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In this paper, self-design automated simulation and artificial neural network (ANN) model were developed to analyze and estimate the resistance factor in rectangle cross-section microchannels. The main purpose is to obtain a universal solution method through numerical simulation which can solve the resistance factor problem for invariant cross-section microchannels. Through Python language, the automatic coalescent of preprocessing Gambit, computing software CFD and post-processing Tecplot make the simulation framework realize the automatic acquisition of microchannel resistance factor samples. Then, 100 simulation samples with different aspect ratios for Reynolds numbers ranging from 50 to 500 were obtained. After validation, the width and height of microchannels were applied as input data set of the ANN model, and the resistance factor was determined as the target data. In order to improve BP algorithm for training ANN, a new swarm evolution algorithm was realized by combining the strong point of gradient descent method, genetic algorithm and particle swarm optimization, which is called particle swarm evolution algorithm. Finally, the result of resistance factor model was established and verified by several existing measurement value of pressure drop from remarkable experimental. [ABSTRACT FROM AUTHOR]

Given an undirected, connected, edge-weighted graph G and a positive integer d, the degree-constrained minimum spanning tree (dc-MST) problem aims to find a minimum spanning tree T on G subject to the constraint that each vertex is either a leaf vertex or else has degree at most d in T, where d is a given positive integer. The dc-MST is NP -hard problem for d ≥ 2 and finds several real-world applications. This paper proposes a hybrid approach (H SSGA) combining a steady-state genetic algorithm and local search strategies for the this problem. An additional step (based on perturbation strategy at a regular interval of time) in the replacement strategy is applied in order to maintain diversity in the population throughout the search process. On a set of available 107 benchmark instances, computational results show the superiority of our proposed H SSGA in comparison with the state-of-the-art metaheuristic techniques. [ABSTRACT FROM AUTHOR]

The paper presents an estimation of the optimum cost of an isolated foundation following the safety and serviceability guidelines of Indian Standard (IS) 456:2000. Two adaptable optimization algorithms are developed for the first time to optimize the cost of any type of isolated footing design. Two optimization methods, i.e., constrained binary-coded genetic algorithm, with static penalty function approach and unified particle swarm optimization are developed in MATLAB compliant for optimal design of any isolated foundations. The objective function formulated is based on the total cost of footing. This includes the cost of concrete, the cost of steel and cost of formwork. The design variables which influence the total cost of footing are plan area and depth of footing and area of flexural reinforcement at moment critical sections. The footing design algorithm is developed according to the biaxial-isolated rectangular footing as per IS codes. The constraints, e.g., dimension of footing, restriction on bending, shear stresses and displacements, are considered in the footing design algorithm which acted as a subroutine to the developed optimization programs. Four different numerical examples have been solved to evaluate the versatility of the developed method. A comparison study has been done to observe the efficacy of both the optimization methods. [ABSTRACT FROM AUTHOR]

Network models are widely used for solving difficult real-world problems. The minimum cost flow problem (MCFP) is one of the fundamental network optimisation problems with many practical applications. The difficulty of MCFP depends heavily on the shape of its cost function. A common approach to tackle MCFPs is to relax the non-convex, mixed-integer, nonlinear programme (MINLP) by introducing linearity or convexity to its cost function as an approximation to the original problem. However, this sort of simplification is often unable to sufficiently capture the characteristics of the original problem. How to handle MCFPs with non-convex and nonlinear cost functions is one of the most challenging issues. Considering that mathematical approaches (or solvers) are often sensitive to the shape of the cost function of non-convex MINLPs, this paper proposes a hybrid genetic algorithm with local search (namely GALS) for solving single-source single-sink nonlinear non-convex MCFPs. Our experimental results demonstrate that GALS offers highly competitive performances as compared to those of the mathematical solvers and a standard genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with hybrid flow shop scheduling problem with unrelated and eligible machines along with fuzzy processing times and fuzzy due dates. The objective is to minimize a linear combination of total completion time and maximum lateness of jobs. A mixed integer mathematical model is presented for the problem. The most challenging parts of hybrid evolutionary algorithms are determination of efficient strategies by which the whole search space is explored to perform local search around the promising search areas. In this study, a clustering-based approach as a data mining tool is introduced to identify the promising search areas. A repetitive clustering with an evolutionary algorithm is simultaneously employed to determine more promising parts of the solution space. Then, the searches in those parts are intensified with a local search. Here, two clustering-based meta-heuristic algorithms are applied to solve the problem, namely particle swarm optimization and genetic algorithm. The parameters are tuned by Taguchi experimental design, and various randomly generated test problems are used to evaluate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Computational fluid dynamic (CFD) simulations present numerous challenges in the domain of artificial intelligence. Computational time, resources and cost that can reach disproportional size before leading a simulation to its fully converged solution are one of the central issues in this domain. In this paper, we propose a novel algorithm that finds optimal parameter settings for the numerical solvers of CFD software. Indeed, this research proposes an alternative approach; rather than going deeper in reducing the mathematical complexity, it suggests taking advantage of the history of previous runs in order to estimate the best parameters for numerical equation resolution. In fact, our approach is bio-inspired and based on a genetic algorithm (GA) and evolutionary strategies enhanced with surrogate functions based on machine-learning meta-models. Our research method was tested on 11 different use cases using various configurations of the GA and algorithms of machine learning such as regression trees extra trees regressors and random forest regressors. Our approach has achieved better runtime performance and higher convergence quality (an improvement varying between 8 and 40%) in all of the test cases when compared to a basic approach which requires manually selecting the parameters. Moreover, our approach outperforms in some cases manual selection of parameters by reaching convergent solutions that couldn't otherwise be achieved manually. [ABSTRACT FROM AUTHOR]

Chinese postman problem is one of the significant combinatorial optimization problems with a wide range of real-world applications. Modelling such real-world applications quite often needs to consider some uncertain factors for which the belief degrees of the experts are essential. Liu (Uncertainty Theory, 2nd edn. Springer, Berlin, 2007) proposed uncertainty theory to model such human beliefs. This paper presents a multi-objective Chinese postman problem under the framework of uncertainty theory. The objectives of the problem are to maximize the total profit earned and to minimize the total travel time of the tour of a postman. Here, we have proposed an expected value model (EVM) for the uncertain multi-objective Chinese postman problem (UMCPP). The deterministic transformation of the corresponding EVM is done by computing the expected value of the uncertain variable using 999-method for which we have proposed an algorithm, 999-expected value model-uncertain multi-objective Chinese postman problem. Subsequently, the model is solved by two classical multi-objective solution techniques, namely global criterion method and fuzzy programming method. Two multi-objective genetic algorithms (MOGAs): nondominated sorting genetic algorithm II and multi-objective cross-generational elitist selection, heterogeneous recombination and cataclysmic mutation are also used to solve the model. A numerical example is presented to illustrate the proposed model. Finally, the performance of MOGAs is compared on six randomly generated instances of UMCPP. [ABSTRACT FROM AUTHOR]

Combined model theory has been widely used to forecast the time series problems that are often nonlinear, nonstationary and irregular. Current forecasting models based on combined models theory could adapt to some time series data and overcome some disadvantages of the single models. However, in previous studies, most forecasting models have just focused on improving the accuracy or stability. Nevertheless, for an effective forecasting model, considering only one criterion or rule (stability or accuracy) is insufficient. In this paper, a novel forecasting system, called non-dominated sorting genetic algorithm III combined system with three objective functions, was proposed and successfully employed to solve the predicament of electricity load forecasting which demands to obtain both high accuracy and strong stability as an example. Both stability and accuracy of our proposed combined system are superior to the model compared which showed in the experiment results. [ABSTRACT FROM AUTHOR]

Up to now, many different methods (e.g., dispatching rules, metaheuristics) have been proposed to solve the job shop scheduling problem (JSSP), but the application of data mining approaches in the literature is limited. In this paper, we propose a data mining-based approach to generate an improved initial population for population-based heuristics/metaheuristics solving the JSSP. First, we apply a combination of 'attribute-oriented induction' and 'association rule mining' techniques to extract the rules behind the optimal or near-optimal schedules of JSSP. Then, a novel method called 'Assignment Procedure' is proposed to heuristically solve the JSSP using the extracted rules. The proposed method is able to generate numerous schedules for a given JSSP instance, and consequently, the generated solutions can be considered as the initial population for population-based solution methods. To evaluate the effectiveness of the proposed method, the two well-known metaheuristics are considered, i.e., genetic algorithm (GA) and particle swarm optimization (PSO). Extensive experiments were carried out on 35 benchmark problem instances, and Friedman test with post hoc Nemenyi test was performed to statistically analyze the results. The results revealed that using the initial populations generated by the proposed approach is very promising compared to the randomly generated population for both GA and PSO. Moreover, the experiments verify the significant amount of FEs that can be saved using the proposed approach and the superiority of the proposed method in comparison with the method of Koonce and Tsai (Comput Ind Eng 38:361–374, 2000) and four well-known dispatching rules in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz's multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. [ABSTRACT FROM AUTHOR]

Abstract  This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach based on the use of wavelet functions to model air pollution time series. One peculiarity of the approach is that of combining the use of wavelets and genetic algorithms to search for the best wavelet parameters. A case study, referring to the modelling of daily averages of SO 2 time series recorded in the industrial area of Syracuse (Italy) is reported in order to compare the performance of a wavelet-based prediction model and a Multi-layer perceptron neural model. The results obtained show that there are no significant differences between the neural and the wavelet approach in terms of model performance and computational effort. There is however an appreciable advantage in using the proposed wavelet-based technique in terms of model readability. [ABSTRACT FROM AUTHOR]

This paper describes the concept of fuzzy regression analysis based on genetic algorithms. It is shown that the performance of fuzzy regression models may be improved and fuzzy modeling technique can be simplified by incorporating genetic algorithms into regression analysis procedure. The effectiveness of the proposed approach is illustrated through simulation of fuzzy linear regression model obtained by other authors and comparison of the results. The paper further demonstrates the applications of the approach to the manufacturing and business problems. [ABSTRACT FROM AUTHOR]

Because most of runoff time series with limited amount of data reveal inherently nonlinear and stochastic characteristics and tend to show chaotic behavior, strategies based on chaotic analysis are popular methods to analyze them from real systems in nonlinear dynamics. Only one kind of predicted method for yearly rainfall-runoff forecasting cannot achieve perfect performance. Thus, a mixture strategy denoted by WT-PSR-GA-NN, which is composed of wavelet transform (WT), phase space reconstruction (PSR), neural network (NN) and genetic algorithm (GA), is presented in this paper. In the WT-PSR-GA-NN framework, the process to deal with time series gathered from Liujiang River runoff data is given as follows: (1) the runoff time series was first decomposed into low-frequency and high-frequency sub-series by wavelet transformation; (2) the two sub-series were separately and independently reconstructed into phase spaces; (3) the transformed time series in the reconstructed phase spaces were modeled by neural network, which is trained by genetic algorithm to avoid trapping into local minima; (4) the predicted results in low-frequency parts were combined with the ones of high-frequency parts, and reconstructed with wavelet inverse transformation, to form the future behavior of the runoff. Experiments show that WT-PSR-GA-NN is effective and its forecasting results are high in accuracy not only for the short-term yearly hydrological time series but also for the long-term one. The comparison results revealed that the overall forecasting performance of WT-PSR-GA-NN proposed by us is superior to other popularity methods for all the test cases. We can conclude that WT-PSR-GA-NN can not only increase the forecasted accuracy, but also its own competitiveness in efficiency, effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Job shop scheduling problem is well-investigated and widely applied in the fields of operational research, system engineering and automatic management. This paper employs uncertain programming to deal with the job shop scheduling problem with uncertain processing time and cost. First, a chance-constrained model is proposed under the framework of uncertainty theory. The model is equivalent to a crisp one by inverse uncertainty distribution method. Furthermore, three heuristic algorithms (genetic algorithm, particle swarm optimization, firefly algorithm) are employed for solving the model. Finally, a numerical example is solved by these three algorithms, and the results are analyzed to show which algorithm is better for solving the established model. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a generic way to represent and manipulate pairwise information about partial orders (representing rankings, preferences, ...) with belief functions. We provide generic and practical tools to make inferences from this pairwise information and illustrate their use on the machine learning problems that are label ranking and multi-label prediction. Our approach differs from most other quantitative approaches handling complete or partial orders, in the sense that partial orders are here considered as primary objects and not as incomplete specifications of ideal but unknown complete orders. [ABSTRACT FROM AUTHOR]

Recently, how to reduce huge energy consumption of data centers has caught wide attention in cloud computing. One effective way is to improve the energy efficiency of servers. To achieve this goal, we propose a new energy-aware multi-job scheduling model based on MapReduce in this paper. In the proposed model, first, the variation of energy consumption with the performance of servers is taken into account; second, since network bandwidth is a relatively limited resource in cloud computing, 100 % data locality is guaranteed; last but not least, considering that task-scheduling strategies depend directly on data placement policies, we formulate the problem as an integer bi-level programming model. It is worth noticing that there are usually tens of thousands of tasks to be scheduled in the cloud, so this is a large-scale optimization problem. In order to solve it efficiently, a local search operator is specifically designed, based on which, a bi-level genetic algorithm is proposed in this paper. Finally, numerical experiments indicate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Owing to the change of our lifestyle, more people suffer from chronic diseases, such as obesity or diabetes. Making improvements in terms of diet and physical activity is helpful for people to reduce the risk of suffering from these chronic diseases. This paper presents a healthy diet assessment mechanism based on fuzzy markup language (FML) for Japanese food. This mechanism is based on the dietary standard, food exchange list (FEL), published by the Japan Diabetes Society, to assess one person's dietary healthy level according to his/her personal profile, his/her daily physical activity, and how balanced he/she consumes food items. We use FML to describe the knowledge base and the rule base of the constructed fuzzy logic system. Additionally, ontology stores the necessary knowledge base and rule base of the adopted fuzzy inference mechanism. The proposed mechanism operates as follows: (i) Dieticians first define the nutrient facts of the collected food items. (ii) The involved subjects record their personal information, eaten food items, and daily physical activity. Through the constructed FML-based personal profile ontology, the estimated energy requirement and the estimated unit requirement for each food group are obtained after inference. (iii) Finally, one-day dietary healthy level is inferred by the proposed fuzzy inference mechanism. From the experimental results, the proposed mechanism is feasible to apply to Japanese dietary assessment. In the future, we will integrate with genetic learning algorithm and collect more subjects' records to further improve the performance. [ABSTRACT FROM AUTHOR]

Imbalanced problems are quite pervasive in many real-world applications. In imbalanced distributions, a class or some classes of data, called minority class(es), is/are under-represented compared to other classes. This skewness in the data underlying distribution causes many difficulties for typical machine learning algorithms. The notion becomes even more complicated when machine learning algorithms are to combat multi-class imbalanced problems. The presented solutions for tackling the issues arising from imbalanced distributions, generally fall into two main categories: data-oriented methods and model-based algorithms. Focusing on the latter, this paper suggests an elegant blend of boosting and over-sampling paradigms, which is called MDOBoost, to bring considerable benefits to the learning ability of multi-class imbalanced data sets. The over-sampling technique introduced and adopted in this paper, Mahalanobis distance-based over-sampling technique (MDO in short), is delicately incorporated into boosting algorithm. In fact, the minority classes are over-sampled via MDO technique in such a way that they almost preserve the original minority class characteristics. MDO, in comparison with the popular method in this field, SMOTE, generates more similar minority class examples to original class samples. Moreover, the broader representation of minority class examples is provided via MDO, and this, in turn, causes the classifier to build larger decision regions. MDOBoost increases the generalization ability of a classifier, since it indicates better results with pruned version of C4.5 classifier; unlike other over-sampling/boosting procedures, which have difficulties with pruned version of C4.5. MDOBoost is applied to real-world multi-class imbalanced benchmarks and its performance is then compared with several data-level and model-based algorithms. The empirical results and theoretical analyses reveal that MDOBoost offers superior advantages compared to popular class decomposition and over-sampling techniques in terms of MAUC, G-mean, and minority class recall. [ABSTRACT FROM AUTHOR]

Product returns have become a significant management issue and an unavoidable cost for a business. This situation made firms to consider the possibility of managing product returns in a more cost-efficient way. In this paper, we develop a mixed integer non-linear programming model of a three-stage logistics network to optimize the number and location of collection/inspection centers and recovery centers as well as collection frequency with the objective of minimizing the total costs which include the reverse logistics shipping costs and fixed costs of opening facilities. We propose two solution algorithms based on genetic algorithm (GA) and simulated annealing (SA). Numerical experiments are used to illustrate the effectiveness and efficiency of the proposed solution approaches and compare the results with the previous works. [ABSTRACT FROM AUTHOR]

Uncertainty in robot dynamic systems is caused by model errors in the dynamic parameters, and accurate identification of the dynamic parameters is essential to improve the control accuracy of the robot. In this paper, a hybrid optimization strategy for modular robot manipulator dynamic model parameter identification is proposed to accurately identify the dynamic parameters of the robot manipulator. Firstly, the robot dynamics model with Coulomb viscous friction is established. Secondly, the cosine adaptive learning and reversal strategies are introduced to improve the genetic algorithm, and the improved genetic optimization algorithm is applied to optimize the excitation trajectories, and all the robot arm joints are commanded to follow the optimized excitation trajectories. In addition, considering that the Coulomb viscous friction model is not sufficient to accurately express the friction terms, a two-step identification method is proposed by analyzing the sensitivity of the parameters of the Stribeck friction model, combining the significantly identified friction coefficients with the quadratically optimized coefficients of the adaptive inverse genetic algorithm, which solves the problem of lower accuracy caused by the inaccuracy of the friction parameter identification. Then, the dynamic parameters are calculated using the least squares method to determine the system dynamics model information. Finally, the parameter identification and load identification are verified using a 6-degree-of-freedom modular robot manipulator, and the proposed hybrid optimization strategy effectively solves the defect of the low accuracy of the robot manipulator dynamics model compared to the dynamics model moment with Coulomb viscous friction, which in turn improves the control accuracy. Meanwhile, the load identification accuracy can reach 97% depending on the identified dynamics information. [ABSTRACT FROM AUTHOR]

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. [ABSTRACT FROM AUTHOR]

Virtual computer-integrated manufacturing (VCIM) is a global integrated manufacturing system which can exploit locally as well as globally distributed manufacturing resources. Production scheduling plays an important role in the success of a VCIM system. In this paper, an innovative genetic algorithm (GA) is developed to search for optimal/sub-optimal solutions to the production scheduling problem in VCIM systems. The developed GA has a unique chromosome representation, two modified crossovers, three modified mutations, dynamic ranking selection, adaptive stop-and-restart-with-memory mechanism, and a parameter set tuned by the response surface method. The effectiveness of the developed GA is validated through a comprehensive case study. The computational data from the case study show that the developed GA outperforms three commercial optimisation solvers. The outcomes of this research serve as a foundation towards a global decision support system that can help decision makers to operate VCIM systems more effectively. [ABSTRACT FROM AUTHOR]

The cellular manufacturing technology, an application of group technology in manufacturing, has been a widely studied combinatorial optimization problem where the entire production system is divided into many cells and part families. In this paper, a novel clonal selection algorithm (CSA) that uses a new affinity function and part assignment heuristic for solving a multi-objective cell formation problem is studied. The proposed CSA has been hybridized with genetic algorithm for generating feasible cell sequences that fulfill both mutual exhaustivity and exclusion properties of machine cells prior to the initial population generation. Additionally, a new part assignment heuristic function that maps parts to machine cells and a novel basic affinity function have been built into the proposed CSA so that it can act as the utility function to solve the multi-objective cell formation problem. This hybrid CSA (HCSA) has been presented and computational results have been obtained for the proposed scheme with a set of 52 benchmark instances collected from literature. The results presented herein demonstrate that overall proposed HCSA is much more promising in comparison with existing approaches available in recent literatures. Extensive statistical and convergence tests have been carried out to ratify the superiority of the proposed HCSA. The improvements can be attributed to the collaborative interactions in the CSA mechanism, the proposed hybridization for initial population generation and so forth. [ABSTRACT FROM AUTHOR]

In this research paper, the proposed work is to put forward a Novel Data Privacy-Preserving Protocol (NDPPP) for Multi-data Users by using genetic algorithm. The data owner outsources the files in the encrypted format to the cloud. The data users can efficiently download the encrypted files from the cloud service provider without any loss of data. To provide this facility, there are certain existing mechanisms in the literature. But the existing mechanisms will result high computation complexity. By means of minimizing the computation complexity, a NDPPP for multi-data users is proposed using genetic algorithm in this research work. Genetic algorithms are usually used to produce high-quality resolutions for optimization. A new trapdoor function is proposed to preserve the data privacy by using our Data Privacy-Preserving Protocol. With the aim of avoiding numerous attacks, a secure authentication protocol is moreover developed between the trusted third party and data user. Additionally, the security is improved. Provided the leakage of data, loss of data as well as the data modification can also be avoided. The proposed work is implemented, and the implementation results of NDPPP illustrate that our protocol is efficient through computation and communication complexity. Moreover, the proposed work NDPPP is secure against various attacks like impersonation attack, eavesdropping, man-in-the-middle attack as well as replay attack. [ABSTRACT FROM AUTHOR]

Computational grid (CG) environment has a group of autonomous dissimilar distributed computing systems to provide service to user tasks. To attain the auspicious usefulness of CG resources, basically best scheduling algorithms are important. The CG service users are very cautious in time needed for task completion. To meet multiple requirements of the user, the job allocation problem in CG is designed as a multi-objective problem. Evolutionary algorithms have an efficient meta-heuristic technique for optimization problem solving. This work introduces a MapReduce model for non-dominated sorting genetic algorithm (NSGA-II) for independent job scheduling in a CG. This research work attempts to find the optimal schedules by considering makespan and flowtime minimization. A fuzzy membership function is used to analyze the efficiency of the schedule. Set of benchmark instances is used to test the algorithm. Experimental results show that MapReduce model-based NSGA-II generated finer solutions in less time than the MapReduce model-based weighted sum multi-objective genetic algorithm (WMOGA) which is also implemented in this paper. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of fault diagnosis performed on a mechanical system, based on acquired vibrations from bearings. In this aim, an optimization algorithm resulted from the alliance between a time–frequency–scale signal processing method (the matching pursuit) and an evolutionary computing technique (mainly, a genetic algorithm) is introduced. The matching pursuit method itself leads to a NP-hard procedure, but, with the help of a metaheuristic, the procedure becomes computationally efficient. A generalization of Baker's procedure implementing the stochastic universal sampling mechanism, as well as a new concept, namely the Boltzmann annealing selection, is introduced, in order to design the genetic algorithm appropriately. This latter not only plays an important role in convergence speed, but also constitutes the basis of a (self) adaptive mechanism aiming to keep in balance the exploration and exploitation features. Based on the optimal solution found through the genetic matching pursuit procedure, the bearings fault diagnosis can successfully be performed, even in case of multiple defects and without prior training of some defect classification model. [ABSTRACT FROM AUTHOR]

Recently, recognition of gender from facial images has gained a lot of importance. There exist a handful of research work that focus on feature extraction to obtain gender-specific information from facial images. However, analyzing different facial regions and their fusion help in deciding the gender of a person from facial images. In this paper, we propose a new approach to identify gender from frontal facial images that is robust to background, illumination, intensity, and facial expression. In our framework, first the frontal face image is divided into a number of distinct regions based on facial landmark points that are obtained by the Chehra model proposed by Asthana et al. The model provides 49 facial landmark points covering different regions of the face, e.g., forehead, left eye, right eye, lips. Next, a face image is segmented into facial regions using landmark points and features are extracted from each region. The compass LBP feature, a variant of LBP feature, has been used in our framework to obtain discriminative gender-specific information. Following this, a support vector machine-based classifier has been used to compute the probability scores from each facial region. Finally, the classification scores obtained from individual regions are combined with a genetic algorithm-based learning to improve the overall classification accuracy. The experiments have been performed on popular face image datasets such as Adience, cFERET (color FERET), LFW and two sketch datasets, namely CUFS and CUFSF. Through experiments, we have observed that, the proposed method outperforms existing approaches. [ABSTRACT FROM AUTHOR]

The resource-constrained project scheduling problem is one of the most intractable problems in the field of operation research and management science. It is important for both academic research and engineering applications to develop effective solution algorithms for scheduling problems. In this paper, the activity back-shift response model is introduced for scheduling problems, based on which a resource-constrained scheduling model is built, and a two-phase decomposition algorithm is proposed to address the project resource-constraint and timing optimization. The first stage is resource optimization, where the conflicted activities are resolved by a genetic algorithm. In this case, if parallel activities are scrambling resources during resource optimization, the activity priority, determined by comparing the activity back-shift response time, is applied to dissolve the resource conflict. The second stage is timing optimization. After obtaining the progress plan with optimized resource constraints, activities are adjusted to move forward as much as possible. The validity of the proposed algorithm is verified by a real project case. The computational results reveal that the proposed model and algorithm obtain appropriate results and have the potential to be applied to other similar problems. [ABSTRACT FROM AUTHOR]

Writing identification based on handwriting has many applications in the real world. Due to various forms of written letters in different languages, one of the major challenges in this context is offering an efficient method not being dependent on any specific language. In this paper, we have proposed a new approach based on image mining techniques for offline and text independent writer identification. In this method, each writers' prominent features are found from training samples, and then identification is done according to them. In the image mining part of the proposed approach, certain techniques including SVM classifier and genetic algorithms are employed. To evaluate this method and show its performance in different languages, CASIA for Chinese, IAM dataset for English and two datasets for Kannada and Persian language handwriting were examined. The experiment results demonstrate that the presented method has over 99% accuracy for these languages. Regarding the results in tested languages and the method details, it is highly likely that our method would have good results in other languages also. [ABSTRACT FROM AUTHOR]

Spectral efficiency (SE) and energy efficiency (EE) are both important metrics in massive multiple-input multiple-output (MIMO) systems. However, maximizing EE and SE is always conflicting with each other, and they can hardly be achieved simultaneously. In this paper, we focus on the tradeoff optimization between EE and SE in multiuser massive MIMO systems in terms of the number of transmit antennas and the transmit power. Different from the previous EE-oriented or SE-oriented method, the EE–SE tradeoff problem is formulated into a multi-objective optimization problem. To efficiently attain the Pareto optimal front (POF) of EE–SE tradeoff, a multi-objective adaptive genetic algorithm, inspired by the non-dominated sorting genetic algorithm (NSGA-II), is proposed to improve the convergence speed. Experimental comparisons against several well-known multi-objective algorithms show that the proposed algorithm can quickly adapt to the true POF of EE–SE tradeoff and maintain good performance on benchmark functions in terms of the adopted performance metrics. [ABSTRACT FROM AUTHOR]

Although Doubly fed induction generator (DFIG)-based wind turbine has several significant advantages, any voltage fluctuation can severely disturb its normal and safe operation, i.e., it is characteristically very sensitive. Hence, fault ride-through is determined as a prominent benchmark for DFIG which must be enhanced by a voltage compensator. Dynamic voltage restorer (DVR), a voltage fluctuation mitigator, is connected in-series with distribution and sub-transmission lines so that DFIG can appropriately operate during any type of disturbance. In this paper, a new two switches boost converter (TSBC) coupled with quinary multilevel inverter (QMLI) is proposed which is fed by fuel cell to produce staircase sinusoidal voltage for DVR. The proposed step-up DC/DC presents two important advantages: low current ripple and low switch stress, while QMLI can create high-step staircase sinusoidal voltage without considering a number of switches. TSBC-QMLI-based DVR has been controlled by neuro fuzzy inference system controller (ANFISC). As the control problem is multi-objective and complex, Multi-Objective Salp Swarm Algorithm (MOSSA) has been applied to optimally tune the ANFISC's parameters, and then compared with Non-dominated Sorting Genetic Algorithm II (NSGA-II). To verify and validate the compensation capability of proposed DVR, it has been thoroughly evaluated under different voltage disturbances. Eventually, the simulation results have validated the compensation capability of the suggested TSBC-QMLI-based DVR which is accurately controlled by MOSSA-based ANFISC to mitigate the balanced and unbalanced voltage sag and swell along with the flicker and harmonic voltage. [ABSTRACT FROM AUTHOR]

Public bicycle-sharing systems have been implemented in many big cities around the world to face many public transport problems. The exploitation and the management of such transportation systems imply crucial operational challenges. The balancing of stations is the most crucial question for their operational efficiency and economic viability. In this paper, we study the balancing problem of stations with multiple vehicles by considering the static case. A mathematical formulation of the problem is proposed, and two lower bounds based on Eastman's bound and SPT rule are developed. Moreover, we proposed four upper bounds based on a genetic algorithm, a greedy search algorithm and two hybrid methods that integrate a genetic algorithm, a local search method and a branch-and-bound algorithm. The developed lower and upper bounds are tested and compared on a large set of instances. [ABSTRACT FROM AUTHOR]

The intermittent feature of renewable energy sources leads to the mismatch between supply and load demand on microgrids. In such circumstance, the system experiences large fluctuations, if the secondary load frequency control (LFC) mechanism is unable to compensate the mismatch. In this issue, this paper presents a well-structured combination of the fuzzy PD and cascade PI-PD controllers named FPD/PI-PD controller as a supplementary (secondary) controller for the secondary load frequency control in the islanded multi-microgrid (MMG). Additionally, two modifications to the JAYA algorithm are made to enhance the diversity of the initial population and ameliorate the global searching ability in the iterative process. Afterward, the improved JAYA algorithm, referred to as IJAYA, is employed for fine-tuning the proposed structured controller installed in areas of the studied MMG. The superiority of the proposed IJAYA is validated by comparative analysis with genetic algorithm and basic JAYA in a similar structure of the PID controller. Furthermore, it will be shown that the proposed FPD/PI-PD controller employing IJAYA provides a higher degree of stability in suppressing the responses deviations as compared with the conventional PID and FPID controller structures. Finally, the novel optimal proposed approach is validated and implemented in the hardware-in-the-loop (HIL) based on OPAL-RT to integrate the fidelity of physical simulation and the flexibility of numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose three novel algorithms such as Novel genetic algorithm complex-valued backpropagation neural network (GA-CVBNN), Novel elegant fuzzy genetic algorithm (EFGA) and elegant fuzzy genetic algorithm-based complex-valued backpropagation neural network (EFGA-CVBNN) for classification of accuracy in datasets. In GA-CVBNN, classical Genetic Algorithm has been used for selecting appropriate initial weights for CVBNN. The EFGA is developed to resolve the drawback of classical GA by employing fuzzy logic to control parameters and selective pressure of GA. The EFGA uses a Min-Heap data structure and Pareto principle to improve the classical genetic algorithm. The EFGA-CVBNN resolves the drawbacks of classical CVBNN by employing EFGA at the time of initial weight selection. From the simulation result, the GA-CVBNN performs better than existing CVBNN and it is not efficient. To enhance the performance of GA-CVBNN, we have developed EFGA-CVBNN. Experimental results on various synthetic datasets and benchmark datasets taken from UCI machine learning repository shows that EFGA-CVBNN outperforms PSO-CVBNN in terms of classification accuracy and time. Statistical t test has been used to validate the obtained results. [ABSTRACT FROM AUTHOR]

This paper introduces a new filter for nonlinear systems state estimation. The new filter formulates the state estimation problem as a stochastic dynamic optimization problem and utilizes a new stochastic method based on genetic algorithm to find and track the best estimation. In the proposed filter, each individual is set based on stochastic selection and multiple mutations to find the best estimation at every time step. The population searches the state space dynamically in a similar scheme to the optimization algorithm. This approach is applied to estimate the state of some nonlinear dynamic systems with noisy measurement and its performance is compared with other filters. The results indicate an improved performance of heuristic filters relatives to classic versions. Comparison of the results to those of extend Kalman filter, unscented Kalman filter, particle filter and heuristic filters indicated that the proposed heuristic filter called genetic filter fulfills the essential requirements of fast and accuracy for nonlinear state estimation. [ABSTRACT FROM AUTHOR]

Open-shop scheduling problem (OSSP) is a well-known topic with vast industrial applications which belongs to one of the most important issues in the field of engineering. OSSP is a kind of NP problems and has a wider solution space than other basic scheduling problems, i.e., Job-shop and flow-shop scheduling. Due to this fact, this problem has attracted many researchers over the past decades and numerous algorithms have been proposed for that. This paper investigates the effects of crossover and mutation operator selection in Genetic Algorithms (GA) for solving OSSP. The proposed algorithm, which is called EGA_OS, is evaluated and compared with other existing algorithms. Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of mesh router placement in a wireless mesh network (WMN). The latter is an emerging networking technology consisting of three kinds of nodes: mesh clients, mesh routers and gateways. Mesh routers form a backbone to forward data between client nodes and the external network. Therefore, the optimization of mesh routers positions strongly influences the performance of the WMN. Since this issue has already been proved as being computationally NP-hard to solve, the use of non-exact methods (such as heuristics and metaheuristics) is indispensable. In this sense, our current work consists to apply and adapt the electromagnetism-like mechanism (EM) metaheuristic to solve the router node placement issue. The idea is to consider a population of solutions encoded as particles subject to attractions and repulsions as in electromagnetic systems. Finally, we have evaluated our proposed approach by simulating different scenarios under various settings. The obtained results indicate that the proposed EM algorithm outperforms the existing particle swarm intelligence algorithm and genetic algorithm in defining near optimal positions for mesh routers with regard to coverage and connectivity. [ABSTRACT FROM AUTHOR]

In this paper, a new bi-objective fuzzy portfolio selection model is proposed, for which Sharp ratio (SR) and Value at Risk ratio (VR) of a portfolio are chosen as objectives. SR is an important nonsystematic risk measurement that examines the investment risk by aspiring the diversification of the capital allocation. On the other hand, VR measures the systematic risk, which reduces the largest loss of an investment at a given confidence level. The proposed fuzzy portfolio model assumes both SR and VR as maximization objectives for which the associated fuzzy parameters are considered as triangular fuzzy numbers. The proposed model is solved using multi-objective genetic algorithms, namely multi-objective cellular genetic algorithm (MOCell), archive-based hybrid scatter search (AbYSS), and nondominated sorting genetic algorithm II (NSGA-II). We have used a data set from the Shenzhen Stock Exchange to illustrate the performance of the proposed model and algorithms. Finally, a comparative study in terms of five standard performance metrics is presented, among the MOCell, AbYSS, and NSGA-II algorithms that are mentioned extensively in various research articles to exhibit the best suitable algorithm. [ABSTRACT FROM AUTHOR]

Normally, genetic algorithm (GA) does not guarantee global optimum for all optimization problems. Crossover operators play a crucial part in the convergence of GAs to a solution. Hence, if crossover is designed to pass on genes that highly contribute to the fitness of individuals, to subsequent generations, the convergence can be obtained faster while obtaining the best possible solution for the given initial population. In this paper, we propose two new crossover operators called the dominance and co-dominance crossover operators, based on the dominance and co-dominance principles of human genetics, respectively, to achieve this in case of applications using integer and real encoding. The dominance crossover operator is designed such that the child obtains a gene (feature) from a parent whose value for a particular gene (feature) is dominant than its value in the other parent. On the other hand, the co-dominance crossover operator is designed such that a child obtains two values for the same gene from both the parents in case both alleles (gene values) are equally dominant. These crossover operators were designed to get the optimal solution in less number of generations without sacrificing the performance of GA. The experiments conducted on test functions and two different problems, namely clustering (Reuters-21578 dataset) and learning to rank (LETOR dataset), emphasize that global optimum in fewer number of generations is obtained using our proposed crossover operators. [ABSTRACT FROM AUTHOR]

The idea of knowledge aggregation contained in C-fuzzy decision tree nodes with OWA operators during the C-fuzzy random forest decision-making process is presented in this paper. C-fuzzy random forest is a new kind of ensemble classifier which consists of C-fuzzy decision trees. There are proposed three kinds of OWA operators for the given problem, called Local OWA, global OWA for each tree in the forest and global OWA for the whole forest. Weights of OWA operators are optimized using a genetic algorithm. In order to evaluate the created classifier, experiments were performed using ten datasets. The classifier was checked in comparison with C4.5 rev. 8 decision tree and single C-fuzzy decision tree. The influence of randomness and proposed OWA operators on the classification accuracy was tested. [ABSTRACT FROM AUTHOR]

Schlieren imaging in wind-tunnels is extensively utilized to study the effects of air on a moving object. One of the interested subjects for research is to study the effects of speed change on the object surface. Speed change results in the occurrence of shock waves, which are visualized as lines on Schlieren images. However, computing new relevant velocity of the wind-tunnel requires solving sophisticated and time-consuming formulas. In this paper, we investigate the problem of estimating relevant speed of the object after occurrence of a shock wave. At first, we propose a feature set of the image that are influenced by the shock wave. Therefore, these features are extracted by the developed image processing component. Afterward, we propose a fuzzy genetic algorithm to estimate the new velocity of the object. We make use of the genetic algorithm to tune the membership functions of the variables of the fuzzy system by leveraging some training images. The evaluation is performed by computing the accuracy of the velocity estimation. For this, the proposed fuzzy system runs by the extracted features of these images and estimates the new velocity. The comparison of the estimated with the real values shows a very close and accurate estimation. [ABSTRACT FROM AUTHOR]

Equalizers play an essential role in processing and enhancement of quality of audio signals apart from their conventional utilizations to flatten out undesirable harmonics from telephone signals, music, etc. In recent times, research in different variants of equalizers has seen a great increase with optimization algorithms trying to provide maximum gain with least interference between adjacent frequency bands. This research paper proposes a 16-order IIR bi-quad filter optimized with evolutionary algorithm in the form of genetic crossover and mutation to provide maximum gain. The magnitude, frequency response and group delay characteristics obtained from the proposed algorithm have been tested and analyzed by comparing with existing conventional Nth-order IIR implementations without optimization and superior performance observed. It could be seen that that peak error oscillates between ± 1.9 dB for hundredfold increase in order of filters with a maximum limit set at 750. Computational complexity is observed to be reduced as the proposed algorithm searches the sample space for the best order suitable for the given target output in the least time possible. The experimentations have been implemented in MATLAB and SNRs measured as performance metric. [ABSTRACT FROM AUTHOR]

This paper presents a new method based on reliability-based possibility degree of interval to handle the general interval bilevel linear programming problem involving interval coefficients in both objective functions and constraints. Considering reliability of the uncertain constraints, the interval inequality constraints are first converted into their deterministic equivalent forms by virtue of the reliability-based possibility degree of interval, and then the original problem is transformed into a bilevel linear programming with interval coefficients in the upper and lower level objective functions only. Then, the notion of the optimal solution of the problem is given by means of a type of the interval order relation. Based on this concept, the transformed problem is reduced into a deterministic bilevel programming with the aid of linear combination method. Furthermore, the proposed method is extended to deal with the fuzzy bilevel linear programming problem through the nearest interval approximation. Finally, three numerical examples are given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Evolutionary K-Means (EKM), which combines K-Means and genetic algorithm, solves K-Means' initiation problem by selecting parameters automatically through the evolution of partitions. Currently, EKM algorithms usually choose silhouette index as cluster validity index, and they are effective in clustering well-separated clusters. However, their performance of clustering noisy data is often disappointing. On the other hand, clustering stability-based approaches are more robust to noise; yet, they should start intelligently to find some challenging clusters. It is necessary to join EKM with clustering stability-based analysis. In this paper, we present a novel EKM algorithm that uses clustering stability to evaluate partitions. We firstly introduce two weighted aggregated consensus matrices, positive aggregated consensus matrix (PA) and negative aggregated consensus matrix (NA), to store clustering tendency for each pair of instances. Specifically, PA stores the tendency of sharing the same label and NA stores that of having different labels. Based upon the matrices, clusters and partitions can be evaluated from the view of clustering stability. Then, we propose a clustering stability-based EKM algorithm CSEKM that evolves partitions and the aggregated matrices simultaneously. To evaluate the algorithm's performance, we compare it with an EKM algorithm, two consensus clustering algorithms, a clustering stability-based algorithm and a multi-index-based clustering approach. Experimental results on a series of artificial datasets, two simulated datasets and eight UCI datasets suggest CSEKM is more robust to noise. [ABSTRACT FROM AUTHOR]

Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS. [ABSTRACT FROM AUTHOR]

This paper presents a biologically inspired deoxyribonucleic acid (DNA) algorithm for solving the inverse kinematics of five degree-of-freedom (DOF) robotic manipulators. Although the 5-DOF robotic manipulator agilely maneuvers from one pose to another and easily performs some tasks, the inverse kinematics problem of the system is redundant. This problem has no general closed-form solutions or analytical solutions for the 5-DOF manipulator. The DNA soft computing is proposed to solve the redundant inverse kinematics problem of the 5-DOF robotic manipulators more efficiently. Simulation results are conducted to illustrate the effectiveness and merit of the proposed methods in comparison with conventional genetic algorithm (GA) and particle swarm optimization. [ABSTRACT FROM AUTHOR]

Multi-robot task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize cost while satisfying operational constraints. It can be challenging to solve a large number of tasks and becomes even more challenging when tightly coupled multi-robot tasks are also taken into account. For example, it is more complex to solve problems that include tasks that have to be carried out jointly by two robots due to the resulting temporal and spatial constraints. Additionally, the complexity of task allocation increases exponentially with rising task variety. This paper focuses on multi-robot task allocation in inspection problems with both single- and two-robot tasks. A novel memetic algorithm is proposed combining a genetic algorithm with two local search schemes. Using permutation representation, eight approaches based on four basic coding strategies are designed for multi-robot task allocation of inspection problems with two-robot tasks. The performance of the memetic algorithm is evaluated in case studies on inspecting a large storage tank area of a petroleum refinery. [ABSTRACT FROM AUTHOR]

In intelligent mechanism design, isomorphism identification of mechanism kinematic chains (IIMKC) is aimed at avoiding repeated mechanism design and is proved to be an NP-complete problem. In this paper, kinematic chains are represented by graphs. An improved hybrid immune algorithm, which integrates the clonal selection immune algorithm with genetic algorithm and the local search algorithm, is proposed to solve IIMKC problem. Moreover, the novel saving and updating operator is proposed to save the best antibodies and maintain a diverse repertoire of antibodies for improving performance of clonal selection. In addition, the pseudo-crossover operator is introduced to enhance the efficiency of genetic algorithm. Simulation results validate the high efficiency and robustness of the hybrid immune algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS ring oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect trade-off between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillation frequency, the Power Consumption, Phase Noise, Figure of Merit, Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e., Overall Nondominated Vector Generation, and Spacing are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm, Multi-Objective Inclined Planes system Optimization, Multi-Objective Particle Swarm Optimization and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Based on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool. [ABSTRACT FROM AUTHOR]