Abstract: Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining different techniques and their respective strengths. In this paper, we present a hybrid intelligent system combining Autoregressive Integrated Moving Average (ARIMA) models and neural networks for demand forecasting. We show improvements in forecasting accuracy and propose a replenishment system for a Chilean supermarket, which leads simultaneously to fewer sales failures and lower inventory levels than the previous solution.

Abstract: Differential evolution (DE) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, which use different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.

Abstract: We introduce the idea of centered OWA operators. We define these as OWA operators that give preference to argument values that lie in the middle between the largest and the smallest. An important class of these using Gaussian type weights is investigated in considerable detail. We describe a number of different examples of centered OWA operators.

Abstract: This study investigates the effectiveness of a hybrid approach based on the artificial neural networks (ANNs) for time series properties, such as the adaptive time delay neural networks (ATNNs) and the time delay neural networks (TDNNs), with the genetic algorithms (GAs) in detecting temporal patterns for stock market prediction tasks. Since ATNN and TDNN use time-delayed links of the network into a multi-layer feed-forward network, the topology of which grows by on layer at every time step, it has one more estimate of the number of time delays in addition to several control variables of the ANN design. To estimate these many aspects of the ATNN and TDNN design, a general method based on trial and error along with various heuristics or statistical techniques is proposed. However, for the reason that determining the number of time delays or network architectural factors in a stand-alone mode does not guarantee the illuminating improvement of the performance for building the ATNN and TDNN model, we apply GAs to support optimization of the number of time delays and network architectural factors simultaneously for the ATNN and TDNN model. The results show that the accuracy of the integrated approach proposed for this study is higher than that of the standard ATNN, TDNN and the recurrent neural network (RNN).

Abstract: We present the results of our investigation into the use of genetic algorithms (GAs) for identifying near optimal design parameters of diagnostic systems that are based on artificial neural networks (ANNs) for condition monitoring of mechanical systems. ANNs have been widely used for health diagnosis of mechanical bearing using features extracted from vibration and acoustic emission signals. However, different sensors and the corresponding features exhibit varied response to different faults. Moreover, a number of different features can be used as inputs to a classifier ANN. Identification of the most useful features is important for an efficient classification as opposed to using all features from all channels, leading to very high computational cost and is, consequently, not desirable. Furthermore, determining the ANN structure is a fundamental design issue and can be critical for the classification performance. We show that a GA can be used to select a smaller subset of features that together form a genetically fit family for successful fault identification and classification tasks. At the same time, an appropriate structure of the ANN, in terms of the number of nodes in the hidden layer, can be determined, resulting in improved performance.

Abstract: The aim of this paper is to develop a compromise ratio (CR) methodology for fuzzy multi-attribute group decision making (FMAGDM), which is an important part of decision support system. Owing to fuzziness being inherent in decision data and group decision making processes, the crisp values are inadequate to model real-life situations. In this paper, the weights of all attributes and the ratings of each alternative with respect to each attribute are described by linguistic terms which can be expressed in trapezoid fuzzy numbers. A fuzzy distance measure is developed to calculate difference between trapezoid fuzzy numbers. The compromise ratio method for FMAGDM is developed by introducing the ranking index based on the concept that the chosen alternative should be as close as possible to the ideal solution and as far away from the negative-ideal solution as possible simultaneously. The computation principle and procedure of the compromise ratio method are described in detail in this paper. Moreover the TOPSIS method which was developed for multi-attribute decision making (MADM) with crisp decision data is analyzed and extended to multi-attribute group decision making (MAGDM) under fuzzy environments. A comparative analysis of the compromise ratio method and the extended fuzzy TOPSIS method is illustrated with a numerical example, showing their similarity and some differences.

Abstract: Single neuron models are typical functional replica of the biological neuron that are derived using their individual and group responses in networks. In recent past, a lot of work in this area has produced advanced neuron models for both analog and binary data patterns. Popular among these are the higher-order neurons, fuzzy neurons and other polynomial neurons. In this paper, we propose a new neuron model based on a polynomial architecture. Instead of considering all the higher-order terms, a simple aggregation function is used. The aggregation function is considered as a product of linear functions in different dimensions of the space. The functional mapping capability of the proposed neuron model is demonstrated through some well known time series prediction problems and is compared with the standard multilayer neural network.

Abstract: This paper presents a nuclear case study, in which a fuzzy inference system (FIS) is used as alternative approach in risk analysis. The main objective of this study is to obtain an understanding of the aging process of an important nuclear power system and how it affects the overall plant safety. This approach uses the concept of a pure fuzzy logic system where the fuzzy rule base consists of a collection of fuzzy IF-THEN rules. The fuzzy inference engine uses these fuzzy IF-THEN rules to determine a mapping from fuzzy sets in the input universe of discourse to fuzzy sets in the output universe of discourse based on fuzzy logic principles. The risk priority number (RPN), a traditional analysis parameter, was calculated and compared to fuzzy risk priority number (FRPN) using scores from expert opinion to probabilities of occurrence, severity and not detection. A standard four-loop pressurized water reactor (PWR) containment cooling system (CCS) was used as example case. The results demonstrated the potential of the inference system for subsiding the failure modes and effects analysis (FMEA) in aging studies.

Abstract: Parallel memetic algorithms (PMAs) are a class of modern parallel meta-heuristics that combine evolutionary algorithms, local search, parallel and distributed computing technologies for global optimization. Recent studies on PMAs for large-scale complex combinatorial optimization problems have shown that they converge to high quality solutions significantly faster than canonical GAs and MAs. However, the use of local learning for every individual throughout the PMA search can be a very computationally intensive and inefficient process. This paper presents a study on two diversity-adaptive strategies, i.e., (1) diversity-based static adaptive strategy (PMA-SLS) and (2) diversity-based dynamic adaptive strategy (PMA-DLS) for controlling the local search frequency in the PMA search. Empirical study on a class of NP-hard combinatorial optimization problem, particularly large-scale quadratic assignment problems (QAPs) shows that the diversity-adaptive PMA converges to competitive solutions at significantly lower computational cost when compared to the canonical MA and PMA. Furthermore, it is found that the diversity-based dynamic adaptation strategy displays better robustness in terms of solution quality across the class of QAP problems considered. Static adaptation strategy on the other hand requires extra effort in selecting suitable parameters to suit the problems in hand.

Abstract: In this paper, we present a multi-surrogates assisted memetic algorithm for solving optimization problems with computationally expensive fitness functions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogate in the spirit of Lamarckian learning. Inspired by the notion of ‘blessing and curse of uncertainty’ in approximation models, we combine regression and exact interpolating surrogate models in the evolutionary search. Empirical results are presented for a series of commonly used benchmark problems to demonstrate that the proposed framework converges to good solution quality more efficiently than the standard genetic algorithm, memetic algorithm and surrogate-assisted memetic algorithms.

Abstract: E-commerce markets can increase their efficiency through the usage of intelligent agents which negotiate and execute contracts on behalf of their owners. The measurement and computation of trust to secure interactions between autonomous agents is crucial for the success of automated e-commerce markets. Building a knowledge sharing network among peer agents helps to overcome trust-related boundaries in an environment where least human intervention is desired. Nevertheless, a risk management model which allows individual customisation to meet the different security needs of agent-owners is vital. The calculation and measurement of trust in unsupervised virtual communities like multi-agent environments involves complex aspects such as credibility rating for opinions delivered by peer agents, or the assessment of past experiences with the peer node one wishes to interact with. The deployment of suitable algorithms and models imitating human reasoning can help to solve these problems. This paper proposes not only a customisable trust evaluation model based on fuzzy logic but also demonstrates the integration of post-interaction processes like business interaction reviews and credibility adjustment. Fuzzy logic provides a natural framework to deal with uncertainty and the tolerance of imprecise data inputs to fuzzy-based systems makes fuzzy reasoning especially attractive for the subjective tasks of trust evaluation, business-interaction review and credibility adjustment.

Abstract: The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the source code into abstract structures not in the source code called subsystems. The clustering process uses evolutionary algorithms to search through the enormous set of possible graph partitions, and is guided by a fitness function designed to measure the quality of individual graph partitions. The second part of this paper focuses on evaluating the results produced by our clustering technique. Our previous research has shown through both qualitative and quantitative studies that our clustering technique produces good results quickly and consistently. In this part of the paper we study the underlying structure of the search space of several open source systems. We also report on some interesting findings our analysis uncovered by comparing random graphs to graphs representing real software systems.

Abstract: Release planning is known to be a cognitively and computationally difficult problem. Different kinds of uncertainties make it hard to formulate and solve the problem. Our solution approach called EVOLVE+ mitigates these difficulties by (i) an evolutionary problem solving method combining rigorous solution methods to solve the actual formalization of the problem combined with the interactive involvement of the human experts in this process, (ii) provision of a portfolio of diversified and qualified solutions at each iteration of the solution process, and (iii) the application of a multi-criteria decision aid method (ELECTRE IS) to assist the selection of the final solution from a set of qualified solutions. At the final stage of the process, an outranking relation is established among the qualified candidate solutions to address existing soft constraints or objectives. A case study is provided to illustrate and initially evaluate the given approach. The proposed method and results are not limited to software release planning, but can be adapted to a wider class of wicked planning problems.

Abstract: This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, ...) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.

Abstract: In fuzzy regression, that was first proposed by Tanaka et al. (Eur J Oper Res 40:389–396, 1989; Int Cong Appl Syst Cybern 4:2933–2938, 1980; IEEE Trans SystMan Cybern 12:903–907, 1982), there is a tendency that the greater the values of independent variables, the wider the width of the estimated dependent variables. This causes a decrease in the accuracy of the fuzzy regression model constructed by the least squares method. This paper suggests the least absolute deviation estimators to construct the fuzzy regression model, and investigates the performance of the fuzzy regression models with respect to a certain errormeasure. Simulation studies and examples show that the proposed model produces less error than the fuzzy regression model studied by many authors that use the least squares method when the data contains fuzzy outliers.

Abstract: In this paper we consider fundamental properties of some types of filters (Boolean, positive implicative, implicative and fantastic filters) of BL algebras defined in Haveshki et al. (Soft Comput 10:657–664, 2006) and Turunen (Arch Math Logic 40:467–473, 2001). It is proved in Haveshki et al. (2006) that if F is a maximal and (positive) implicative filter then it is a Boolean filter. In that paper there is an open problem Under what condition are Boolean filters positive implicative filters? One of our results gives an answer to the problem, that is, we need no more conditions. Moreover, we give simple characterizations of those filters by an identity form ∀ x, y(t(x, y) ∈ F), where t(x, y) is a term containing x, y.

Abstract: A novel structure of type 2 fuzzy logic controller is presented. The method is highly efficient regarding computational time and implementation effort. Type-2 input membership functions were optimized using the Human Evolutionary Model (HEM) considering as the objective function the Integral of Squared Error at the controllers output. Statistical tests were achieved considering how the error at the controller’s output is diminished in presence of uncertainty, demonstrating that the proposed method outperforms an optimized traditional type-2 fuzzy controller for the same test conditions.

Abstract: Alternative forms of neural networks have been applied to forecast daily river flows on a continuous basis with the purpose of understanding how recent architectures like ANFIS, GRNN and RBF compare with traditional FFBP when monsoon-fed rivers involving significant statistical bias are involved. The forecasts are made at a location called Rajghat along river Narmada in India. Division of yearly data into four seasons and development of separate networks accordingly was found to be more useful than a single network applicable for the entire year. When a variety of error criteria were viewed together the most satisfactory network for all seasons was the radial basis function, which showed better performance then FFBP, GRNN and ANFIS. The FFBP network was found to be equally acceptable as the RBF in seasons other than the monsoon. Generally the peak flows were more satisfactorily modeled by the RBF than FFBP, GRNN and ANFIS. The relatively simpler handling of data non-linearity in FFBP was more attractive than complex ones of ANFIS and GRNN. The representative statistical model, namely response surface method, yielded highly unsatisfactory results compared to any ANN model involved in this study, confirming that the complexity of ANNs is really necessary to model daily river flows.

Abstract: The design of fuzzy controllers for the implementation of behaviors in mobile robotics is a complex and highly time-consuming task. The use of machine learning techniques, such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process. In this paper, the automated design of a fuzzy controller using genetic algorithms for the implementation of the wall-following behavior in a mobile robot is described. The algorithm is based on the Iterative Rule Learning (IRL) approach, and a parameter (@d) is defined with the aim of selecting the relation between the number of rules and the quality and accuracy of the controller. The designer has to define the universe of discourse and the precision of each variable, and also the scoring function. No restrictions are placed neither in the number of linguistic labels nor in the values that define the membership functions.

Abstract: Techniques for detecting elemental level damage using the traditional methods receive the setback because of the difficulties in formulating the problems mathematically, specially in case of inverse problems. Artificial neural networks (ANN) have been proved to be an effective alternative for solving the inverse problems because of the pattern-matching capability. But there is no specific recommendation on suitable design of network for different structures and generally the parameters are selected by trial and error, which restricts the approach context dependent. A hybrid neuro-genetic algorithm is proposed in order to automate the design of neural network for different type of structures. The neural network is trained considering the frequency and strain as input parameter and the location and amount of damage as output parameter. The performance is demonstrated using two test problems: (i) clamped-free beam and (ii) plane frame. The outcomes of the results are quite encouraging and prove the robustness of the proposed damage assessment algorithm.

Abstract: Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research in this paper proposes a neural-genetic algorithm for feature selection to classify microcalcification patterns in digital mammograms. It aims to develop a step-wise algorithm to find the best feature set and a suitable neural architecture for microcalcification classification. The obtained results show that the proposed algorithm is able to find an appropriate feature subset, which also produces a high classification rate.

Abstract: In this paper, the statistical fuzzy interval neural network with statistical interval input and output values is proposed to perform statistical fuzzy knowledge discovery and the currency exchange rate prediction. Time series data sets are grouped into time series data granules with statistical intervals. The statistical interval data sets including week-based averages, maximum errors of estimate and standard deviations are used to train the fuzzy interval neural network to discover fuzzy IF-THEN rules. The output of the fuzzy interval neural network is an interval value with certain percent confidence. Simulations are completed in terms of the exchange rates between US Dollar and other three currencies (Japanese Yen, British Pound and Hong Kong Dollar). The simulation results show that the fuzzy interval neural network can provide more tolerant prediction results.

Abstract: As an important coordination and cooperation mechanism in multi-agent systems, coalition of agents exhibits some excellent characteristics and draws researchers' attention increasingly. Cooperation formation has been a very active area of research in multi-agent systems. An efficient algorithm is needed for this topic since the numbers of the possible coalitions are exponential in the number of agents. Genetic algorithm (GA) has been widely reckoned as a useful tool for obtaining high quality and optimal solutions for a broad range of combinatorial optimization problems due to its intelligent advantages of self-organization, self-adaptation and inherent parallelism. This paper proposes a GA-based algorithm for coalition structure formation which aims at achieving goals of high performance, scalability, and fast convergence rate simultaneously. A novel 2D binary chromosome encoding approach and corresponding crossover and mutation operators are presented in this paper. Two valid parental chromosomes are certain to produce a valid offspring under the operation of the crossover operator. This improves the efficiency and shortens the running time greatly. The proposed algorithm is evaluated through a robust comparison with heuristic search algorithms. We have confirmed that our new algorithm is robust, self-adaptive and very efficient by experiments. The results of the proposed algorithm are found to be satisfactory.

Abstract: An accurate simulation model is a necessary tool for optimizing allocation of scarce water resources in large-scale river basins. Adaptive Neural Fuzzy Inference System (ANFIS) method is used to simulate seven interconnected sub-basins in a regional river system located in Iran. Simulated predictions of the method are compared with historical data measurements. ANFIS is a powerful tool for simulating water resources systems of all sub-basins. In this study, a new methodology, Adaptive Neural Fuzzy Reinforcement Learning (ANFRL) is presented for obtaining optimal values of the decision variables. By combining ANFIS with Fuzzy Reinforcement Learning within the content of historical data over a consecutive monthly management period, ANFRL method was derived. Based upon the results of this research, this methodology can be used to develop fuzzy rule systems that accurately simulate the behavior of complex river basin systems within the context of uncertainty. As previous researches have shown that, when simulation model accurately reproduces observed river basin behavior, the optimization model yields better results. Application of this approach in the present case study shows that the effects of uncertainty, imprecise and random factors are 21, 11 and 15% over water resources system, water demand estimated and hydrological regime, respectively. Finally, the results of this method showed that about 16% improvement in water allocation was attained when compared to the primary water resources management in this case study.

Abstract: In this paper, a scheduling problem for drilling operation in a real-world printed circuit board factory is considered. Two derivatives of multi-objective genetic algorithms are proposed under two objectives, i.e. makespan and total tardiness time. The proposed algorithms possess a rare characteristic from traditional multi-objective genetic algorithms. The crossover and mutation rates of the proposed algorithms can be variables or adjusted according to the searching performance while the rates of traditional algorithm are fixed. Production data retrieved from the shop floor are used as the test instances. The numerical result indicates that both two proposed multi-objective genetic algorithms have satisfactory performance and the adaptive multi-objective genetic algorithm performs better. The result shows the algorithms are effective and efficiency to the current system used in the shop floor. Thus, the result may be of interest to practical applications.

Abstract: This paper proposes fuzzy system and neural network approaches to identify the incipient faults in the power transformer using dissolved gas analysis (DGA) method. Using the IEC/IEEE DGA criteria and the gas concentration values as references the fuzzy diagnosis system and neural network are built. The proposed systems are verified using practical data collected from Electricity Board. The fuzzy system is tested with triangular, trapezoidal and Gaussian membership functions and its effectiveness is analyzed through simulation in terms of accuracy in identifying the transformer faults. The proposed Back propagation network is verified to overcome the drawbacks of conventional methods. The proposed schemes are simulated and tested in the software environment. The simulation results are presented.

Abstract: Classification and association rule discovery are important data mining tasks. Using association rule discovery to construct classification systems, also known as associative classification, is a promising approach. In this paper, a new associative classification technique, Ranked Multilabel Rule (RMR) algorithm is introduced, which generates rules with multiple labels. Rules derived by current associative classification algorithms overlap in their training objects, resulting in many redundant and useless rules. However, the proposed algorithm resolves the overlapping between rules in the classifier by generating rules that does not share training objects during the training phase, resulting in a more accurate classifier. Results obtained from experimenting on 20 binary, multi-class and multi-label data sets show that the proposed technique is able to produce classifiers that contain rules associated with multiple classes. Furthermore, the results reveal that removing overlapping of training objects between the derived rules produces highly competitive classifiers if compared with those extracted by decision trees and other associative classification techniques, with respect to error rate.

Abstract: Analytical methods are proposed for solving fuzzy linear system of equations when the composition is max-product. These methods provide universal algorithm for computing the greatest solution and the set of all minimal solutions, when the system is consistent. In case of inconsistency, the equations that can not be satisfied are obtained.

Abstract: Sensitivity analysis is a general technique for investigating the robustness of the output of a mathematical model and is performed for various different purposes. The practicability of conducting such an analysis of a probabilistic network has recently been studied extensively, resulting in a variety of new insights and effective methods, ranging from properties of the mathematical relation between a parameter and an output probability of interest, to methods for establishing the effects of parameter variation on decisions based on the output distribution computed from a network. In this paper, we present a survey of some of these research results and explain their significance.