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The expectation function of fuzzy variable is an important and widely used criterion in fuzzy optimization, and sound properties on the expectation function may help in model analysis and solution algorithm design for the fuzzy optimization problems. The present paper deals with some analytical properties of credibilistic expectation functions of fuzzy variables that lie in three aspects. First, some continuity theorems on the continuity and semicontinuity conditions are proved for the expectation functions. Second, a differentiation formula of the expectation function is derived which tells that, under certain conditions, the derivative of the fuzzy expectation function with respect to the parameter equals the expectation of the derivative of the fuzzy function with respect to the parameter. Finally, a law of large numbers for fuzzy variable sequences is obtained leveraging on the Chebyshev Inequality of fuzzy variables. Some examples are provided to verify the results obtained.

Clustering is commonly exploited in engineering, management, and science fields with the objective of revealing structure in pattern data sets. In this article, through clustering we construct meaningful collections of information granules (clusters). Although the underlying goal is obvious, its realization is fully challenging. Given their nature, clustering is a well-known NP-complete problem. The existing algorithms commonly produce some suboptimal solutions. As a vehicle of pattern clustering, we discuss in this article how to use a DNA-based algorithm. We also discuss the details of encoding being used here with statistical methods combined with the DNA-based algorithm for pattern clustering.

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

A novel method of interpretive structural modeling (ISM) using a DNA-based algorithm is proposed in this paper. ISM is commonly used when the current technology and its application to business administration, industrial and systems engineering, organizational behavior, etc., concern complicated or problematic issues, or situations among an element set of the given problem context for making decisions. When structuring a problem with a large number of elements in an ISM process, the crossings among elements should be minimized. This computationally complex minimization is NP-complete. The proposed algorithm describes how to calculate complex relations among elements to create a hierarchically restructured digraph. This paper also presents a new approach for applying a biological method to ISM to measure the efficiency of the algorithm in calculating a large number of elements for decision making.

The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to "reward" or "punish" the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems. [ABSTRACT FROM AUTHOR]

Skin cancers are increasing at an alarming rate, and detection in the early stages is essential for advanced treatment. The current segmentation methods have limited labeling ability to the ground truth images due to the numerous noisy expert annotations present in the datasets. The precise boundary segmentation is essential to correctly locate and diagnose the various skin lesions. In this work, the lesion segmentation method is proposed as a Markov decision process. It is solved by training an agent to segment the region using a deep reinforcement-learning algorithm. Our method is similar to the delineation of a region of interest by the physicians. The agent follows a set of serial actions for the region delineation, and the action space is defined as a set of continuous action parameters. The segmentation model learns in continuous action space using the deep deterministic policy gradient algorithm. The proposed method enables continuous improvement in performance as we proceed from coarse segmentation results to finer results. Finally, our proposed model is evaluated on the International Skin Imaging Collaboration (ISIC) 2017 image dataset, Human against Machine (HAM10000), and PH2 dataset. On the ISIC 2017 dataset, the algorithm achieves an accuracy of 96.33% for the naevus cases, 95.39% for the melanoma cases, and 94.27% for the seborrheic keratosis cases. The other metrics are evaluated on these datasets and rank higher when compared with the current state-of-the-art lesion segmentation algorithms. [ABSTRACT FROM AUTHOR]

The high dimensional, multimodal, and discontinuous well placement optimization is one of the main difficult factors in the development process of conventional as well as shale gas reservoir, and to optimize this problem, metaheuristic techniques still suffer from premature convergence. Hence, to tackle this problem, this study aims at introducing a dimension-wise diversity analysis for well placement optimization. Moreover, in this article, quantum computational techniques are proposed to tackle the well placement optimization problem. Diversity analysis reveals that dynamic exploration and exploitation strategy is required for each reservoir. In case studies, the results of the proposed approach outperformed all the state-of-the-art algorithms and provided a better solution than other algorithms with higher convergence rate, efficiency, and effectiveness. Furthermore, statistical analysis shows that there is no statistical difference between the performance of Quantum bat algorithm and Quantum Particle swarm optimization algorithm. Hence, this quantum adaptation is the main factor that enhances the results of the optimization algorithm and the approach can be applied to locate wells in conventional and shale gas reservoir. [ABSTRACT FROM AUTHOR]

• A novel NN-based hybrid method for solving quadratic-BLPPs is presented. • Proposed algorithm combines GA (handles upper-level problem) and DRNN (for lower-level decision problem). • GA solves the upper-level decision problem by choosing candidate solutions and passing them to the lower-level. • Parameterized dual-NN is used in the lower-level problem to determine the optimal solutions. • This combination offers many benefits like parallel computing, faster convergence to global optimum, etc. The current paper presents a neural network-based hybrid strategy that combines a Genetic Algorithm (GA) and a Dual Recurrent Neural Network (DRNN) for efficiently and accurately solving the quadratic -Bi-level Programming Problem (BLPP). In this model, the GA is used to handle the upper-level decision problem by choosing desirable solution candidates and passing them to the lower-level problem. Subsequently, in the lower-level, the parameterized-DRNN is used to determine possible optimal solutions. This combination offers several benefits such as being a parallel computing structure, the RNN offers faster convergence to the optimum for the lower-level decision problem and it also helps to quickly and accurately determining the global optimal. Moreover, the GA can quickly reach the global optima and can search without becoming stuck to the local optimal. Additionally, by choosing desirable initialization of parameters, the proposed algorithm reaches the optimum with higher accuracy. Apart from that, there are still a few utilizations of hybrid NN-based methods for solving BLPPs. Hence, we believe the proposed algorithm will contribute to solving quadratic -BLPPs involved in various engineering, management, and finance applications. The accuracy and efficiency of the proposed method have been found better than the existing and widely used approaches, while doing experimental verification using four well-known examples used in prior works. [ABSTRACT FROM AUTHOR]

In this research, a quantum computing idea based bat algorithm (QBA) is proposed to solve many-objective combined economic emission dispatch (CEED) problem. Here, CEED is represented using cubic criterion function to reduce the nonlinearities of the system. Along with economic load dispatch, emissions of SO2, NOx, and CO2 are considered as separate three objectives, thus making it a four-objective (many-objective) optimization problem. A unit-wise price penalty factor is considered here to convert all the objectives into a single objective in order to compare the final results with other previously used methods like Lagrangian relaxation (LR), particle swarm optimization, and simulated annealing. QBA is applied in six-unit power generation system for four different loads. The obtained results show QBA successfully solve many-objective CEED problem with greater superiority than other methods found in the literature in terms of quality results, robustness, and computational performance. In the end of this paper, a detailed future research direction is provided based on the simulation results and its analysis. The outcome of this research demonstrates that the inclusion of quantum computing idea in metaheuristic technique provides a useful and reliable tool for solving such many-objective optimization problem. [ABSTRACT FROM AUTHOR]

Methods of qualitative analysis, such as qualitative classification, have gained importance as an essential complement of existing quantitative analysis in numerous fields. Only a few models have been developed to deal with qualitative inputs in the form of type-2 fuzzy(T2F) sets properly, given that traditional defuzzification method like the Karnik-Mendel algorithm performs dimensionality reduction at the cost of loss of information. To improve the situation, we define the expected value and variance of T2F set in this paper. By using a combination of them, we transfer the vertical three-dimensional uncertainty of T2F set to horizontal range uncertainty without much distortion of information. Additionally, current classification models are unsuitable to the partial classification problem if an output is not fully assigned to a single class. We build a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM) combined with type-2 fuzzy expected regression (FER) to solve the partial classification problem as mentioned. This classifier (i.e. FER-FSVM) makes it possible to achieve the discrimination of output while characterizing membership for each class in terms of multidimensional qualitative inputs (attributes) in the form of T2F sets. FER-FSVM also can self-learn the data structure and shift between FER or FSVM for classification automatically, thus largely improving the efficiency of the classification process. The new model is almost 7 times more efficient than FSVM, as shown by our empirical experiments. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Indonesia has a discrepancy between the realized amount and the planned amount of gasoline consumption. This discrepancy has burdened the national budget in Indonesia because this unplanned increase in the imported amount of gasoline has made the government pay more to the importers. The objectives of this research are to develop a robust and accurate model to forecast future gasoline consumption and to provide an attractive alternative model for gasoline consumption forecasting by applying a metaheuristic approach. We apply the harmony search (HS) algorithm for developing a model of gasoline consumption in Indonesia using general socioeconomic variables that can be easily retrieved from public data. The variables used are the gross domestic product (GDP), population, and the total numbers of passenger cars and motorcycles. The HS algolithm selects the optimal weight factors within the proposed exponential model. The results show that the proposed exponential HS algorithm-based model outperforms the conventional nonlinear regression method and particle swarm optimization (PSO)-based model in terms of the mean absolute percentage error (MAPE). [ABSTRACT FROM AUTHOR]

Abstract: A new class of fuzzy stochastic optimization models—two-stage fuzzy stochastic programming with Value-at-Risk (FSP-VaR) criteria is built in this paper. Some properties of the two-stage FSP-VaR, such as value of perfect information (VPI), value of fuzzy random solution (VFRS), and bounds of the fuzzy random solution, are discussed. An Approximation Algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence of the approximation algorithm is proved. To solve the two-stage FSP-VaR, a hybrid mutation-neighborhood-based particle swarm optimization (MN-PSO) which comprises the Approximation Algorithm is proposed to search for the approximate optimal solution. Furthermore, a neural network-based acceleration method is discussed. A numerical experiment illustrates the effectiveness of the proposed hybrid MN-PSO algorithm. The comparison shows that the hybrid MN-PSO exhibits better performance than the one when using other approaches such as hybrid PSO and GA. [Copyright &y& Elsevier]

Classical job shop scheduling includes an intractable manufacturing scheduling problem. The problem is how to minimise the maximum manufacturing completion time in high variety and low volume requirements. Further, we have to consider work centre sequences for how all the given jobs are sequenced by work centre orders, meaning that it is an NP-hard problem. A number of different types of methods and algorithms have been proposed for solving this issue. In this article, we focused on molecular computational methods and biological techniques to propose a new algorithm. Our algorithm can be used to search all the feasible manufacturing schedules, and isolate the schedule with the truly minimised maximum manufacturing completion time. [ABSTRACT FROM AUTHOR]

Wellbore trajectory design is a nonlinear and constrained mathematical optimization problem that is used to build a cost-efficient, safe, and easily reachable trajectory. True measured depth (TMD), torque, and strain energy are used as objective functions to evaluate the wellbore trajectory design in this work. The minimum values of these objective functions enable a trajectory to be drilled with minimum drilling cost and maximum safety. A lot of modifications to the original metaheuristic methods were made during previous applications, which mostly improve the exploration capability of original algorithms keeping exploitation capability unaddressed. To address this issue a new hybridization of cellular automata (CA) technique with grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms is proposed in this paper which solves these three optimization objectives of drilling through 17 tuning variables. The improvements of the original PSO Algorithm are proposed by updating its exploitation phase with the incorporation of the GWO algorithm and the exploration phase by using a cellular automaton. During the optimization, the operational constraints of a wellbore such as true vertical depth and casings along with the bounds of tuning variables were utilized. Better performances were observed in cases of Pareto optimal front, search capabilities, and diversity of solutions by comparing the proposed method with other standard methods like MOCPSO, MOGWO, and MOPSO. Several parametric tests (IGD, SP, MS) were done to investigate the effect of proposed hybridization. The mean value of IGD was 0.0208 by the proposed method, which is 46.8% better than MOCPSO, 49.78% than MOPSO, and 60.80% better than the MOGWO. The proposed optimization method also had the minimum spacing metric and maximum spread. • True measured depth, torque, and strain energy are utilized to evaluate a wellbore trajectory design. • GWO algorithm is hybridized with PSO and cellular automata. • The effects of hybridization have been investigated by comparing it with MOCPSO, MOPSO, MOGWO. • The sensitivity has been investigated by the Spearman correlation coefficient statistical test. [ABSTRACT FROM AUTHOR]