of these models is the Multi-Criteria Optimization Classifier (MCOC). By finding a trade-off between overlapping of different classes and total distance from input points to the decision boundary, MCOC can derive a decision function from distinct classes of training data and subsequently use this function to predict the class label of an unseen sample. In many real world applications, however, owing to noise, outliers, class imbalance, nonlinearly separable problems and other uncertainties in data, classification quality degenerates rapidly when using MCOC. In this paper, we propose a novel multi-criteria optimization classifier based on kernel, fuzzification, and penalty factors (KFP-MCOC): Firstly a kernel function is used to map input points into a high-dimensional feature space, then an appropriate fuzzy membership function is introduced to MCOC and associated with each data point in the feature space, and the unequal penalty factors are added to the input points of imbalanced classes. Thus, the effects of the aforementioned problems are reduced. Our experimental results of credit risk evaluation and their comparison with MCOC, support vector machines (SVM) and fuzzy SVM show that KFP-MCOC can enhance the separation of different applicants, the efficiency of credit risk scoring, and the generalization of predicting the credit rank of a new credit applicant. (C) 2014 Elsevier B.V. All rights reserved.

2014





*  83(<-333): SINGLE-LAYER PERCEPTRON WITH NON-ADDITIVE PREFERENCE INDICES AND ITS APPLICATION TO BANKRUPTCY PREDICTION

Preference Ranking Organization METHods for Enrichment Evaluations
(PROMETHEE), based on outranking relation theory, are used extensively
in Multi-Criteria Decision Aid (MCDA). In PROMETHEE, an overall
preference index based on weighted average aggregation represents the
intensity of preference for one pattern over another pattern and can
be measured by a given preference function. Unfortunately, as the
criteria making up the patterns are not always independent, the
assumption of additivity among single-criterion preference indices may
not be reasonable. This paper develops a novel PROMETHEE-based
perceptron using nonadditive preference indices for ordinal sorting
problems. The applicability of the proposed non-additive
PROMETHEE-based single-layer perceptron (SLP) to bankruptcy prediction
is examined by using a sample of 53 publicly traded, Taiwanese firms
that encountered financial failure between 2000 and 2008. The proposed
model performs well compared to PROMETHEE with additive preference
indices and other additive PROMETHEE-based classification approaches.

2011





*  85(<-459): Bankruptcy prediction using ELECTRE-based single-layer perceptron

For the outranking relation theory, the ELECTRE methods are one of the
most extensively used outranking methods. To measure the degree of
agreement and the degree of disagreement of the proposition "one
alternative outranks another alternative", the concordance and
discordance relations are usually associated with the outranking
relation. Instead of the traditional single-layer perceptron (SLP)
developed according to the multiple-attribute utility theory, this
paper contributes to develop a novel ELECTRE-based SLP for
multicriteria classification problems based on the ELECTRE methods
involving pairwise comparisons among patterns. A
genetic-algorithm-based method is then designed to determine
connection weights. A real-world data set involving bankruptcy
analysis obtained from Moody's Industrial Manuals is employed to
examine the classification performance of the proposed ELECTRE-based
model. The results demonstrate that the proposed model performs well
compared to an arsenal of well-known classification methods involving
quantitative disciplines of statistics and machine learning. (C) 2009
Elsevier B.V. All rights reserved.

2009




* 139(<-322): Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems

Risk and return are interdependent in a stock portfolio. To achieve
the anticipated return, comparative risk should be considered
simultaneously. However, complex investment environments and dynamic
change in decision making criteria complicate forecasts of risk and
return for various investment objects. Additionally, investors often
fail to maximize their profits because of improper capital
allocation. Although stock investment involves multi-criteria decision
making (MCDM), traditional MCDM theory has two shortfalls: first, it
is inappropriate for decisions that evolve with a changing
environment; second, weight assignments for various criteria are often
oversimplified and inconsistent with actual human thinking
processes. In 1965, Rechenberg proposed evolution strategies for
solving optimization problems involving real number parameters and
addressed several flaws in traditional algorithms, such as their use
of point search only and their high probability of falling into
optimal solution area. In 1992, Hillis introduced the co-evolutionary
concept that the evolution of living creatures is interactive with
their environments (multi-criteria) and constantly improves the
survivability of their genes, which then expedites evolutionary
computation. Therefore, this research aimed to solve multi-criteria
decision making problems of stock trading investment by integrating
evolutionary strategies into the co-evolutionary criteria evaluation
model. Since co-evolution strategies are self-calibrating, criteria
evaluation can be based on changes in time and environment. Such
changes not only correspond with human decision making patterns (i.e.,
evaluation of dynamic changes in criteria), but also address the
weaknesses of multi-criteria decision making (i.e., simplified
assignment of weights for various criteria). Co-evolutionary evolution
strategies can identify the optimal capital portfolio and can help
investors maximize their returns by optimizing the preoperational
allocation of limited capital. This experimental study compared
general evolution strategies with artificial neural forecast model,
and found that co-evolutionary evolution strategies outperform general
evolution strategies and substantially outperform artificial neural
forecast models. The co-evolutionary criteria evaluation model avoids
the problem of oversimplified adaptive functions adopted by general
algorithms and the problem of favoring weights but failing to
adaptively adjust to environmental change, which is a major limitation
of traditional multi-criteria decision making. Doing so allows
adaptation of various criteria in response to changes in various
capital allocation chromosomes. Capital allocation chromosomes in the
proposed model also adapt to various criteria and evolve in ways that
resemble thinking patterns. (C) 2011 Elsevier Inc. All rights
reserved.

2011





* 148(<-461): Pairs selection and outranking: An application to the S&P 100 index

Pairs trading is a popular quantitative speculation strategy. This
article proposes a general and flexible framework for pairs
selection. The method uses multiple return forecasts based on
bivariate information sets and multi-criteria decision techniques. Our
approach can be seen as a sort of forecast combination but the output
of the method is a ranking. It helps to detect potentially under- and
overvalued stocks. A first application with S&P 100 index stocks
provides promising results in terms of excess return and directional
forecasting. (C) 2008 Elsevier B.V. All rights reserved.

2009




* 151(<-142): A new multicriteria approach for the analysis of efficiency in the Spanish olive oil sector by modelling decision maker preferences

The efficiency in production is often analysed as technical efficiency
using the production frontier function. Efficiency scores are usually
based on distance computations to the frontier in an m + s-dimensional
space, where m inputs produce s outputs. In addition, efficiency
improvements consider the total consumption of each input. However, in
many cases, the "consumption" of each input can be divided into
input-consumption sections (ICSs), and trade-off among the ICSs is
possible. This share framework can be used for computing
efficiency. This analysis provides information about both the total
optimal consumption of each input, as does data envelopment analysis,
and the most efficient allocation of the "consumption" among the
ICSs. This paper studies technical efficiency using this approach and
applies it to the olive oil sector in Andalusia (Spain). A
non-parametrical methodology is presented, and an input-oriented
Multi-Criteria Linear Programming model (MLP) is proposed. The
analysis is developed at global, input and ICSs levels, defining the
extent of satisfaction achieved at all these levels for each company,
in accordance with their own preferences. The companies' preferences
are modelled with their utility function and their set of weights. MLP
offers more detailed information to assist decision makers than other
models previously proposed in the literature. In addition to this
application, it is concluded that there is room for improvement in the
olive oil sector, particularly in the management of the skilled
labour. Additionally, the solutions with two opposite scenarios
indicate that the model is suitable for the intended decision making
process. (C) 2013 Elsevier B.V. All rights reserved.

2014




* 254(<-612): Evolutionary multiobjective optimization approach for evolving ensemble of intelligent paradigms for stock market modeling

The use of intelligent systems for stock market predictions has been widely established. This paper introduces a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. As evident from the empirical results, none of the five considered techniques could find an optimal solution for all the four performance measures. Further the results obtained by these five techniques are combined using an ensemble and two well known Evolutionary Multiobjective Optimization (EMO) algorithms namely Non-dominated Sorting Genetic Algorithm II (NSGA II) and Pareto Archive Evolution Strategy (PAES) algorithms in order to obtain an optimal ensemble combination which could also optimize the four different performance measures (objectives). We considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index as test data. Empirical results reveal that the resulting ensemble obtain the best results.

2005




* 282(<- 57): Modelling commodity value at risk with Psi Sigma neural networks using open-high-low-close data

The motivation for this paper is to investigate the use of a promising
class of neural network models, Psi Sigma (PSI), when applied to the
task of forecasting the one-day ahead value at risk (VaR) of the oil
Brent and gold bullion series using open-high-low-close data. In order
to benchmark our results, we also consider VaR forecasts from two
different neural network designs, the multilayer perceptron and the
recurrent neural network, a genetic programming algorithm, an extreme
value theory model along with some traditional techniques such as an
ARMA-Glosten, Jagannathan, and Runkle (1,1) model and the RiskMetrics
volatility. The forecasting performance of all models for computing
the VaR of the Brent oil and the gold bullion is examined over the
period September 2001-August 2010 using the last year and half of data
for out-of-sample testing. The evaluation of our models is done by
using a series of backtesting algorithms such as the Christoffersen
tests, the violation ratio and our proposed loss function that
considers not only the number of violations but also their
magnitude. Our results show that the PSI outperforms all other models
in forecasting the VaR of gold and oil at both the 5% and 1%
confidence levels, providing an accurate number of independent
violations with small magnitude.

2015





* 300(<-534): Functional-link net with fuzzy integral for bankruptcy prediction

The classification ability of a single-layer perceptron could be improved by considering some enhanced features. In particular, this form of neural networks is called a functional-link net. In the output neuron's activation function, such as the sigmoid function, an inner product of a connection weight vector with an input vector is computed. However, since the input features are not independent of each other for the enhanced pattern, an assumption of the additivity is not reasonable. This paper employs a non-additive technique, namely the fuzzy integral, to aggregate performance values for an input pattern by interpreting each of the connection weights as a fuzzy measure of the corresponding feature. A learning algorithm with the genetic algorithm is then designed to automatically find connection weights. The sample data for bankruptcy analysis obtained from Moody's Industrial Manuals is considered to examine the classification ability of the proposed method. The results demonstrate that the proposed method performs well in comparison with traditional functional-link net and multivariate techniques. (c) 2006 Elsevier B.V. All rights reserved.

2007





* 322(<-315): A dynamic feedback model for partner selection in agile supply chains

Purpose - The purpose of this paper is to present a four-phase dynamic
feedback model for supply partner selection in agile supply chains
(ASCs). ASCs are commonly used as a response to increasingly dynamic
markets. However, partner selection in ASCs is inherently more complex
and difficult under conditions of uncertainty and ambiguity as supply
chains form and re-form. Design/methodology/approach - The model draws
on both quantitative and qualitative techniques, including the
Dempster-Shafer and optimisation theories, radial basis function
artificial neural net:works (RBF-ANN), analytic network process-mixed
integer multi-objective programming (ANP-MIMOP), Kraljic's supplier
classification matrix and principles of continuous improvement. It
incorporates modern computer programming techniques to overcome the
information processing difficulties inherent in selecting from amongst
large numbers of potential suppliers against multiple criteria in
conditions of uncertainty. Findings - The model enables decision
makers to make efficient and effective use of the vastly increased
amount of data that is available in today's information-driven society
and it offers a comprehensive, systematic and rigorous approach to a
complex problem. Research limitations/implications - The model has two
main drawbacks. First, practitioners may find it difficult to match
supplier evaluation criteria with the strategic objectives for an
ASC. Second, they may perceive the model to be too complex for use
when speed is of the essence. Originality/value - The main
contribution of this paper is that, for the first time, it draws
together work from previous articles that have described each of the
four stages of the model in detail to present a comprehensive overview
of the model.

2012



* 323(<-342): A multiple kernel learning-based decision support model for contractor pre-qualification

Due to the complex nature of the contractor pre-qualification such as
subjectivity, non-linearity and multi-criteria, advanced model should
be required for achieving a high accuracy of this decision-making
process. Previous studies have been conducted to build up quantitative
decision models for contractor pre-qualification, among them
artificial neural network (ANN) and support vector machine (SVM) have
been proved to be desirable in solving the pre-qualification problem
with regards to their higher accuracy and efficiency for solving the
non-linear problem of classification. Based on the algorithm of SVM,
multiple kernel learning (MKL) method was developed and it has been
proved to perform better than SVM in other areas. Hence, MKL is
proposed in this research, the capability of MKL was compared with SVM
through a case study. From the result, it has been proved that both
SVM and MKL perform well in classification, and MKL is more preferable
than SVM, with a proper parameter setting. Therefore, MKL can enhance
the decision making of contractor pre-qualification. (C) 2010 Elsevier
B.V. All rights reserved.

2011





* 385(<-563): Learning-based automated negotiation between shipper and forwarder

This paper studies an automated negotiation system by means of a
learning-based approach. Negotiation between shipper and forwarder is
used as an example in which the issues of negotiation are unit
shipping price, delay penalty, due date, and shipping quantity. A data
ratios method is proposed as the input of the neural network technique
to explore the learning in automated negotiation with the negotiation
decision functions (NDFs) developed by [Faratin, P., Sierra, C., &
Jennings, N.R. (1998). Negotiation Decision Functions for Autonomous
Agents. Robotics and Autonomous Systems, 24 (3), 159-182]. The
concession tactic and weight of every issue offered by the opponent
can be learned from this process exactly. After learning, a trade-off
mechanism can be applied to achieve better negotiation result on the
distance to Pareto optimal solution. Based on the results of this
study, we believe that our findings can provide more insight into
agent-based negotiation and can be applied to improve negotiation
processes. (c) 2006 Elsevier Ltd. All rights reserved.

2006




* 390(<-388): Pairs trading and outranking: The multi-step-ahead forecasting case

Pairs trading is a popular speculation strategy. Several
implementation methods are proposed in the literature: they can be
based on a distance criterion or on co-integration. This article
extends previous research in another direction: the combination of
forecasting techniques (Neural Networks) and multi-criteria decision
making methods (Electre III). The key contribution of this paper is
the introduction of multi-step-ahead forecasts. It leads to major
changes in the trading system and raises new empirical and
methodological questions. The results of an application based on S&P
100 Index stocks are promising: this methodology could be a powerful
tool for pairs selection in a highly non-linear environment. (C) 2010
Elsevier B.V. All rights reserved.

2010




* 405(<- 27): EFFECTS OF PROJECT UNCERTAINTIES ON NONLINEAR TIME-COST TRADEOFF PROFILE

This study presents the effects of project uncertainties on nonlinear
time-cost tradeoff (TOT) profile of real life engineering projects by
the fusion of fuzzy logic and artificial neural network (ANN) models
with hybrid meta-heuristic (HMH) technique, abridged as
Fuzzy-ANN-HMH. Nonlinear time-cost relationship of project activities
is dealt with ANN models. ANN models are then integrated with HMH
technique to search for Pareto-optimal nonlinear TCT profile. HMH
technique incorporates simulated annealing in the selection process of
multiobjective genetic algorithm. Moreover, in real life engineering
projects, uncertainties like management experience, labor skills, and
weather conditions are commonly involved, which affect the duration
and cost of the project activities. Fuzzy-ANN-HMH analyses
responsiveness of nonlinear TOT profile with respect to these
uncertainties. A comparison of Fuzzy-ANN-HMH is made with another
method in literature to solve nonlinear TCT problem and the
superiority of Fuzzy-ANN-HMH is demonstrated by results. The study
gives project planners to carry out the best plan that optimizes time
and cost to complete a project under uncertain environment.

2015



* 406(<-169): Integrated ANN-HMH Approach for Nonlinear Time-Cost Tradeoff Problem

This paper presents an integrated Artificial Neural Network - Hybrid
Meta Heuristic(ANN-HMH) method to solve the nonlinear time-cost
tradeoff(TCT) problem of real life engineering projects. ANN models
help to capture the existing nonlinear time-cost relationship in
project activities. ANN models are then integrated with HMH technique
to search for optimal TCT profile. HMH is a proven evolutionary
multiobjective optimization technique for solving TCT problems. The
study has implication in real time monitoring and control of project
scheduling processes.

2014


* 407(<-522): Neural network embedded multiobjective genetic algorithm to solve non-linear time-cost tradeoff problems of project scheduling

This paper presents a novel method to solve non-linear time-cost
tradeoff (TCT) problem of real world engineering
projects. Multiobjective genetic algorithm (MOGA) is employed to
search for optimal TCT profile. Applicability of ANN based model for
rapid estimation of time-cost relationship by invoking its function
approximation capability is investigated, ANN models are then
integrated with MOGA so as to develop a comprehensive approach to
solve non-linear TCT problems of project scheduling. The study has
implications in real time monitoring and control of project scheduling
process.

2008






* 432(<-500): Determining the parameters of dual-card kanban system: an integrated multicriteria and artificial neural network methodology

In this study, we proposed a methodology for determining the design
parameters of kanban systems. In this methodology, a backpropagation
neural network is used in order to generate simulation meta-models,
and a multi-criteria decision making technique (TOPSIS) is employed to
evaluate kanban combinations. In order to reflect the decision maker's
point of view, different weight structures are used to find the
optimum design parameters. The proposed methodology is applied to a
case problem and the results are presented. We also performed several
experiments on different types of problems to show the effectiveness
of the methodology.

2008




* 509(<-630): A comparison of discrete and continuous neural network approaches to solve the class/teacher timetabling problem

This study explores the application of neural network-based heuristics
to the class/teacher timetabling problem (CTTP). The paper begins by
presenting the problem characteristics in terms of hard and soft
constraints and proposing a formulation for the energy function
required to map the issue within the artificial neural network
model. There follow two distinct approaches to simulating neural
network evolution. The first uses a Potts mean-field annealing
simulation based on continuous Potts neurons, which has obtained
favorable results in various combinatorial optimization
problems. Afterwards, a discrete neural network simulation, with
discrete winner-takes-all neurons, is proposed. The paper concludes
with a comparison of the computational results taken from the
application of both heuristics to hard hypothetical and real CTTP
instances. This experiment demonstrates that the discrete approach
performs better, in terms of solution quality as well as execution
time. By extending the comparison, the neural discrete solutions are
also compared with those obtained from a multiobjective genetic
algorithm, which is already being successfully used for this problem
within a timetabling software application. (C) 2003 Elsevier B.V. All
rights reserved.

2004





* 525(<-178): A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system

One important issue related to the implementation of cellular
manufacturing systems (CMSs) is to decide whether to convert an
existing job shop into a CMS comprehensively in a single run, or in
stages incrementally by forming cells one after the other, taking the
advantage of the experiences of implementation. This paper presents a
new multi-objective nonlinear programming model in a dynamic
environment. Furthermore, a novel hybrid multi-objective approach
based on the genetic algorithm and artificial neural network is
proposed to solve the presented model. From the computational
analyses, the proposed algorithm is found much more efficient than the
fast non-dominated sorting genetic algorithm (NSGA-II) in generating
Pareto optimal fronts. (C) 2013 Elsevier Ltd. All rights reserved.

2013