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This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. [ABSTRACT FROM AUTHOR]

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k -labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection by achieving better coverage of labels and inter-label correlations. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared to the RAKEL algorithm and to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Operational support assists users while process instances are being executed, by making predictions about the instance completion, or recommending suitable actions, resources or routing decisions, on the basis of the already completed instances, stored as execution traces in the event log. In this paper, we propose a case-based retrieval approach to business process management operational support, where log traces are exploited as cases. Once past traces have been retrieved, classical statistical techniques can be applied to them, to support prediction and recommendation. The framework enables the user to submit queries able to express complex patterns exhibited by the current process instance. Such queries can be composed by several simple patterns (i.e., single actions, or direct sequences of actions), separated by delays (i.e., actions we do not care about). Delays can also be imprecise (i.e., the number of actions can be given as a range). The tool also relies on a tree structure, adopted as an index for a quick retrieval from the available event log. Our approach is highly innovative with respect to the existing literature panorama, since it is the first work that exploits case-based retrieval techniques in the operational support context; moreover, the possibility of retrieving traces by querying complex patterns and the indexing strategy are major departures also with respect to other existing trace retrieval tools proposed in the case based reasoning area. Thanks to its characteristics and methodological solutions, the tool implements operational support tasks in a flexible and efficient way, as demonstrated by our experimental results. [ABSTRACT FROM AUTHOR]

Abstract: Data stream mining is an emerging research topic in the data mining field. Finding frequent itemsets is one of the most important tasks in data stream mining with wide applications like online e-business and web click-stream analysis. However, two main problems existed in relevant studies: (1) The utilities (e.g., importance or profits) of items are not considered. Actual utilities of patterns cannot be reflected in frequent itemsets. (2) Existing utility mining methods produce too many patterns and this makes it difficult for the users to filter useful patterns among the huge set of patterns. In view of this, in this paper we propose a novel framework, named GUIDE (Generation of maximal high Utility Itemsets from Data strEams), to find maximal high utility itemsets from data streams with different models, i.e., landmark, sliding window and time fading models. The proposed structure, named MUI-Tree (Maximal high Utility Itemset Tree), maintains essential information for the mining processes and the proposed strategies further facilitates the performance of GUIDE. Main contributions of this paper are as follows: (1) To the best of our knowledge, this is the first work on mining the compact form of high utility patterns from data streams; (2) GUIDE is an effective one-pass framework which meets the requirements of data stream mining; (3) GUIDE generates novel patterns which are not only high utility but also maximal, which provide compact and insightful hidden information in the data streams. Experimental results show that our approach outperforms the state-of-the-art algorithms under various conditions in data stream environments on different models. [Copyright &y& Elsevier]

Abstract: An adaptation phase is crucial for a good and reasonable case-based design (CBD) process, which is responsible for finding a solution to solve a new problem in the principle of k-nearest neighbors (k-NN). Statistical adaptation method is a classical method for feature-based case adaptation (FCA) because of its domain-independent and easily to be implemented, but with low adaptation accuracy. Therefore, this paper presents a new adaptation method for solution feature values of retrieved cases by introducing the adaptability value to improve the adaptation performance, called as adaptability-based FCA (AFCA). Unlike the classical statistical FCA method (SFCA) based on similarity or distance value, AFCA is performed in terms of the adaptability of old solution feature calculated by the adaptability measurement (AM) mechanism. A new AM method is studied as well in this paper, where the adaptability value for each solution feature is computed by utilizing the decision tree technique and similarity value, and the similarity is derived from the multi-algorithm-oriented hybrid SM strategy. Furthermore, to validate the feasibility and superiority of AFCA, the proposed method was applied to the power transformer design and was compared with the classical SFCAs. Empirical comparison results indicated that AFCA achieves the better adaptation performance under k-NN than other SFCAs on the basis of the adaptation accuracy. [Copyright &y& Elsevier]

Abstract: This paper developed an optimized mathematical model to address the modification of spare parts. In addition, it can evaluate the execution of modified product plans; it also evaluates the suppliers and distributes spare parts supplied by the suppliers. In this mathematical model, analytic hierarchy process (AHP) was proposed for the formulation of factor weights. In addition, an improved particle swarm optimization (PSO) algorithm was also developed for solving the mathematical model; with the introduction of memory and inhibition mechanisms, it could eliminate worse results and excessive searches. Finally, this paper combines the improved mechanisms and four PSO speed-updating criterions. The case study shows that, this improved PSO has good solving capabilities. [Copyright &y& Elsevier]

Abstract: This paper considers earliness/tardiness (ET) scheduling problem on a parallel machine environment with common due-date under the effects of time-dependent learning and linear and nonlinear deterioration. In this paper, the effects of learning and deterioration are considered simultaneously. By the effects of learning and deterioration, we mean that the processing time of a job is defined by increasing function of its execution start time and position in the sequence. This study shows that optimal solution for ET scheduling problem under effects of learning and deterioration is V-shape schedule under certain agreeable conditions. Furthermore, we present a mathematical model for the problem under study and an algorithm for solving large test problems. The algorithm can solve problems of 1000 jobs and four machines within 3s on average. [Copyright &y& Elsevier]

Waste sorting is an imperative and significant issue in China, of which sorted-waste collection and transportation are indispensable parts. Despite its vital yet practical significance, few studies research mathematical models or algorithms of waste collection and transportation from the perspective of waste sorting. To address this issue, we extend a novel transportation model for the waste management system, namely, capacitated location routing problem with queuing time (CLRPQT) and design a cross-entropy and simulated-annealing based hyper-heuristic algorithm (CE-SAHH) for it. The main idea of this paper is three-fold: (1) As a particular property of this problem, source nodes cannot but need to be served by more than one vehicle that causes queuing time between a heterogeneous fleet of vehicles, which is novel in terms of the proposed model; (2) For the methodological contribution, a character encoding scheme, new decoding procedure, and local search strategy are designed embedded in the proposed method; (3) An integration of simulated annealing strategy and the cross-entropy-based hyper-heuristic algorithm is developed to overcome the combinatorial optimization problem with a more complex solution of this study. Finally, the results and analysis of three numeric experiments on benchmark datasets, new instances of CLRPQT, and simulation data in Shanghai, China, verify the effectiveness and universality of the proposed model and method. • A novel model with interactional routes for sorted-waste transportation is proposed. • A cross-entropy-based hyper-heuristic algorithm is proposed and performs well. • The novel model and method based on sorted waste enrich the waste management system. • Real-life data is used to verify the method and some managerial insights are drawn. [ABSTRACT FROM AUTHOR]

In an inter-organizational setting the manual construction of process models is challenging because the different people involved have to put together their partial knowledge about the overall process. Process mining, an automated technique to discover and analyze process models, can facilitate the construction of inter-organizational process models. This paper presents a technique to merge the input data of the different partners of an inter-organizational process in order to serve as input for process mining algorithms. The technique consists of a method for configuring and executing the merge and an algorithm that searches for links between the data of the different partners and that suggests rules to the user on how to merge the data. Tool support is provided in the open source process mining framework ProM. The method and the algorithm are tested using two artificial and three real life datasets that confirm their effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

• Proposing an improved group teaching optimization algorithm for UAV route planning. • A mathematical model of UAV route planning with various obstacles is established. • Introducing a segment-based fitness strategy to deal with the constraints. • Performance of the method is assessed on 3 different UAV flight environment models. • The proposed algorithm is very competitive and superior to the compared algorithms. The complexity and diversity of the flight environment pose great challenges to unmanned aerial vehicle route planning, which demands feasible flight strategies and efficient route planning algorithms. To address the issue, this paper constructs a 3-D flight environment model with multiple obstacles, and designs a novel diversified group teaching optimization algorithm for the generation of flight routes of unmanned aerial vehicles. In the environment model, a variety of obstacles are taken into consideration to make the flying scenarios more realistic, including mountain, cuboid, cylinder and triangular prism, and corresponding strategies are presented for unmanned aerial vehicles to safely avoid these obstacles. In the proposed algorithm, three novel teaching methods are introduced to balance the exploitation and exploration phases. Besides, a novel constrained optimization strategy is adopted, in which constraints are incrementally added to the fitness function to avoid the premature phenomenon in the initial iteration stage of algorithm. The experimental results show that compared with several state-of-the-art optimization algorithms, the proposed algorithm is significantly superior and can always generate the optimal flight route in complicated environments. [ABSTRACT FROM AUTHOR]

Abstract: Although several approaches have been proposed to specify multi-agent commitment-based protocols that capture flexible and rich interactions among autonomous and heterogeneous agents, very few of them synthesize their formal specification and automatic verification in an integrated framework. In this paper, we present a new logic-based language to specify commitment-based protocols, which is derived from ACTL∗c , a logic extending CTL∗ with modalities to represent and reason about social commitments and their actions. We present a reduction technique that formally transforms the problem of model checking ACTL∗c to the problem of model checking GCTL∗ (an extension of CTL∗ with action formulae). We prove that the reduction technique is sound and we fully implement it on top of the CWB-NC model checker to automatically verify the NetBill protocol, a motivated and specified example in the proposed specification language. We also apply the proposed technique to check the compliance of another protocol: the Contract Net protocol with given properties and report and discuss the obtained results. We finally develop a new symbolic algorithm to perform model checking dedicated to the proposed logic. [Copyright &y& Elsevier]

Abstract: In general, times series forecasting is considered as a highly complex problem, which is particularly true for financial time series. In this paper, a fuzzy model evolved through a bio-inspired algorithm is proposed to produce accurate models for the prediction of these time series. The performance of this model is compared to that of a group of state-of-the-art statistical models. A thorough experimental study is designed and carry out in order to assess the merits of the proposal. The experimental results allow us to state that our proposal forecasts consistently outperform the other considered methods. [Copyright &y& Elsevier]

Abstract: The paper studies computation models for tasks performed by autonomous mobile robots. Such tasks can be accomplished by reactive control algorithms. Reactive control systems can be described using different models of computation which have as distinguishing feature the abstraction level of time. Thus, three computation models are defined: the untimed model, the synchronous model and the timed model. It is shown that the clocked-synchronous model of computation is more appropriate for describing the controller for a parallel parking task. [Copyright &y& Elsevier]

Abstract: Understanding how the various factors in a supply chain contribute to the overall performance of its operation has become an important topic in management science research nowadays. In this paper, we propose and apply a two-stage methodology to an industrial survey data set to investigate relations among the key factors in a supply chain model. Precisely, we use the PC-algorithm to discover the connectivity relation among the factors of interest in the supply chain model. Critical factors in the model are then identified, and we then utilize the neural network to quantify the relative importance of some of the factors in predicting the critical factors. An advantage of our proposed method is that it frees up the researcher from making subjective decisions in his or her analysis, for example, from the needs of specifying plausible initial path models required in a structural equation modeling analysis (which is usually used in business and management research) and of selecting factors for the subsequent predictive modeling. We envision that the analysis results can aid a decision maker in optimizing the system performance by suggesting to the decision maker which ones of the factors are the important ones that he or she should devote more resources and efforts on. [Copyright &y& Elsevier]

Abstract: This paper proposes a new procedure and an improved model to mine association rules of customer values. The market of online shopping industry in Taiwan is the research area. Research method adopts Ward’s method to partition online shopping market into three markets. Customer values are refined from an improved RFMDR model (based on RFM/RFMD model). Supervised Apriori algorithm is employed with customer values to create association rules. These effective rules are suggested to apply on a customized marketing function of a CRM system for enhancing their customer values to be higher grades. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a new neuron model (geometric mean neuron model) with an aggregation function based on geometric mean of all inputs. Performance of the geometric mean neuron model was evaluated using various learning algorithms like the back-propagation and resilient propagation on various real life data sets. Comparison of the performance of this model was made with the performance of multilayer perceptron. It has been shown that the geometric mean based aggregation function with resilient propagation (RPROP) performs the best both in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

Abstract: Cross-docking is considered as an efficient method to control the inventory flow, which is essential in supply chain management. In this paper, we consider a model that integrates cross-docking into the vehicle routing problem. In the model, a set of identical vehicles are used to transport goods from supplies to retailers through a cross-dock and the whole process must be completed in the planning horizon. Each supplier and retailer can be visited only once and the total quantity of goods in a vehicle must be less than its capacity. The objective of the problem is to determine the number of vehicles and a set of vehicle schedules with a minimum sum of operational cost and transportation cost. A new tabu search (TS) algorithm is proposed to obtain a good feasible solution for the problem. Through extensive computational experiments, it is shown that the proposed TS algorithm can achieve better performance than an existing TS algorithm while using much less computation time. The average improvements are as high as 10–36% for different size of problems. [Copyright &y& Elsevier]

Abstract: Identification of Hammerstein plants finds extensive applications in stability analysis and control design. For identification of such complex plants, the recent trend of research is to employ nonlinear network and to train their weights by evolutionary computing tools. In recent years the area of Artificial Immune System (AIS) has drawn attention of many researchers due to its broad applicability to different fields. In this paper by combining the principles of AIS and PSO, we propose two new but simple hybrid algorithms called Clonal PSO (CPSO) and Immunized PSO (IPSO) which involve less complexity and offers better identification performance. Identification of few benchmark Hammerstein models is carried out through simulation study and the results obtained are compared with those obtained by standard PSO, Clonal and GA based methods. Various simulation results demonstrate that IPSO algorithm offers best identification performance compared to the other algorithms. Out of the two algorithms proposed, the CPSO is computationally simpler but offers identification performance nearly similar to its PSO counterpart. [Copyright &y& Elsevier]

Abstract: Aiming at the multi-disease diagnosis, a self-organizing map (SOM) is developed. In this paper the tomato disease features are extracted and a mapping relationship between the diseases and the features is created. The inaccurate clustering of traditional SOM algorithm is analyzed. According to the analysis, Euclidean distance is taken as the main discrimination, and the adjacent-searching algorithm is optimized. Using the optimized algorithm, the cluster results of input samples are obtained, features of diseases are mapped, and a multi-disease diagnosis model is developed. The proposed SOM-based model has two layers. The feature array of diseases can be accurately and rapidly sorted and clustered using this model. This model can achieve an accurate diagnosis of multi-diseases. The simulation results show that the proposed model performs well and the proposed multi-disease diagnosis is effective. [Copyright &y& Elsevier]

Abstract: This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases; each case with 24 numerical attributes; based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance; where it may be used efficiently, and quickly in automatic processing of credit applications. [Copyright &y& Elsevier]

Abstract: This study develops an improved fuzzy time series method via adjustment of the latest value factor and previous error patterns. There are many fuzzy extended applications in the literature, and the fuzzy time series is one successful implementation of fuzzy logical modelling. Fuzzy time series have been studied for over a decade, and many researchers have proposed to remove some of the drawbacks of the initial fuzzy time series algorithm. In this paper, fuzzy integrated logical forecasting (FILF) and extended FILF (E-FILF) algorithms are suggested for short term forecasting purposes. Empirical studies are performed over the Baltic Dry Index (BDI), and indicate the superiority of the proposed approach compared to conventional benchmark methods. [Copyright &y& Elsevier]

Abstract: The process of combining multiple computational intelligence techniques to build a single hybrid model has become increasingly popular. As reported in the literature, the performance indices of these hybrid models have proved to be better than the individual components when used alone. Hybrid models are extremely useful for reservoir characterization in petroleum engineering, which requires high-accuracy predictions for efficient exploration and management of oil and gas resources. In this paper, we have utilized the capabilities of data mining and computational intelligence in the prediction of porosity and permeability, two important petroleum reservoir characteristics, based on the hybridization of Fuzzy Logic, Support Vector Machines, and Functional Networks, using several real-life well-logs. Two hybrid models have been built. In both, Functional Networks were used to select the best of the predictor variables for training directly from input data by using its functional approximation capability with least square fitting algorithm. In the first model (FFS), the selected predictor variables were passed to Type-2 Fuzzy Logic System to handle uncertainties and extract inference rules, while Support Vector Machines made the final predictions. In the second, the selected predictor variables were passed to Support Vector Machines for training by transforming them to a higher dimensional space, and then to Type-2 Fuzzy Logic to handle uncertainties, extract inference rules and make final predictions. The simulation results show that the hybrid models perform better than the individual techniques when used alone for the same datasets with their higher correlation coefficients. In terms of execution time, the hybrid models took less time for both training and testing than the Type-2 Fuzzy Logic, but more time than Functional Networks and Support Vector Machines. This could be the price for having a better and more robust model. The hybrid models also performed better than a combination of two of the individual components, Type-2 Fuzzy Logic and Support Vector Machines, in terms of higher correlation coefficients as well as lower execution times. This is due to the effective role of Functional Networks, as a best-variable selector in the hybrid models. [Copyright &y& Elsevier]

Abstract: The development of efficient on-line data processing and decision support algorithms is one of future trends of expert systems for machine condition monitoring research. This paper contributes to a lean model for machine performance assessment by combining an efficient signal processing algorithm, an effective feature selection criterion, and an intelligent assessment method. In the proposed model, firstly, a second generation wavelet packet transform is used to project raw signals into the wavelet domain; secondly, the Fisher criterion is applied to reduce redundant dimensions; eventually, a fuzzy c-means clustering method is used to assess and classify the performance of mechanical systems. The vibration signals from a rolling element bearing experiment has been used to verify both efficiency and effectiveness of the lean model. Compared with conventional methods, the lean model can reduce the time consumption of feature extraction by 49.7% and storage space or data transfer load related to the feature dimensionality by 97.7%, which indicates a great improvement in efficiency. [Copyright &y& Elsevier]

Abstract: Multicast routing is regarded as a critical component in networks especially the real-time applications become increasingly popular in recent years. This paper proposes a novel fast multi-objective evolutionary algorithm called MOEAQ for solving multicast routing problem (MRP) in MANET. The strengths and limitations of the well-known multicast model are analyzed firstly in this work. Specifically, the “Greedy” and “family competition” approach are integrated into MOEAQ to speed up the convergence and to maintain the diversity of population. The theoretical validations for the proposed method are presented to show its efficiency. After that, a CBT-based improved protocol is then proposed to simplify the MRP, and finally, the performance of MANET scaled from 20 to 200 nodes with different types of service is evaluated by OPNET, experimental results show that the proposed method is capable of achieving faster convergence and more preferable for multicast routing in MANET compared with other GA-based protocol well-known in the literature. [Copyright &y& Elsevier]

Abstract: Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. Due to the inherent ambiguity of image-label mapping and the scarcity of training examples, the annotation task has become a challenge to systematically develop robust annotation models with better performance. From the perspective of machine learning, the annotation task fits both multi-instance and multi-label learning framework due to the fact that an image is usually described by multiple semantic labels (keywords) and these labels are often highly related to respective regions rather than the entire image. In this paper, we propose an improved Transductive Multi-Instance Multi-Label (TMIML) learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. The experiments over the well known Corel 5000 data set demonstrate that the proposed method is beneficial in the image annotation task and outperforms most existing image annotation algorithms. [Copyright &y& Elsevier]

Abstract: Pyrometallurgy is often used in the industrial process for treating gold-bearing slime. Slag compositions have remarkable influences on the recovery of gold and the gold content in slag. A method for determining optimum flux compounding with neural networks is studied in this paper, and the neural network model for estimating the gold contents with different slag compositions is presented. On the basis of the neural network model, an algorithm for searching the optimum flux compounding in the gold-slime smelting process is proposed, and the optimum flux compositions are obtained accordingly. [Copyright &y& Elsevier]

This paper studies a new kind of dynamic multi-stage facility layout problem under dynamic business environment, in which new machines may be added into, or old machines may be removed from the plant. We define this problem first on the basis of unequal area machines and continual presentation of layouts. Compared with nodes and arcs of the flow chart, we convert this problem into a shortest path problem by studying its cost function and machine adding/removing heuristic rules, and the corresponding mathematical model for this problem is established. An auction algorithm is proposed here to solve the shortest path problem. Finally, a shortest path based simulated annealing algorithm is presented to solve the optimization problem. Parameters of the SP based SA algorithm are discussed to improve the performance of the algorithm. Some cases are used to verify the proposed algorithm. [Copyright &y& Elsevier]

In this paper we propose a hybrid algorithm to optimize the structure of TSK type fuzzy model using backpropagation (BP) learning algorithm and non-dominated sorting genetic algorithm (NSGA-II). In a first step, BP algorithm is used to optimize the parameters of the model (parameters of membership functions and fuzzy rules). NSGA-II is used in a second phase, to optimize the number of fuzzy rules and to fine tune the parameters. A well known benchmark is used to evaluate performances of the proposed modelling approach, and compare it with other modelling approaches. [Copyright &y& Elsevier]

This paper presents a genetic programming (GP) approach to extract symbolic rules from data sets with continuous-valued classes, called GPMCC. The GPMCC makes use of a genetic algorithm (GA) to evolve multi-variate non-linear models [Potgieter, G., & Engelbrecht, A. (2007). Genetic algorithms for the structural optimisation of learned polynomial expressions. Applied Mathematics and Computation] at the terminal nodes of the GP. Several mechanisms have been developed to optimise the GP, including a fragment pool of candidate non-linear models, k-means clustering of the training data to facilitate the use of stratified sampling methods, and specialized mutation and crossover operators to evolve structurally optimal and accurate models. It is shown that the GPMCC is insensitive to control parameter values. Experimental results show that the accuracy of the GPMCC is comparable to that of NeuroLinear and Cubist, while producing significantly less rules with less complex antecedents. [Copyright &y& Elsevier]

• A multi-strategy boosted Sine Cosine Algorithm named SGLSCA is proposed. • Salp Swarm Algorithm and Grey Wolf Optimizer are introduced into the basic SCA. • Levy flight strategy is also embedded into SCA. • The superior performance of SGLSCA is confirmed over various advanced algorithms. • SGLSCA is tested over benchmark functions and engineering optimization. The Sine Cosine Algorithm (SCA) is a popular population-based optimization method, which has shown competitive results compared to other algorithms, and it has been utilized to tackle optimization cases in various domains. Despite popularity, the initial SCA suffers from minimalistic originality, mediocre performance, and shallow mathematical model. In fact, there is undoubtedly room for improvement in the structure of original SCA because it may face problems of lazy convergence and inertia to local optima. To relieve these drawbacks, this paper develops a new multi-core SCA named SGLSCA, which is combined with three strategies based on the patterns of Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and Levy flight (LF). Based on introducing the updating strategy of SSA and GWO, it is proposed to strengthen the exploration aptitude of the conventional SCA. Also, the SSA updating strategy aims to further update the population based on the best solution of SCA, while the GWO updating plan helps using the top three solutions of SCA. Also, the LF strategy is embedded to achieve the random individual walk during the history of the exploration and further augment the competence of SCA to avoid local optimal solutions. To substantiate the structure and results of the proposed multi-core SCA, which is entitled SGLSCA, it is compared against nine state-of-art algorithms, six improved SCA variants, and nine successful advanced algorithms on 34 benchmark functions selected from 23 benchmark functions and 30 IEEE CEC 2014 benchmark problems. Additionally, three practical, real-world engineering problems are considered. The final experimental results expose that the multi-core SGLSCA outperforms other optimizers including LSHADE-cnEpSin and LSHADE methods in terms of both convergence and optimality of solutions. A public repository will support this research at http://aliasgharheidari.com for future works and possible guidance. [ABSTRACT FROM AUTHOR]

• Decision making among candidates with dynamic continuous information. • Challenge: determining the right time to make a decision which maximizes the gain. • We present optimal and approximation solutions. • The quality of the decision in the approximation is near-optimal but faster. Decision making is the ability to select the best alternative from a set of candidates based on their respective values. When the value depends on uncertain future events, this task becomes more complicated. The question is then whether to wait for more information before making a decision or to stop and make a decision based on uncertain information. This has been addressed in previous work, when the information (events) could be represented as discrete random variables. However, there are real world domains where this assumption is incorrect. Thus, in this paper, we propose a novel framework and algorithms designed to cope with the challenge posed when future events are represented as continuous random variables. More specifically, we define a mathematical representation to model the utility functions of the candidates and introduce optimal and approximate algorithms to compute the best time to stop, and make a decision in order to optimize the utility. We evaluate our model and algorithms theoretically and empirically, and measure their performance in terms of the gain they achieve and their runtime. Our experiment demonstrates that there is no significant difference between the quality of the decision reached by the two algorithms, while the runtime of the optimal algorithm is much higher than that of the approximate algorithm. [ABSTRACT FROM AUTHOR]