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This paper studies the minimum edge-dilation K-center (MEDKC) problem for edge-weighted, undirected and connected graphs. This problem which is N P -hard holds significant relevance in designing efficient routing schemes for computer networks. To the best of our knowledge, there exists only two variants of genetic algorithm that have been developed for this problem. In this paper, we propose an artificial bee colony algorithm (ABC_MEDKC) for this problem. The proposed ABC_MEDKC incorporates two adaptable neighborhood operators specifically tailored for this problem in which the first neighborhood operator utilizes solution components of another solution, and the second neighborhood operator follows swapping of center vertices with non-center vertices in a mixed strategies of greedy and random approach. On available benchmark instances, computational results of ABC_MEDKC indicate that ABC_MEDKC overall outperforms the existing two variants of genetic algorithm in both solution quality and computational time. ABC_MEDKC also outperforms an existing polynomial-time approximation algorithm developed for this problem in terms of solution quality. ABC_MEDKC finds new values for 17 instances out of 91 instances. In addition, the convergence behavior of ABC_MEDKC and the statistical analysis are also studied. [ABSTRACT FROM AUTHOR]

The failure modes of concrete columns can be applied to judge the ductility of concrete columns under seismic loads. In addition, the load-bearing capacity of concrete columns was an important parameter in evaluating the seismic performance of concrete columns under seismic loads. However, it was difficult to determine the failure modes and evaluate the load-bearing capacity of concrete columns under seismic loads with the application of high-strength concrete. In this paper, two ANNs, including a genetic algorithm optimization-based support vector machine (GA-SVM) and an ANFIS, were developed to determine the failure modes of concrete columns under seismic loads. The main investigated parameters, including specimens' shear span ratio, the compressive strength of concrete, the yield strength and volumetric ratio of stirrups, and the yield strength and ratio of longitudinal reinforcement, were applied as input variables to the models. The proposed models were trained and tested using 247 data sets from previous studies. The proposed models had high reliability and predictability to determine the failure modes of concrete columns under seismic loads. Furthermore, an ANFIS network was developed to evaluate the load-bearing capacity of concrete columns, which had high reliability and predictability. [ABSTRACT FROM AUTHOR]

Stock markets around the world are growing rapidly, and vulnerability created by default risk of option holders has become a serious issue. In such circumstances, this paper suggests the pricing of vulnerable European and barrier options, when the underlying asset and asset held by the option writer follow the geometric Liu process and uncertain mean-reverting process, respectively. According to the uncertainty theory, we present closed-form analytic formulas of these option prices and use the residual method to estimate the model parameters. In order to estimate the model parameters, the genetic algorithm to solve the generalized moment estimation problem derived by the residual method is suggested. From the uncertain hypothesis test, we then evaluate whether the proposed uncertain stock models fit the observed data. Ultimately, based on the estimated parameters, we evaluate the effect of the vulnerable option parameters on its price. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is a contemporary network architecture concept that integrates complex network functions into software-based virtual network functions (VNFs) instead of specialized hardware. Resource allocation for multiple service function chain requests (SFC) in NFV-enabled networks is a fundamental challenge for network providers. Several research studies have focused on this problem in detail. Specifically, SFC routing attempts to satisfy each request by assigning a path across data centers that places the ordered set of required VNFs. This paper formulates this problem as an SFC provisioning problem (SFCRP) with the two objectives of maximizing the number of accepted requests and balancing the utilization of network resources. To address this problem, a two-stage routing algorithm (2PRT) that integrates Yen's algorithm and a genetic algorithm is proposed. The simulation results based on 12 scenarios that use three real-world networks and randomly generated requests indicate that the batch mode significantly enhances the acceptance rate compared to the existing methods. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the laser cladding process parameters of WC/Inconel 625 composite on a low-carbon steel substrate. This process aims to improve industrial parts' wear and corrosion properties in corrosive and high-temperature environments. Laser power, laser cladding speed, powder-feeding rate, and WC content percentage were process input parameters, each at three levels. The dilution ratio and micro-hardness of samples were considered as process responses. Taguchi orthogonal arrays were employed for experimental design, and the results were analyzed using signal-to-noise ratio and analysis of variance. A linear regression model was developed to understand the relationship between process parameters and responses. In addition, two-objective optimization of the process parameters using the second-generation non-dominated sorting genetic algorithm (NSGA-II), and a hybrid algorithm based on the multi-objective whale optimization algorithm (MOWOAGA) were carried out to determine the optimal parameters that minimize the dilution ratio and maximize the cladding hardness. The results showed that the laser power and the powder-feeding rate have the most excellent effect on the dilution ratio. In contrast, WC content percentage and powder-feeding rate strongly affect micro-hardness. Based on the applied meta-heuristic algorithms and confirmed by experimental tests, the laser power of 370 W, laser cladding speed of 4 mm/s, powder-feeding rate of 8 g/min, and WC content percentage of 20.5% are the most optimal process parameters that lead to micro-hardness of 397 Vickers and dilution percentage of 2.5%. This research guides selecting, controlling, and optimizing WC/Inconel 625 composite laser cladding process parameters. [ABSTRACT FROM AUTHOR]

The fast progress of artificial intelligence (AI) technology has significantly impacted the field of interpretation. Nonetheless, interpretation education at many Chinese schools and universities is poor quality. It strongly emphasizes theory while providing basic content, resulting in minimal pupil growth. Furthermore, existing interpretation assessment procedures must be revaluated to improve comprehensive instructional growth. Aiming at this problem, this paper systematically constructs the Automatic Interpretation Assessment System by integrating key components. Firstly, this paper explains the significance of interpretation as a cognitive bridge between AI and human cognition by emphasizing the critical relevance of interpretability in AI systems. Second, the research explores the intricacies of the internal workings of backpropagation (BP) neural networks. It explains their structural composition, signal propagation processes, and weight adjustment procedures, all of which are strategically employed to enable optimal learning within the context of the model. Third, it integrates genetic algorithms (GA) with BP neural networks in a unique way, combining the optimization capabilities of GA. This collaborative strategy employs selection, crossover, and mutation strategies to improve system performance and create a strong issue-detection feedback mechanism. Finally, utilizing interpretable AI theory, the study methodically creates the Automatic Interpretation Assessment System. This approach has a three-tier paradigm, with careful evaluation criteria including interpreter ability and skill. The requirements include bilingual competence, extra-lingual knowledge, interpreting abilities, professional excellence, and psychological quality. We comprehensively evaluated using essential parameters such as accuracy, precision, recall, F1 score, and execution time. These results demonstrate that our proposed system excels with an accuracy of 95%, a precision of 94%, a recall of 96%, and an F1-score of 0.95, showcasing its superiority over the other systems. These findings highlight our proposed system effectiveness in reliably assessing interpretation quality and efficient processing capabilities. [ABSTRACT FROM AUTHOR]

Music expresses thoughts and emotions in artistic form and is made up of different components including harmony, rhythm, and melody. Several musical elements are tied together during the songwriting process in order to generate melodies that are harmonic. A music melody is the essential element of any music to generate strong feelings from listeners and capture their interest. In the process of music appreciation, melody controls the emotional changes of music. It is an efficiently perceived part and the tone of a song. In recent years, Sichuan unvoiced music has developed rapidly and attracted much attention. This paper selects Sichuan unvoiced music as the main research theme and constructs a melody generation algorithm by utilizing the state-of-the-art techniques of deep learning (DL) and evolutionary algorithms (EAs) such as recurrent neural network-long short-term memory (RNN-LSTM) and genetic algorithm (GA). Firstly, this paper briefly describes the concept of DL algorithms, the deep generation model, and sequence to sequence model, as they constitute the technological foundation for this research. Secondly, this paper proposes a melody generation algorithm that utilizes RNN-LSTM for melody generation and GA for melody optimization. More specifically, the melody is generated by preprocessing data, creating, and training the RNN-LSTM model. A GA was used to determine the melodic fitness function for eight songs as the fitness function directly affects the selected termination condition. The fitness function can be thought of as either a person or an evolutionary rule. Finally, the average score of these songs, before and after evolution, is calculated, which demonstrates that the analysis and rotation creation methods are more precise and that the song's average melody score is higher. The method proposed in this study has been thoroughly compared to the existing approaches proposed in earlier studies, and it was found that the approach we propose is more effective in terms of accuracy and the average melody score. [ABSTRACT FROM AUTHOR]

Accurately predicting the performance in various sports is crucial in comprehending training characteristics and enhancing physical education and training processes. The increasing significance of sports in China has enhanced the interest for developing prediction models to assess athletes' sports performance, accurately. These models will greatly contribute to improving sports performance and implementing scientific training methods. This paper proposes a prediction model for athletes' sports performance using Neural Networks as the underlying framework. This research aims to enhance sports performance and scientific training by developing a reliable and effective prediction model. First, the proposed model utilizes neural network algorithms, including error backpropagation and genetic algorithms, to train and optimize the prediction model. Second, it analyzes temporal patterns, extracts statistical features, and adapts to new data by ensuring reliability and effectiveness of the proposed model. Third, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models to evaluate the accuracy of the prediction model. In addition, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models. Finally, the results demonstrate that our proposed method achieves a maximum error of 36.12%, with the highest error rate in the BP network prediction being 6.76%. Furthermore, we compared our predicted results with multiple linear regression and other existing prediction methods, and our proposed method demonstrated superior accuracy with an overall prediction accuracy of 97.6%. Our suggested solution surpasses previous approaches with much higher accuracy, reduced latency, improved recall, and increased scalability. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop and test three meta-heuristic algorithms to solve large size multi-objective supply chain network design problems. The algorithms are based on tabu search, genetic algorithm, and simulated annealing to find near-optimal global solutions. The three algorithms are designed, coded, and tested, and their parameters are fine tuned. The exact ε-constraint algorithm embedded in the General Algebraic Modeling System is used to validate the results of the three algorithms. The algorithms are compared using a typical multi-objective supply chain model utilizing several performance measures. The measures include the mean ideal distance, diversification metric, and percent of domination, inverted generational distance, and computation time. The results show that the tabu search algorithm outperformed the other two algorithms in terms of the percent of domination and computation time. On the other hand, the simulated annealing solutions are the best in terms of their diversity. The work in this paper is expected to help managers to solve large-scale supply chain problems that arise in oil and gas, petrochemical, and food supply chains. [ABSTRACT FROM AUTHOR]

The wireless network is composed of multiple micronetwork nodes that are difficult to replace. These network nodes are usually located in areas with poor environmental conditions, so the energy consumption and security of nodes have become the main limitations and challenges. Therefore, this paper discusses the network topology, data transmission and node trust mechanism, and proposes a cloud computing technology algorithm for intelligent regional wireless network. According to the data of arrival delay difference in intelligent regional wireless network environment, the approximate location of mobile system is set as different positioning results. The dynamic position of the mobile system is obtained through multiple calculations. With the support of cloud computing technology, this paper also studies the color rendering system of 3D animation. 3D animation can not only express emotions and feelings, but also color render through the facial expressions of characters in the animation, so as to enhance the entertainment and interest visible to the audience. However, due to the great demand for computing resources and color rendering resources, at present, the main research and implementation are carried out on the desktop platform. There are also solutions to use high-end infrared devices for 3D animation color rendering on the mobile system. However, because this method has high cost in use and does not have universality on the mobile device, this paper proposes a rendering system suitable for the general mobile platform. This paper studies the 3D animation color rendering system based on wireless network cloud computing technology, hoping to realize the universality of the system with the support of technology. [ABSTRACT FROM AUTHOR]

In the construction of enterprise financial risk prediction model, the obtaining of the optimal solution has a key impact on the prediction effect of the prediction model. Exploring the optimization and improvement of the prediction model to obtain the optimal solution is an urgent problem. In this paper, based on genetic algorithm and data mining technology, a genetic algorithm model based on adaptive improvement is proposed. Through linear adaptive optimization, the optimal solution of complex problems is obtained. When estimating parameters, EM algorithm is used to estimate, so that the final effect of model fusion can reach the expectation. At the same time, it integrates data mining technology, Borderline STATE EasyEnsemble sampling method and SVM intelligent technology to build an optimized comprehensive risk early warning model, which realizes the automatic integration and prediction of financial risks. The adjusted mathematical model is better than the single technical model. This study verified that the prediction ability of the improved model for minority samples has been greatly improved. Under the new method, F1 + F2 + F3 has increased by 14.83% compared with the single F1 feature, and the prediction accuracy of most types of samples has not been reduced. The prediction effect has generally improved. Finally, according to the current situation of enterprise finance, this paper puts forward effective measures to prevent enterprise financial risks. [ABSTRACT FROM AUTHOR]

Calculus equation is an important tool for mathematical research and plays an important role in most natural science research. Since the beginning of the eighteenth century, people have gradually used differential and integral equations to solve physical problems. In general, several different aspects of differential equations in the field of mathematics are concerned and studied by most scholars. However, this paper studies and establishes the optimal model for numerical solution of differential equations through deep learning and genetic algorithm. In this paper, the solution of ordinary differential equations is solved through the use of polynomial function space, while the linear combination of simple function x and its product nx can obtain multinomial function space. The space function form of polynomial is very simple, and the operation ability is very strong. Almost all functions can be approximated, and the function space can be transformed by a simple function. Through data simulation test results, it can be found that the oscillation of neural network output is stronger and stronger with the increase in depth, that is to say, the deeper depth endows the neural network with stronger oscillation properties, so for the oscillation function, the depth neural network fitting effect is better than the shallow neural network. Therefore, by combining deep learning and genetic algorithm, this paper studies and establishes the optimal model for numerical solution of differential equations, and finds that the deep neural network can largely complete data simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for solving the system of fuzzy relation equations (SFRE) with extended max–min composition for inverse inference problems. The properties of interval and constrained solutions with granular and relational structure of the solution set are investigated. The extended max–min SFRE can be represented in the form of the max–min subsystems aggregated using the min operator or dual min–max subsystems aggregated using the max operator. When decomposing the SFRE, the set of solutions can be decomposed into the lower and upper subsets bounded by the same aggregating solutions. Each lower (upper) subset is defined by the unique greatest (least) or aggregating solution and the set of minimal (maximal) solutions. Following Bartl et al. (Fuzzy Sets Syst 187:103–112, 2012), to avoid excessive granularity and ensure interpretability of the interval solutions when restoring causes through observed effects, the constraints in the form of linguistic modifiers are imposed on the measures of causes significances. The interval solutions are modeled by the complete crisp solutions, that is, the maximum solutions for the vectors of binary weights of the linguistic modifiers. The search for approximate solutions of the SFRE amounts to solving the optimization problem using the genetic algorithm. Due to the properties of the solution set, the genetic search for the lower and upper subsets is parallelized for each aggregating solution. The developed method makes it possible to simplify the search for the solution set based on the constraints on accuracy (interpretability) of the applied problem. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of locating vehicle-identification sensors along the arcs of the transportation network. The aim is to estimate the traffic volumes for a given set of routes under the assumption that the available sensors are insufficient to uniquely identify all route flows. We present a novel mixed-integer linear programming (MILP) model to determine the sensor locations so that in the system of linear equations solved in the path reconstruction phase, those routes whose volume cannot be uniquely determined, are linked to each other by equations involving a small number of unknowns. By this approach, experts' opinions or historical information can be used to give a more precise estimation for those routes whose volumes are not uniquely observable. Since the direct resolution of the model via MILP solvers is time-consuming over moderate- and large-sized instances, by utilizing the problem structure, a genetic algorithm is adopted to find high-quality solutions to the model. Computational experiments over different instances, taken from the literature, confirm the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Product Portfolio Planning (PPP) is one of the most critical decisions for companies to gain an edge in the competitive market. It seeks for the optimal combination of products and attribute levels offered for customers in the target market, which is a NP-hard combinatorial optimization problem. In this paper, a Probability-based Discrete Particle Swarm Optimization (PDPSO) algorithm is proposed to solve the PPP problem. In PDPSO, the particle is encoded as discrete values, which can be straightforwardly used to represent the product portfolio with discrete attributes. PDPSO adopts a probability-based mechanism to update particles. Specifically, a probability vector is used to decide the probability of three search behaviors, i.e., learning from the personal best position, global best position, or random search. In experiments, the search performance of PDPSO has been compared with that of a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm on generated PPP problem cases with different sizes. The results indicate that PDPSO obtains significantly better optimization results than GA and SA in most cases and obtains desirable/near-optimal solutions on various PPP problem cases. A case study of notebook computer portfolio planning is also presented to illustrate the efficiency and effectiveness of PDPSO. [ABSTRACT FROM AUTHOR]

Advancements in technology have led to a significant growth of agricultural data. The potential for forecasting crop yield depends on parameters such as climate, soil characteristics, and water factors has emerged as significant research area. Combining a comprehensive dataset from diverse sources like climate, soil and water parameters for precise time series forecasting in agricultural yield prediction is a significant challenge. Therefore, there is a need to integrate this data with agricultural processes to enhance crop production. In this paper, propose Genetic Algorithm based Autoregressive Integrated Moving Average (ARIMA) and Reinforced Random Forest Algorithm forecasting models for enhancing crop yield prediction. The genetic algorithm emulates the evolutionary process, simulating the selection of the strongest individuals over weaker ones through mechanisms such as selection, crossover, and mutation. The new approach incorporates reinforcement learning during the selection of attributes for tree construction by optimizing sample usage. By assessing variable significance, it identifies the most significant variables for node splitting improving model development and maximizing training data utilization. In addition to internal cross-validation, the method minimizes parameter tuning, mitigates overfitting, accelerates computations, and enhances transparency. The proposed model Genetic Algorithm based ARIMA achieves better results for enhancing crop yield forecasting on evaluation metrics, i.e., MAE (0.80%), RMSE (3.75%), RRSE (1.21%), RAE (0.82%), MSE (0.07%), R2 (0.54%) and accuracy (80.00%). For Reinforced Random Forest Algorithm model, i.e. MAE (1.28%), RMSE (2.48%), RRSE (0.54%), RAE (0.91%), MSE (0.55%), R2 (0.98%) and accuracy (78.00%). The analysis reveals that the genetic algorithm based ARIMA model outperforms the reinforced random forest algorithm in terms of efficiency and accuracy. Both proposed models better perform than machine learning classical models excelling not only in enhanced crop yield forecasting but also in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

Big data technology has undoubtedly accelerated the efficiency of data processing, made data become an important reference basis in enterprise operation, and changed the mode of enterprise operation as well as the existing business model. With the development of computer technology and related equipment, more and more enterprises and organizations can begin to use big data processing technology. However, many small and medium-sized enterprises, because they cannot afford the high cost of research and development and leasing, are gradually eliminated in the information-based business competition, so it is necessary to adopt certain strategies to help small and medium-sized enterprises out of this crisis. For fragmented big data obtained from different data sources, this paper adopts load-balancing technology to provide horizontal service cluster scalability and designs a separate system module for routine testing. The experimental results show that the improved big data processing system based on genetic algorithm in this paper can better meet the business needs of small and medium-sized enterprises and meet the application needs of small and medium-sized enterprises. The simulation results also show the advantages of the system in this paper, which is faster, higher accuracy, less energy consumption, lower requirements for equipment, and more suitable for small and medium-sized enterprises. This paper designs a kind of effective big data processing system by studying genetic algorithm and computer technology. [ABSTRACT FROM AUTHOR]

Enhancing the financial ecosystem encourages financial reform, stability, and the achievement of all-encompassing and sustainable development. Financial risk assessment is essential, because it affects not only a company's development but also the general economic progress of society. The backpropagation neural network (BPNN) was used in this study to create a proactive financial risk pre-alarm system by recognizing the critical role that risk assessment plays in promoting financial reform, stability, and sustainable development within the financial ecosystem. In this paper, we utilized the capabilities of the backpropagation neural network (BPNN) as a way of creating a proactive financial risk pre-alarm system by realizing the crucial significance of risk assessment in supporting financial reform, stability, and sustainable development within the financial ecosystem. First, data on assessing financial risk is collected, including earnings reports, economic indicators, political developments that have an impact on the economy, and some previous financial data. After the data have been acquired, it is preprocessed by being cleaned, having extra attributes removed, missing values removed, and normalized. An initial set of random parameters is used to create a BPNN architecture, which is then trained using a fraction of the collected data. Next, a genetic algorithm is employed to optimize the parameters of BPNN, including learning rate, neuronal count, hidden layer count, etc. The remaining data from the collection is then used to test the optimized BPNN, and its performance is assessed. The result of the developed model is carefully assessed and compared with other methodologies and with previously published works, showing that this method's accuracy is unquestionably superior to that of the other financial risk analysis algorithms. The mistake is reduced by 45.69%, and our model's accuracy is 97.94%, which is 21.32% greater than the accuracy of the comparison algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses an integrated material flow optimization problem of cross-docking satellites, in which the transportation problem, the truck-door assignment problem with material placement plans, and the two-dimensional truck loading problem are taken into account. The study aims to find the best distribution and operational plans for the cross-docking satellites to minimize the total transportation cost of the materials. To solve the considered problem, a hybrid genetic algorithm (HGA) is developed, which integrates simulated annealing (SA) algorithm within a genetic algorithm (GA). In this way, a new individual with a low solution quality is rejected by using the stochastic solution acceptance feature of the SA. Moreover, the HGA is enhanced with an advanced two-dimensional loading-check procedure and a rule-based material placement procedure to obtain efficient solutions. The proposed loading-check procedure reduces the processing time of the HGA by avoiding duplicate examinations for the truck loading plans. Likewise, the rule-based material placement procedure prevents unnecessary searches for the assignment plans of the products in a temporary storage area. In computational studies, the performance of the HGA is tested on two different problem sets by comparing HGA with the SA and GA. Furthermore, the HGA is applied to a problem set that is formed by using real-life data of a logistics company. The computational results show that the HGA introduces effective solutions and outperforms both the SA and GA. In addition, the results of the real-life application denote that the HGA can be employed to find effective material flow plans in real situations of cross-docking operations. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective optimization is a field that has experienced a rapid growth in the last two decades. Although an important number of new multi-objective evolutionary algorithms have been designed and implemented by the scientific community, the popular Non-Dominated Sorting Genetic Algorithm (NSGA-II) remains as a widely used baseline for algorithm performance comparison purposes and applied to different engineering problems. Since every evolutionary algorithm needs several parameters to be set up in order to operate, parameter control constitutes a crucial task for obtaining an effective and efficient performance in its execution. However, despite the advancements in parameter control for evolutionary algorithms, NSGA-II has been mainly used in the literature with fine-tuned static parameters. This paper introduces a novel and computationally lightweight self-adaptation mechanism for controlling the distribution index parameter of the polynomial mutation operator usually employed by NSGA-II in particular and by multi-objective evolutionary algorithms in general. Additionally, the classical NSGA-II using polynomial mutation with a static distribution index is compared with this new version utilizing a self-adapted parameter. The experiments carried out over twenty-five benchmark problems show that the proposed modified NSGA-II with a self-adaptive mutator outperforms its static counterpart in more than 75% of the problems using three quality metrics (hypervolume, generalized spread, and modified inverted generational distance). [ABSTRACT FROM AUTHOR]

Under the retailing environment, shelf space-based competition between items with different brands has drawn much attention from practitioners and researchers, the decision for low-temperature stored items on limited shelf spaces are compounding this challenge. This paper discusses the shelf display space partition assignment decision for low-temperature dairy items with two-phase demand in remaining shelf-life to maximize the profit of the shelf. Two-phase demand is assumed for the first time based on the knowledge that consumers' perception on the quality of the dairy item is unequal to its true shelf-life. Moreover, considering shelf partitions in different heights carry different sale gaining potentials, three profit weights are given to the partitions. Herein, a new research model named as SPSPA of shelf profit space partition assignment for the low-temperature stored dairy items is constructed. To effectively solve the proposed SPSPA, a hybrid firefly particle swarm optimization (HFPSO) is developed combing the superiority of particle swarm optimization (PSO) in coarse search and firefly algorithm (FA) in fine search. Numerical experiments have been performed on small- and large-scale cases to verify the performance of the proposed HFPSO algorithm by comparing with genetic algorithm, differential evolution (DE), PSO, and FA based on which management insights are obtained for the shelf space assignment decision. [ABSTRACT FROM AUTHOR]

This study explores the applicability of the metaheuristic algorithm for calculating the building period. Firefly algorithm (FA) employing the feed-forward (FF) model is applied to an existing period dataset of several wood buildings, whose motions were recorded at ambient conditions. The composition of the considered artificial neural network (ANN) is optimized employing the firefly algorithm, and the fixed weights are determined with the least error for the model. The model's precision is evaluated by comparing the results with the multiple linear regression (MLR) model, the ANN model combined with the genetic algorithm (GA-ANN), particle swarm optimization algorithm (PSO-ANN), and the models existing in building code. The results are also compared with the models presented in the literature for the period estimate required for the seismic design of wood buildings. The paper concludes that the proposed informational model can predict the fundamental period of light-frame wood buildings more accurately than the other available models. [ABSTRACT FROM AUTHOR]

Nowadays, automated guided vehicles (AGVs) play a key role in manufacturing systems because of improving system efficiency and lowering the cost of production. To increase the efficiency and stability of AGVs, it is crucial to consider maintenance planning for them. To the best of our knowledge, there are rarely found studies related to maintenance planning and AGVs' design and control. Accordingly, in this paper, a new integrated nonlinear mathematical model is developed for optimizing the AGV design (including AGV fleet sizing and AGV assignment to workshops) and preventive maintenance policy. The proposed model aims to determine the preventive maintenance cycles, the optimal number of employed AGVs in manufacturing, and the optimal assignment of AGVs to manufacturing workshops so that the total cost is minimized. To solve this model, a genetic algorithm (GA) is developed, and its performance is compared with the global solver of LINGO software on 15 test problems, some of which are large dimensions. To tune the GA parameters, a Taguchi method is used. Moreover, a sensitivity analysis is performed to represent the validity of the model and solution approach. The results have demonstrated the effectiveness of GA in terms of computational time and solution quality. [ABSTRACT FROM AUTHOR]

Meta-heuristics have been successfully applied to many complex optimization problems. One of the main reasons for its success is its ability to handle non-convex, nonlinear, multimodal, multi-variable, and multi-objective problems with easy implementation. However, the quality of the response of these algorithms to an optimization problem is highly susceptible to the control parameters, and few works aim to tune them or find tools that can improve the algorithms. The literature is rich in proposals for new algorithms, but not for improving existing ones. This paper presents different strategies for tuning and accelerating meta-heuristics using the first hybrid algorithm in the literature. The Lichtenberg algorithm is inspired by lightning and Lichtenberg figures and has been increasingly successfully applied to various optimization problems. However, a study of its best parameters has never been presented until now. After a discussion of the best tuning tools, its tuning parameters are performed using response surface methodology. Then, 14 versions are studied through 10 test functions using chaos theory and Lévy flights scenarios. After 13,500 simulations, the chaotic Lichtenberg algorithm equipped with the piecewise function and tuned parameters proved the best version with only 16% similarity to the original algorithm. Then, it was compared to the genetic algorithm, particle swarm optimization, gray wolf optimizer, salp swarm optimization, whale optimization algorithm, and dragonfly algorithm. The proposed algorithm had both the best average accuracy, lower computational cost, and the smallest standard deviation. [ABSTRACT FROM AUTHOR]

Many tasks generated by mobile user devices are computation-intensive and latency-sensitive, such as autonomous driving and video analysis. However, due to limited energy and computing capacity, a user device may not be able to complete its task within a given time, leading to a poor user experience. Mobile edge computing (MEC) can address this challenge by offloading tasks to edge servers with stronger computing capacity and more resources for execution, which can save energy of user devices and reduce the task computation time. Different offloading strategies will impact the number of tasks completed, latency, energy overhead and so on. This paper investigates the problem of maximizing the number of completed tasks while minimizing the average completion time, energy overhead and cost in MEC under both time and energy constraints. To solve the problem, we develop the mayfly genetic algorithm (MGA), which jointly optimizes task offloading locations and ratios, central processor unit (CPU) frequencies of user devices and computing capacities allocated to user devices by edge servers. Simulation experiments indicate that MGA outperforms state-of-the-art algorithms in terms of the number of completed tasks. [ABSTRACT FROM AUTHOR]

This paper considers a new meta-heuristic method called Grey Wolf optimizer (GWO). It is inspired by the behavior of grey wolves (Canis lupus) in nature, especially the leadership hierarchy and the hunting mechanism of a pack. This recent optimization method presents an interesting case of study given its promising performance in a variety of engineering fields. We are mainly focusing on the parameter identification problem given its crucial role in systems modeling and control. For this reason, the GWO is investigated to solve this kind of problem for an activated sludge process model used in wastewater treatment. The considered model is a nonlinear hybrid one. It has four unknown parameters to be identified. Simulation results are carried out and compared to other techniques: classical (Nelder–Mead method) and intelligent ones (Genetic Algorithm, Particle Swarm Optimization, Fireflies Algorithm, Cuckoo Search, and Teaching–Learning Based Optimization). The outcome of this comparison shows the satisfactory effectiveness and simplicity of the proposed method versus the others approaches. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid genetic-particle swarm optimization (GPSO) algorithm for solving multi-constrained optimization problems. This algorithm is different from the traditional GPSO algorithm, which adopts genetic algorithm (GA) and particle swarm optimization (PSO) in series, and it combines PSO and GA through parallel architecture, so as to make full use of the high efficiency of PSO and the global optimization ability of GA. The algorithm takes PSO as the main body and runs PSO at the initial stage of optimization, while GA does not participate in operation. When the global best value (gbest) does not change for successive generations, it is assumed that it falls into local optimum. At this time, GA is used to replace PSO for particle selection, crossover and mutation operations to update particles and help particles jump out of local optimum. In addition, the GPSO adopts adaptive inertia weight, adaptive mutation parameters and multi-point crossover operation between particles and personal best value (pbest) to improve the optimization ability of the algorithm. Finally, this paper uses a nonlinear constraint problem (Himmelblau's nonlinear optimization problem) and three structural optimization problems (pressure vessel design problem, the welded beam design problem and the gear train design problem) as test functions and compares the proposed GPSO with the traditional GPSO, dingo optimization algorithm, whale optimization algorithm and grey wolf optimizer. The performance evaluation of the proposed algorithm is carried out by using the evaluation indexes such as best value, mean value, median value, worst value, standard deviation, operation time and convergence speed. The comparison results show that the proposed GPSO has obvious advantages in finding the optimal value, convergence speed and time overhead. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for time-efficiently finding multiple answers that meet multiple logical constraints and are Pareto-optimal for multiple objective functions. The proposed framework determines a set of optimal answers by iteratively replacing a set of definite clauses. Clause replacement is performed using a SAT solver consisting of equivalent transformation rules (ETRs). An ETR replaces a definite clause with one or more clauses while preserving the declarative meaning of the union of the original clause and problem clauses. To efficiently find optimal answers, in this paper, we define a new class of ETRs that are generated based on the evaluation results of a multi-objective genetic algorithm (MOGA) and propose a method for generating ETRs that belong to the new class. ETRs belonging to the new class help to replace definite clauses according to user objectives such as cost–benefit performance, reliability, and financial constraints. Thus, a SAT solver that uses the new ETR class in addition to extant ETR classes can preferentially replace definite clauses that produce the optimal answer for user objectives. Experimental results indicate that the proposed framework can significantly reduce the computation time and memory usage necessary to determine a set of optimal answers for user objectives. [ABSTRACT FROM AUTHOR]

Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit parallelism in population-based search algorithms, to solve multiple coupled subsystems simultaneously in a complex system. The proposed MSGA is composed of within-subsystem evolution and cross-subsystem migration operators. The objective of the former is to optimize each subsystem by appropriate search strategies, and the objective of the latter is to exchange information between multiple subsystems by migration, which is based on the similarity probability of objectives and constraints, and the intersection probability of solutions in different subsystems. During migration across subsystems, three statistical approaches of measuring similarity and three metrics of solution intersection in information theory are used to calculate these probabilities. Performance is tested on a set of multi-subsystem benchmark functions, and the simulation results show that cross-subsystem migration plays the key role for the performance of MSGA. Furthermore, the proposed MSGA is compared with other competitive algorithms, and results show that it is a promising multi-system optimization algorithm. In summary, the contribution of this paper is the introduction of multi-system optimization to the EA community. [ABSTRACT FROM AUTHOR]

Computational intelligence (CI) has become one of the most useful and successful tools for dealing with uncertainties and complex problems in many fields, such as neural networks, genetic algorithms, and swarm intelligence, artificial intelligence, risk management, financial monitoring, etc. With the development of CI, abundant publications have arisen related to many research directions and hotspots. Based on the technical support from bibliometrics and the corresponding approach as well as the content analysis, this study conducts a science mapping analysis and a coherent knowledge picture of the research field in CI. The research contributes to clear future development trends and provides more ideas for scholars in this field. First, this paper focuses on the fundamental characteristics of CI publications, including annual numbers, term co-occurrence, and hot research directions, as the preliminary exploration of this field. Then, according to the widely used core database, i.e., Web of Science (WoS), and the technologies of software, VOS viewer, and CiteSpace, the productive institutions, authors, and journals are explored. Next, the corresponding internal characteristics of the CI research are analyzed, including the citation features of countries/regions, institutions, journals and authors. Furthermore, to analyze the development trend of research hotspots, the keywords of all CI publications are studied: (a) classifying them into three phases in chronological order aimed; (b) implementing the burst detection algorithm to intuitively reflect the scientific research in the field of CI. Finally, this paper provides a relatively throughout perspective for the CI articles and reviews and discloses the future development trend, which will help the scholars interested in this area conduct deep research. We conclude this bibliometric overview with the limitations and recommendations for future research in the field of CI. [ABSTRACT FROM AUTHOR]

The multi-objective design of suspension system for multi-axle heavy goods vehicle (HGV) is presented in this paper. Minimum road damage and goods damage are taken as design criteria for design. The objective function and constraint calculation in suspension design involve dynamic responses of the vehicle that has a high computational cost. Hence, a hybrid genetic algorithm (GA) is proposed for fast convergence. Proposed hybrid GA is designed using the selection method of two different genetic algorithms, i.e. non-dominated sorting genetic algorithm II (NSGA-II) and 2-objective geometry inspired genetic algorithm (2GIGA). Proposed hybrid GA is tested for different benchmarking optimization problems along with suspension optimization problems. Two types of suspension systems, passive and semi-active suspensions, are designed. A semi-active suspension system design involves designing a controller for a controllable damper along with passive elements. An improvised control law using a proportional integral derivative (PID) controller is used for the semi-active suspension system of multi-axle HGV. This control law is designed in such a way that it will eliminate the need for a passive damper in a semi-active suspension system. Results of the proposed hybrid GA are compared with the NSGA-II and 2GIGA. The proposed hybrid GA is performing better and saves significant computational cost for the suspension design of HGV. [ABSTRACT FROM AUTHOR]

This paper proposes an improved genetic algorithm (IGA) to provide feasible solutions to the technician routing and scheduling problem (TRSP). The TRSP covers the feasible team generation, the assignment of feasible teams to suitable tasks, the proficiency level of workers, routings considering the allowed days, and the skill desire of the task. The paper deals with a five-day multi-period planning horizon, and a task is performed in any one of 5 days. The IGA consists of crossover, mutation, and three neighborhood structures. Mutation and crossover largely try to avoid getting caught in the local solution trap. Three neighborhood structures improve genetic algorithm (GA) by searching for better solutions. Further, the performance of the proposed algorithm is experimentally compared with GA and improved particle swarm optimization (IPSO) algorithm by providing the TRSP solutions on the generated benchmark instances. The numerical results indicate that IGA offers fast and better solutions considering GA and IPSO algorithms. [ABSTRACT FROM AUTHOR]

As natural disasters often cause catastrophic damages, disaster management must be considered. Relief logistics planning in disaster management is a dynamic process that includes deliberate processes taken before and after a disaster. This paper presents a robust optimization model for relief logistics planning covering before and after disaster actions to minimize the total cost. In this model, demand and traveling time between nodes are non-deterministic parameters, and scenario-based robust optimization is applied to handle uncertainty. The model is an integrated multi-echelon, multi-period, and multi-commodity vehicle routing problem considering the fair distribution of goods and services in a way that social costs are minimized. Vehicles can enter nodes and exit them several times. In this paper, a genetic algorithm (GA) is considered the solution approach. To estimate the GA parameters, an experimental design based on the Taguchi method is considered. A roulette wheel method is applied to select a child from parents for the next generation. Single-point crossover and swap mutation operators are used to generate the new child. Test problems in different scales are generated and solved using GAMS and GA. The results indicate that GA solutions have 3.75% gaps in average with optimal solutions. Finally, the model is applied with the data of a real case (Tehran). The results also indicate that it is better to consider the appropriate capacity for supply centers and distributors and select vehicles. [ABSTRACT FROM AUTHOR]

Most genetic operators use random mating selection strategy and fixed rate crossover operator to solve various optimization problems. In order to improve the convergence and diversity of the algorithm, an adaptive adjacent maximum distance crossover operator is proposed in this paper. A new mating selection strategy (distance-based mating selection strategy) and an adaptive mechanism (adaptive crossover strategy based on population convergence) are adopted. Distance-based mating selection strategy purposefully selects parents to produce better offspring. Adaptive crossover strategy based on population convergence increases the convergence speed of the algorithm by controlling the crossover probability. The proposed crossover strategy is evaluated on the simulated binary crossover operators of non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. The performance of the algorithm is verified on a series of standard test problems. Finally, the optimization results of the improved algorithm using adaptive adjacent maximum distance crossover operator and the standard algorithm are compared and analyzed. The experimental results show that the algorithm using adaptive adjacent maximum distance crossover operator has better optimization results. [ABSTRACT FROM AUTHOR]

The influence of distributed generation sources in the Islanded microgrid has adverse effects on frequency stability because of the absence of inertia. Accordingly, the concept of a virtual synchronous generator (VSG) has been proposed, which follows the behavior of conventional synchronous generators. The virtual inertia control can be implemented on energy storage systems (ESSs) as a part of a virtual synchronous generator. Virtual inertial control does not perform optimally against the uncertainty of islanded microgrid parameters and disturbances. Therefore, virtual inertial control requires a supplementary controller in its structure that is resistant to these effects. In this paper, a new robust output feedback control method was used to control the virtual inertia in a two-area microgrid, which is based on the linear matrix inequality. The proposed control method does not need to measure all modes and only uses the output feedback. We compared the results of the proposed controller for controlling the virtual inertia in multiple different scenarios by considering the uncertainty of the two-area microgrid parameters as well as disturbances applied to the microgrid with the virtual inertia control based on optimized PI controllers (genetic algorithm), no controller (virtual inertia), and without considering virtual inertia (conventional methods). Accordingly, the effectiveness of the proposed method was demonstrated in terms of response speed, reduction of the frequency deviations and tie-line power deviations, robustness against parameters' uncertainty, and disturbances occurring to the two-area microgrid. [ABSTRACT FROM AUTHOR]

The transportation problems, which consist of multiple objectives with heterogeneous conveyances, are the pragmatic representation of the transportation occurring in the real world. However, situations do exist where the solutions obtained by the classical optimization techniques do not reflect the acumen of the decision-maker. Against this backdrop, a systematic algorithm is proposed in this paper to generate an initial population to tackle the multi-objective solid transportation problem efficiently. In addition, the non-dominated sorting genetic algorithm (NSGA) III is carried out to acquire solutions that can demonstrate the varying degrees of objectives. Furthermore, two problems of different sizes are framed, and their performance is compared via NSGA II, hybrid genetic algorithm, and fuzzy programming technique. [ABSTRACT FROM AUTHOR]

In this paper, a new U-shaped assembly line balancing problem is studied. For the first time, the criteria such as equipment cost, number of stations and activity performing quality level are considered to be optimized simultaneously by activity to station and worker to station decisions. For this aim, a multi-objective nonlinear formulation is proposed and its linearized version is also presented. Since, according to the literature, the U-shaped assembly line balancing problem with equipment requirements is an NP-hard problem, the problem of this study is NP-hard too. Because of this complexity, the classical algorithms like simulated annealing, variable neighborhood search, and classical genetic algorithm with a novel encoding/decoding scheme are used as solution approaches. As an extension, two hybrid versions of the proposed classical algorithms are proposed according to the characteristics of the problem. In order to evaluate the proposed meta-heuristics, because the problem is new, some test problems are generated randomly. Computational study of the paper, including sensitivity analysis of the proposed meta-heuristics and final experiments on the test problems, proves the superiority of the hybrid versions of the classical algorithms. [ABSTRACT FROM AUTHOR]

Students' performance prediction systems play a vital role in enhancing the educational performance inside universities, schools, and training centers. Big data can come from different resources such as examination centers, virtual courses, registration departments, e-learning systems. Extracting meaningful knowledge from educational data is a complex task, so reducing the data dimensionality is needed. In this paper, we proposed an enhanced binary genetic algorithm (EBGA) as a wrapper feature selection algorithm. Novel hybrid selection mechanism based on a k-means algorithm and electromagnetic-like mechanism (EM) method is proposed. K-means will cluster the population into a set of clusters, while EM will determine a value called a total force (TF) for each solution. Each cluster has an accumulated total force (ATF) (i.e., adding all TFs together). Selection process will select two solutions with the highest TF from the cluster, which has the highest ATF. We employed a hybrid machine learning approach between the proposed EBGA and five different classifiers (i.e., k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)). Two real case studies obtained from UCI Machine Learning Repository are used in this paper. Obtained results showed the ability of the proposed approach to enhance the performance of the binary genetic algorithm. Moreover, the performances of all classifiers are improved between 1 % and 11 % . [ABSTRACT FROM AUTHOR]

Developing fault diagnosis for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. This paper mainly studies and explores the compound fault diagnosis for the cooling dehumidifier. Firstly, the dehumidifier data acquisition system is built, which can be applied to the data acquisition, work status simulation and fault diagnosis. Secondly, a compound fault diagnosis model based on radial basis function neural network (RBFNN) improved by kernel principle component analysis (KPCA) and adaptive genetic algorithm (AGA) is proposed. Aiming at the problems that the selection of RBF width depends on expert experience and knowledge, the network structure scale is large or the training speed is slow in the conventional RBFNN models; on the one hand, AGA and K-means clustering algorithm are employed to automatically optimize the RBF width, the number of hidden layer neurons and the neuron centers, which guarantees the model has small structure and fast running speed on the premise of sufficient output precision, and the local optimization problem of K-means clustering algorithm is well suppressed by AGA group calculation; this also ensures the performance of RBFNN; on the other hand, KPCA is employed to reduce the dimension of the model input data, which not only effectively extract the nonlinear features, but also further simplify the network structure. Finally, the proposed method is validated and compared with the conventional models. The results show that this proposed model can not only be effectively applied to the dehumidifier compound fault diagnosis, but also has prominent application advantages. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported product quantities and the facility locations in the Euclidean plane. Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA) are utilized to solve the problem. We compared obtained compromise solutions of the problem by evolutionary algorithms with population size, the maximum number of generations, crossover probability, mutation probability, and computational time. Sensitivity analysis for supply, demand, and carbon cap parameters is incorporated in the model's solution, which helps the decision maker make the appropriate decision. These evolutionary algorithms (NSGA-II and NSGA-III) give Pareto-optimal solutions, and it helps management decide on the selection of p-facility locations. They could transport their product to the facility locations easily, with minimum transport, CE costs, and transportation time. As a result, management can give equal attention to their profit and environment, which helps to build world market credibility. At last, the paper concludes the results. [ABSTRACT FROM AUTHOR]

Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually have a structured manifold implicitly hidden in the data and its labels. In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection. The first scheme is a wrapper technique, while the other two schemes are filtering techniques. In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-of-the-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods. [ABSTRACT FROM AUTHOR]

Recommender system plays an increasingly important role in identifying the individual's preference and accordingly makes a personalized recommendation. Matrix factorization is currently the most popular model-based collaborative filtering (CF) method that achieves high recommendation accuracy. However, similarity computation hinders the development of CF-based recommendation systems. Preference obtained only depends on the explicit rating without considering the implicit content feature, which is the root cause of preference bias. In this paper, the content feature of items described by fuzzy sets is integrated into the similarity computation, which helps to improve the accuracy of user preference modeling. The importance of a user is then defined according to preferences, which serves as a baseline standards of the core users selection. Furthermore, core users based matrix factorization model (CU-FHR) is established, then genetic algorithm is used to predict the missing rating on items. Finally, MovieLens is used to test the performance of our proposed method. Experiments show CU-FHR achieves better accuracy in prediction compared with the other recommendation methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel fault detection system by the combination of Support Vector Data Description and Squirrel Search Algorithm. This approach is capable to deal with processes or machines where the number of fault observations is small or not even available for training phase. In this work the use of classic Support Vector Data Description as well as its fast version with two kernel functions is proposed. The experimental results showed that the proposed system exhibits suitable capabilities for fault detection in complex industrial processes such as the one presented in this research. Moreover, a nonparametric statistical analysis is also included in order to compare the considered strategies and enhance the efficiency of the presented approach. Finally, a comparison with genetic algorithm approach and the one-class classifier based on support vectors is carried out which shows that the proposed algorithm outperforms traditional techniques. [ABSTRACT FROM AUTHOR]

This paper addresses a sustainable cement supply chain network problem for an Indian city (Srinagar). The major problem addressed in this research is the dilemma of several sites and selecting sites to set up distribution centers and retail centers for cement supply chain networks. The primary goal of this research work is to focus on sustainable product delivery to clients, which leads us to the topic of facility site selection. The quality of a facility's location has a direct impact on the company's output and operations. As a result, businesses need to develop scientific and effective methodologies for assessing facility location issues and making informed decisions. The conundrum of several sites and picking places to set up distribution centers and retail centers for cement supply chain networks is the key challenge addressed in this research. This research focuses on the current state of the location problem, with an emphasis on the mathematical model and a genetic algorithm in the analysis and analytical approach of the location problem. In this regard, a sustainable supply chain network model has been developed. A real-coded genetic algorithm-based solution approach has been developed for optimizing the sustainable supply chain network design for Srinagar city. Using the input data as a search space for the solving procedure, the resulting solution will provide us with the ideal site selection points. A model for a long-term supply chain network has been established in this regard. For optimizing the sustainable supply chain network architecture for Srinagar city, a real-coded genetic algorithm-based solution strategy has been created. A real-life case study of a cement supply chain network has been considered for validation of the proposed model. [ABSTRACT FROM AUTHOR]

The operational law proposed by Zhou et al. (J Intell Fuzzy Syst 30(1): 71–87, 2016) contributes to developing fuzzy arithmetic, while its applicable conditions are confined to strictly monotone functions and regular LR fuzzy numbers, which are hindering their operational law from dealing with more general cases, such as problems formulated as monotone functions and problems with fuzzy variables represented as fuzzy intervals (e.g., trapezoidal fuzzy numbers). In order to handle such cases we generalize the operational law of Zhou et al. in both the monotonicity of function and fuzzy variables in this paper and then apply the extensive operational law to the cases with monotone (but not necessarily strictly monotone) functions with regard to regular LR fuzzy intervals (LR-FIs) (of which regular LR fuzzy numbers are special cases). Specifically, we derive the computational formulae for expected values (EVs) of LR-FIs and monotone functions with regard to regular LR-FIs, respectively. On the other hand, we develop a solution scheme to dispose of fuzzy optimization problems with regular LR-FIs, in which a fuzzy programming is converted to a deterministic equivalent one and a newly devised solution algorithm is utilized to get the deterministic programming solved. The numerical experiments are conducted using our proposed solution scheme and the traditional fuzzy simulation-based genetic algorithm in the context of a purchasing planning problem. Computational results show that our method is much more efficient, yielding high-quality solutions. [ABSTRACT FROM AUTHOR]

The objective of a fixed-cost transportation problem (FCTP) is to minimize the total transportation cost (or to maximize the total profit). FCTPs become more complex due to the presence of uncertain factors like suppliers' capacity, customers' demand, fixed cost and amount of goods transported. In order to deal with its complexity, three mathematical models, namely the expected value, chance-constrained and measure chance models, are basically available in the literature. This paper aims to solve the crisp equivalent models of an uncertain two-echelon FCTP by employing a robust hybrid evolutionary algorithm. The framework of the designed algorithm comprises of parameter-free Jaya algorithm and the efficient quadratic approximation operator. Initially, its efficiency is validated on the 24 constrained benchmark functions from CEC 2006 (Liang et al. in Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization, 2006) and 30 unconstrained benchmark functions (Rao in Int J Ind Eng Comput 7:19–34, 2016). Friedman rank test and Iman–Davenport test are conducted for both constrained and unconstrained test instances. Additionally, five real-life engineering design problems are solved to further analyze the flexibility of the proposed algorithm. The computational results and convergence graphs justify the robustness of the designed algorithm over state-of-the-art algorithms. Finally, its application is demonstrated on chance-constrained and measure chance two-echelon FCTP models. Near-optimal results are contradicted by genetic algorithm and particle swarm optimization in solving both these models. Further, a few large-scale instances of FCTP are solved by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models. [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]

Research on multi-objective discrete optimization of Waste Electrical and Electronic Equipment (WEEE) remanufacturing remains under-studied in the literature. Remanufacturing laptops to extend their useful life is viewed as the best End-Of-Life (EOL) alternative considering environmental and social factors. This paper develops a model to decide the best EOL option, namely reuse, conditional repair, and disposal of quality-graded laptop parts, with economic and environmental objectives. The first objective is to maximize the profit of remanufactured laptops over a multi-period planning horizon. The second objective is to minimize the emissions associated with remanufacturing. A Multi-Objective Discrete Particle Swarm Optimization (MODPSO) algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are embedded as a decision support tool in Microsoft Excel with a user interface to yield Pareto optimal solutions, and the results are compared. The Taguchi approach is applied to find the optimum value of the control parameters of the proposed algorithms. The approach is tested with inputs from an authorized remanufacturer in Bangalore, India. The performance of the algorithms is further investigated using randomly generated test problems. The MODPSO algorithm provided better solutions for all problem instances based on the convergence and diversity metrics. The inclusion of conditional repair options and parts with low and medium-quality grades in remanufacturing leads to higher profit, albeit with more emissions. A variation in the quality grade assigned to the conditional repair option for the parts needed for higher profit margin laptops is observed. A sensitivity analysis is conducted to observe the impact of supply, demand, repair cost, shortage cost, and emissions on the two extreme Pareto solutions. The decision-making tool offers a continuum of trade-offs to help a remanufacturer choose the EOL options, depending on economic and environmental performance preferences. [ABSTRACT FROM AUTHOR]