Search

To drive a paradigm shift towards a human-centric approach in remanufacturing systems and promote energy-efficient, carbon-reduction-friendly upgrades, this paper introduces a stochastic human–robot collaborative disassembly line balancing problem for real-world disassembly lines characterized by uncertain operation times. The developed mathematical model takes into account task priority relationships, task attributes, human–robot collaboration, and cycle time constraints, with the goal of optimizing workstation numbers, smoothing indices, and carbon emissions. Subsequently, we propose a hybrid evolutionary algorithm (HEA) to efficiently address this problem. The algorithm includes methods for generating initial solutions and decoding them based on random disassembly time. Additionally, an enhanced differential evolution operation is integrated into the non-dominated sorting genetic algorithm II (NSGA-II) framework to improve the algorithm's evolutionary and search capabilities. Validation experiments involving 21 benchmark cases demonstrate that HEA significantly outperforms seven other algorithms reported in the existing literature. Finally, the proposed algorithm is applied to a printer case comprising 55 components. The results underscore the algorithm's superior search capabilities compared to the innovative hybrid algorithm, genetic simulated annealing algorithm, as well as traditional NSGA-II and evolutionary algorithms, under both low and high randomness conditions. • The carbon emission evaluation index was developed for HRC-DLBP. • Incorporating product- and operator-induced uncertainty into HRC-DLBP. • Developed a multi-objective hybrid evolutionary algorithm for SHRC-DLBP. • Apply the proposed method to the discarded printer disassembly case. [ABSTRACT FROM AUTHOR]

Investing in the stock market involves high risks and equivalent returns. Many investors tend to be loss averse and suffer significant losses due to information asymmetry and emotion-based investing. In order to minimize such losses, various studies are attempting to predict the stock market's future and automate investment decisions. However, the stock market is affected by many factors and poses a challenge in prediction. Also, many researchers who develop stock trading systems are interested primarily in improving prediction model performance. Particularly, most studies set up-down labels in the data labeling phase. However, up-down labeling views all stock price fluctuations as identical, resulting in inefficient model training by including substantial noise in the learning data. Therefore, this study proposes SGA (Selective Genetic Algorithm) labeling to overcome such concerns. A trading system was developed using the proposed method, and a machine learning model and ensemble method supported the label prediction phase. The proposed trading system was analyzed empirically with 38 NASDAQ stocks and 33 KOSPI stocks, and a comparative experiment with up-down labeling method was conducted. In this study, SGA labeling performance exceeded that of up-down labeling. Additionally, instance selection and ensemble methods affected the trading system's performance. [ABSTRACT FROM AUTHOR]

During the past two decades, evolutionary algorithms have seen great achievements in solving complex optimization problems owing to the advantages brought by their properties, especially constrained multi-objective optimization problems (CMOPs) with multiple conflicting objective functions and constraints which widely exist in industry, scientific research, and daily life. Among the real-world CMOPs, mechanical design problems (MDPs) from the industry widely exist and are important, while unfortunately, most constrained multi-objective evolutionary algorithms (CMOEAs), developed based on benchmark CMOPs, neglect the specific features and challenges of MDPs and thus cannot solve them well to provide the practitioners promising Pareto optimal solutions for decision making. To overcome this limitation, this paper analyzes the features and challenges of MDPs, including badly scaled objective space, decision space properties, and decision variable linkages. Then, we propose a new CMOEA named CMORWMDP. First, instead of the homogeneous operator in existing CMOEAs, a heterogeneous operator strategy is adopted to use the operator of Genetic Algorithm to enhance the convergence and the operator of Differential Evolution to tackle variable linkages. In addition, an improved fitness function that considers normalization is designed for environmental and mating selections. The proposed algorithm is simple, parameter-free, and easy to implement. Experiments on 21 real-world MDPs show its superiority compared to 20 state-of-the-art CMOEAs under the Friedman test and Wilcoxon test on different metrics, demonstrating the effectiveness of the heterogeneous operator and normalization-based fitness for selections for real-world MDPs. Moreover, the effectiveness of the proposed algorithm in solving other real-world CMOPs is also verified, revealing that our methods are very promising in tackling real-world problems. • Challenges of real-world constrained multi-objective mechanical design problems. • Heterogeneous operator strategy to tackle the challenges. • Improved fitness function with normalization. • A parameter-free constrained multi-objective optimization evolutionary algorithm. [ABSTRACT FROM AUTHOR]

The distributed manufacturing mode is an important paradigm of networked development in the context of globalization. The integration of process planning and shop scheduling in distributed discrete manufacturing system can fully use the production system's flexibility, which is helpful to further enhance the anti-risk ability of enterprises, improve production efficiency, and reduce production costs. This paper established the mathematical model of the distributed integrated process planning and scheduling (DIPPS) in heterogeneous manufacturing environment and designed a high-dimensional integrated encoding to deal with the coupling problem of multiple sub-problems of the DIPPS. Based on this encoding method, an improved genetic algorithm (IGA) is proposed to solve the DIPPS problem. According to the characteristics of the DIPPS, a hierarchical crossover operator with three sub-crossover operations is designed to expand the search dimension of the IGA. The mutation operators are planned to maintain population variety. Finally, five sets of experiments are planned to test the viability of the suggested approach for resolving the DIPPS problem. The outcomes demonstrate that the IGA algorithm outperforms competing algorithms in various scale jobs and factories. [ABSTRACT FROM AUTHOR]

Pneumonia is a disease that can be detected by the opacity changes in chest X-rays and can lead to fatal consequences. Medical image analysis has several challenges, such as limited labeled datasets, imbalanced class distribution, image noise, and overfitting, so individual Convolutional Neural Networks (CNNs) are insufficient to detect pneumonia accurately. Although ensemble CNN models have been used in previous studies, the literature lacks guidance on identifying the optimal CNN models and weight ratio to combine them. In this study, we propose a novel ensemble CNN framework to accurately detect pneumonia, with optimum weights set by a Genetic Algorithm (GA). Firstly, a noise outside the lung was removed, and the model performance was enhanced by performing lung segmentation on Chest X-ray. The performances of several CNN models were analyzed by hyperparameter optimization. The framework combines the three models that give the best accuracy and the two models that provide the lowest false-negative value with the ensemble method in the ratio of the most appropriate weights. The proposed framework provided the best performance on the public test dataset with an accuracy of 97.23% and an F1-score of 97.45% compared to state-of-the-art methods. The study's main contributions are determining suitable models and their optimal weights of the ensemble method based on the GA. The proposed framework enables a rapid and effective diagnostic process, less costly healthcare services, and more efficient use of resources. The demo-link is https://www.youtube.com/watch?v=KZ50K3HL70U. • A novel ensemble CNN framework is proposed. • Ensemble model weights were optimized with the Genetic Algorithm. • CNN models with different architectures were optimized. • An algorithm is presented to remove noise from the images. • Pneumonia was detected with high accuracy. [ABSTRACT FROM AUTHOR]

This study proposes the combined use of a phenomenological model and neural networks trained by experimental data to predict Carbon dioxide adsorption on Zeolitic imidazolate framework – 8 (ZIF-8) considering different temperature and surface area conditions. The Sips model presented the best fit among the models studied and the perceptron networks presented the best performance to represent the phenomenon. The use of genetic algorithms assisted in the optimization of the neural network architecture, in a shorter computational time. The model indicates that at high pressures the area presents a strong positive correlation for carbon capture, however at lower pressures (of the order of 1 bar) the influence was negative, but with less significance. The study contributes to the development of more efficient carbon capture processes, helping to direct future research in the area of greenhouse gas mitigation, particularly in the field of developing products for carbon capture, among other applications involving carbon dioxide adsorption. [Display omitted] • Combination of phenomenological and neural networks modeling. • A novel approach to model CO 2 adsorption on ZIF-8. • The model includes a wide range of temperature and surface area. • Especially at high pressures, surface area had a positive influence on adsorption. • The developed model reached R2 of 0.9996 and MAE of 0.0303. [ABSTRACT FROM AUTHOR]

Energy-efficient routing protocols for Underwater Wireless Sensor Networks (UWSNs) have become critical in recent years for the intelligent and reliable collection of data from the seas and oceans. UWSNs are a group of deep-water sensors that are used for marine exploration and ocean surveillance. This network is used to route data collected by sensors deployed at different water depths to surface water sensors (sinks). Transmitting the collected data from the ocean's depths to the surface is difficult due to the limited available bandwidth, inconvenient location, high mobility of the sensors, and, most importantly, limited energy. The purpose of this paper is to present a routing protocol that improves the reliability of data transmission from a source sensor to a destination sensor. A hybrid metaheuristic algorithm called GSLS is proposed to use in this protocol, which combines a Global Search Algorithm (GSA) and a Local Search Algorithm (LSA). The proposed GSA is an algorithm inspired by several of the Genetic Algorithm's (GAs) solution updating properties. The proposed LSA algorithm is an extension of the scattered search algorithm. The proposed GSA and LSA are combined in parallel to search the problem's space simultaneously to find an optimal path in an acceptable time. as a result, more problem area is examined, and the algorithm's run time to find the best route is reduced. Our simulation results emphasize the high quality of the path, the algorithm's low energy consumption, and the algorithm's high speed in comparison to the state-of-the-art. [ABSTRACT FROM AUTHOR]

In this article, a Multi-skilled Resource-Constrained Flexible Flow Shop Scheduling Problem (MSRC-FFSSP) is introduced. To process each job at every machine within this problem category, it is imperative to possess at least one set of multi-skilled human resources. Each human resource can operate a minimum of 1 and a maximum of N jobs depending on their skills and each job requires a minimum set of different skills. For this problem, the model is formulated as a MILP model with two objectives: i) minimizing the total completion time and ii) minimizing the total idle time for human resources. Moreover, a case study in a preventive maintenance environment as a flow shop is performed to establish the applicability of the model in real-world problems. Given that the problem is NP-Hard, a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm in which the related parameters are tuned by the Taguchi method are presented. Finally, numerical results have been presented to verify the model, in addition to the analysis of the idle time of human resources. Additionally, to solve this problem, it was shown that the GA has better efficiency and performance compared to the SA algorithm. However, the SA algorithm outperforms the GA in terms of running time. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of electrical power output (P e) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy P e. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R 2), mean absolute error (M A E), mean square error (M S E), root mean square error (R M S E), mean absolute percentage error (M A P E), Kling–Gupta Efficiency (K G E), and Bland–Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE Y V E − 2 achieves the highest (P e) accuracy, including R 2 = 0. 9368 , M A E = 3. 3378 , M S E = 18. 4800 , R M S E = 4. 2985 , M A P E = 0. 7354 % , and K G E = 0. 9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper's outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating P e in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology. • GP symbolic expressions can estimate electrical power output of CCPP. • Symbolic expressions with AT-V, AT-AP, AT-RH, AT-V-AP, AT-V-RH, AT-AP-RH, AT-V-AP-RH as input variables are most accurate. • B-A analysis, R2, MAE, MSE, RMSE, MAPE, and KGE metrics used as filter of large number of expressions. • Lower estimation errors are achieved with averaging ensemble. [ABSTRACT FROM AUTHOR]

The fitness evaluation mechanism (FEM) based on nondominated sorting may lead to slow convergence when solving large-scale many-objective optimization problems (LSMaOPs), because the number of comparisons will become extremely large with the increase of optimization objectives and iterations. To solve this problem, a novel FEM based on distance and cosine similarity (DCS) is proposed in this paper. In each iteration, DCS needs to generate an ideal point after normalizing all objective functions. DCS consists of two important components, i.e., the distance and cosine similarity. The distance similarity that mines the similar relationship between solutions and ideal point is calculated as the convergence measure, and the cosine similarity that reflects the uniformity of solution distribution is calculated as the diversity measure. Furthermore, DCS fuses the distance and cosine similarity into a comprehensive similarity to fully evaluate the quality of solutions. Both theoretical analysis and empirical results indicate that DCS has lower computational complexity than other state-of-the-art FEMs. To verify the performance of DCS in solving LSMaOPs, DCS and the competitors are respectively embedded in genetic algorithm, and then compared on 56 test instances with 5–15 objectives and 100–1000 decision variables. The experimental results show the effectiveness and superiority of DCS. [ABSTRACT FROM AUTHOR]

Searching for a near-optimal highway alignment is essential to achieve safe, efficient, and cost-effective transportation infrastructure. The challenges with the traditional design approach lie in its reliance on geometric elements which may lead to alignments that are complex, financially burdensome, and environmentally disruptive despite exploring a wide search area. This paper presents a novel and intelligent approach for highway alignment optimization, focusing on the development of the alignment using station points within a reduced domain. The approach involves utilizing a pre-generated Least Cost Path from a Geographic Information System (GIS) model, serving two purposes: first, determining the reduced search domain, and second, using it as input for genetic algorithms (GAs) until a near-optimal alignment is achieved. This approach simplifies the design process, reduces costs, and yields alignments that better harmonize with various relevant factors. The integrated GIS-GA model of this study helped reduce the whole search space to a confined tubular square corridor (CTSC), where the best alternative exists. Additionally, the method introduced a new perspective on highway alignment design, challenging conventional practices. The model is validated using comprehensive and sensitive scenarios for key parameter values. The results reveal that the search time is significantly reduced and that better solutions are located in real-time. This methodology has the potential to revolutionize the way transportation engineers and planners approach highway alignment projects, resulting in more efficient, well-informed, cost-effective, and context-sensitive designs. Further studies are highly recommended to fully explore the implications and applications of the proposed methodology. • Highway alignment development concerns researchers, aiming for optimal solutions, reducing time/cost, and lowering construction expenses. • This project reports the reduction of the search space from the whole to a CTSC, where optimal highway alignment exists in 3D space. • A problem oriented GIS model was integrated with a Genetic Algorithm Model into a single one to address the development of an optimum highway alignment solution in real time. • The resulting alignment was configured via a novel approach using station points along its length in 3D, rather than traditional highway design elements. • The resulting optimum highway alignment was promising, validating space reduction, cutting processing time, and improving solution quality. [ABSTRACT FROM AUTHOR]

Electric double layer capacitors (EDLCs) are promising energy storage solutions, yet aging and degradation issues impede reliability and lifespan. The research proposes integrated simulation, modeling, and optimization to actively control EDLC degradation during charge-discharge cycles, mathematically modeling and simulating electrical and aging dynamics. These aging simulations are coupled with a genetic algorithm (GA) optimization routine that identifies the optimal combinations of influential EDLC parameters like internal resistance and capacitance for mitigating deterioration. Equivalent circuit models quantify electrical signatures, as aging factors induce gradual drifts in capacitance and resistance during thousands of simulated operational cycles. These MATLAB simulations effectively capture aging phenomena noted in real EDLCs in terms of measurable capacitance fading and resistance growth trends over continual usage in line with experimental data. The GA optimization subsequently determines optimal charging voltage ranges and achievable reductions in charge/discharge asymmetry by over 70 % that significantly enhance lifespan trajectories through aging control under similar test conditions. The technique's efficacy is further ascertained through systematic tuning of GA parameters like mutation rates and population sizes using Taguchi experimental models. The findings showcase superior optimization outcomes for larger populations and lower mutation probabilities. The research integrates digital twin for rapid evaluations, addressing reliability challenges via computational aging control. The flexible modeling platform enables customized what-if analyses for EDLC designers, aiding in material, cycling, and duty cycle exploration. This aging mitigation approach offers simulation-driven insights and automated optimization tools to extend the operational duration of high-performance EDLCs. • Development of simulation approach due to control aging and degradation of EDLCs. • The application of GA for optimization of aging/degradation process in EDLCs. • Tuning the GA computations utilizing Taguchi design model. • Sensitive analysis of the most significant features by simulation approaches. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of electrical power output (P e) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy P e. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R 2), mean absolute error (M A E), mean square error (M S E), root mean square error (R M S E), mean absolute percentage error (M A P E), Kling–Gupta Efficiency (K G E), and Bland–Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE Y V E − 2 achieves the highest (P e) accuracy, including R 2 = 0. 9368 , M A E = 3. 3378 , M S E = 18. 4800 , R M S E = 4. 2985 , M A P E = 0. 7354 % , and K G E = 0. 9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper's outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating P e in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology. • GP symbolic expressions can estimate electrical power output of CCPP. • Symbolic expressions with AT-V, AT-AP, AT-RH, AT-V-AP, AT-V-RH, AT-AP-RH, AT-V-AP-RH as input variables are most accurate. • B-A analysis, R2, MAE, MSE, RMSE, MAPE, and KGE metrics used as filter of large number of expressions. • Lower estimation errors are achieved with averaging ensemble. [ABSTRACT FROM AUTHOR]

The fitness evaluation mechanism (FEM) based on nondominated sorting may lead to slow convergence when solving large-scale many-objective optimization problems (LSMaOPs), because the number of comparisons will become extremely large with the increase of optimization objectives and iterations. To solve this problem, a novel FEM based on distance and cosine similarity (DCS) is proposed in this paper. In each iteration, DCS needs to generate an ideal point after normalizing all objective functions. DCS consists of two important components, i.e., the distance and cosine similarity. The distance similarity that mines the similar relationship between solutions and ideal point is calculated as the convergence measure, and the cosine similarity that reflects the uniformity of solution distribution is calculated as the diversity measure. Furthermore, DCS fuses the distance and cosine similarity into a comprehensive similarity to fully evaluate the quality of solutions. Both theoretical analysis and empirical results indicate that DCS has lower computational complexity than other state-of-the-art FEMs. To verify the performance of DCS in solving LSMaOPs, DCS and the competitors are respectively embedded in genetic algorithm, and then compared on 56 test instances with 5–15 objectives and 100–1000 decision variables. The experimental results show the effectiveness and superiority of DCS. [ABSTRACT FROM AUTHOR]

Searching for a near-optimal highway alignment is essential to achieve safe, efficient, and cost-effective transportation infrastructure. The challenges with the traditional design approach lie in its reliance on geometric elements which may lead to alignments that are complex, financially burdensome, and environmentally disruptive despite exploring a wide search area. This paper presents a novel and intelligent approach for highway alignment optimization, focusing on the development of the alignment using station points within a reduced domain. The approach involves utilizing a pre-generated Least Cost Path from a Geographic Information System (GIS) model, serving two purposes: first, determining the reduced search domain, and second, using it as input for genetic algorithms (GAs) until a near-optimal alignment is achieved. This approach simplifies the design process, reduces costs, and yields alignments that better harmonize with various relevant factors. The integrated GIS-GA model of this study helped reduce the whole search space to a confined tubular square corridor (CTSC), where the best alternative exists. Additionally, the method introduced a new perspective on highway alignment design, challenging conventional practices. The model is validated using comprehensive and sensitive scenarios for key parameter values. The results reveal that the search time is significantly reduced and that better solutions are located in real-time. This methodology has the potential to revolutionize the way transportation engineers and planners approach highway alignment projects, resulting in more efficient, well-informed, cost-effective, and context-sensitive designs. Further studies are highly recommended to fully explore the implications and applications of the proposed methodology. • Highway alignment development concerns researchers, aiming for optimal solutions, reducing time/cost, and lowering construction expenses. • This project reports the reduction of the search space from the whole to a CTSC, where optimal highway alignment exists in 3D space. • A problem oriented GIS model was integrated with a Genetic Algorithm Model into a single one to address the development of an optimum highway alignment solution in real time. • The resulting alignment was configured via a novel approach using station points along its length in 3D, rather than traditional highway design elements. • The resulting optimum highway alignment was promising, validating space reduction, cutting processing time, and improving solution quality. [ABSTRACT FROM AUTHOR]

Electric double layer capacitors (EDLCs) are promising energy storage solutions, yet aging and degradation issues impede reliability and lifespan. The research proposes integrated simulation, modeling, and optimization to actively control EDLC degradation during charge-discharge cycles, mathematically modeling and simulating electrical and aging dynamics. These aging simulations are coupled with a genetic algorithm (GA) optimization routine that identifies the optimal combinations of influential EDLC parameters like internal resistance and capacitance for mitigating deterioration. Equivalent circuit models quantify electrical signatures, as aging factors induce gradual drifts in capacitance and resistance during thousands of simulated operational cycles. These MATLAB simulations effectively capture aging phenomena noted in real EDLCs in terms of measurable capacitance fading and resistance growth trends over continual usage in line with experimental data. The GA optimization subsequently determines optimal charging voltage ranges and achievable reductions in charge/discharge asymmetry by over 70 % that significantly enhance lifespan trajectories through aging control under similar test conditions. The technique's efficacy is further ascertained through systematic tuning of GA parameters like mutation rates and population sizes using Taguchi experimental models. The findings showcase superior optimization outcomes for larger populations and lower mutation probabilities. The research integrates digital twin for rapid evaluations, addressing reliability challenges via computational aging control. The flexible modeling platform enables customized what-if analyses for EDLC designers, aiding in material, cycling, and duty cycle exploration. This aging mitigation approach offers simulation-driven insights and automated optimization tools to extend the operational duration of high-performance EDLCs. • Development of simulation approach due to control aging and degradation of EDLCs. • The application of GA for optimization of aging/degradation process in EDLCs. • Tuning the GA computations utilizing Taguchi design model. • Sensitive analysis of the most significant features by simulation approaches. [ABSTRACT FROM AUTHOR]

Ensuring the seamless flow of goods from farmer to stakeholder is paramount especially considering the facts such as demand fluctuations, quantity availability, vehicle capacity, inventory limits and truck preferences. In this paper, we proposed a mixed integer linear programming (MILP) model for distributing perishable food goods from surplus states to surplus and deficit state wholesalers in the Indian subcontinent, with the goal of minimizing total cost (fixed, variable, and operational) and transportation time. To ensure product delivery on time, we looked at various scenarios including a case with real-life constraints. Solving this computationally complex task with conflicting objectives in a reasonable time is difficult. To handle the aforementioned multi-objective problem, we proposed an evolutionary algorithm based modified non-dominated sorting genetic algorithm II (MNSGA-II). Furthermore, the suggested algorithm's performance is tested against the most extensively used multi-objective algorithm, Non-dominated Sorting Genetic method-II (NSGA-II). According to the findings, the proposed MNSGA-II outperforms practically all instances. [ABSTRACT FROM AUTHOR]

In the future, distributed generation is expected that to be applied more frequently. This study investigates the use of a hybrid of genetic algorithms (GA) and particle swarm optimization (PSO) to enhance distributed generation planning and placement determination for load models. The analysis takes a combination of GA and PSO into account for coordinated control of distributed generations. After doing this, we found that we could reduce the real and reactive power losses and increase the voltage profile. The 37-bus distribution system is the primary illustration of the success of the proposed strategy. Researchers, academics, and anybody else working on distributed generation smart grid will greatly benefit from this study. [ABSTRACT FROM AUTHOR]

Wildfires are complex phenomena with harmful consequences, ranging from environmental and property destruction to loss of human lives. In this sense, predicting wildfire behaviour is essential to mitigate its impacts and consequences. The Rothermel model is the most used fire rate of spread prediction model. However, input parameter uncertainty is a significant source of prediction error. In this paper, we propose the calibration of the input parameters of the fire propagation model by metaheuristic algorithms under a two-stage framework. The fire spread model consists on the Rothermel model in a two-dimensional approach for surface fires. The proposed calibration is performed in two stages iteratively repeated over time: (i) the calibration of the fire spread model's input parameters and (ii) the wildfire spread prediction using the calibrated input parameters. The calibration was performed by the genetic algorithm, differential evolution, and simulated annealing, which calibrates the surface-area-to-volume ratio, fuel bed depth, live fuel moisture and dead fuel moisture. The symmetric difference between the real and predicted fire map shapes was defined as the fitness function of all three metaheuristic algorithms. For validation, simulations were done on two prescribed fires. The results for the real and estimated fire behaviour were then compared and revealed that all the tested metaheuristic algorithms produce a better fit to the real fire's perimeter when compared to the uncalibrated Rothermel model. From the results, differential evolution provided the majority of best results when compared to genetic algorithm and simulated annealing algorithms in each scenario. [ABSTRACT FROM AUTHOR]

Task allocation is a key combinatorial optimization problem, crucial for modern applications such as multi-robot cooperation and resource scheduling. Decision makers must allocate entities to tasks reasonably across different scenarios. However, traditional methods assume static attributes and numbers of tasks and entities, often relying on dynamic programming and heuristic algorithms for solutions. In reality, task allocation resembles Markov decision processes, with dynamically changing task and entity attributes. Thus, algorithms must dynamically allocate tasks based on their states. To address this issue, we propose a two-stage task allocation algorithm based on similarity, utilizing reinforcement learning to learn allocation strategies. The proposed pre-assign strategy allows entities to preselect appropriate tasks, effectively avoiding local optima and thereby better finding the optimal allocation. We also introduce an attention mechanism and a hyperparameter network structure to adapt to the changing number and attributes of entities and tasks, enabling our network structure to generalize to new tasks. Experimental results across multiple environments demonstrate that our algorithm effectively addresses the challenges of dynamic task allocation in practical applications. Compared to heuristic algorithms like genetic algorithms, our reinforcement learning approach better solves dynamic allocation problems and achieves zero-shot generalization to new tasks with good performance. The code is available at https://github.com/yk7333/TaskAllocation. • Innovative Task Allocation : Uses Markov Decision Process and reinforcement learning for better solutions. • Dynamic Adaptation : Attention mechanisms adapt to changing entities and tasks efficiently. • Fine-Tuned Selection : Seq2seq structure optimizes agent selection, reducing costs and boosting efficiency. [ABSTRACT FROM AUTHOR]

The Artificial neural networks (ANN) model established in this study can accurately predict the rolling force of Titanium-Aluminium (TiAl) alloy during isothermal rolling process, and can achieve online monitoring and rolling plan optimization. TiAl alloys are widely used in the aerospace field due to their high strength, low density, good corrosion resistance, and high temperature stability. However, the wrinkling and cracking problems of TiAl alloys are still difficult to solve. The important way to solve this problem is isothermal rolling, and the precise control of rolling force during the rolling process provides a strong guarantee for solving the problem. This study established a rolling force prediction model for TiAl alloy isothermal rolling based on genetic algorithm optimized Back Propagation (BP) neural network. The database used for training and testing is from a controlled two-roll rolling mill. The input parameters of the network are inlet temperature, roller temperature, rolling speed, reduction ratio and friction coefficient between roller and strip. The superiority of genetic algorithm is verified by comparing genetic algorithm with BP neural network optimized by fuzzy theory. The results show that when the population size is 40, the crossover probability is 0.2, the mutation probability is 0.1, the evolutionary algebra is 10, the transfer functions of the hidden layer and the output layer are tansig, and the structure of the neural network is 5-5-1, optimization works best. The maximum relative error is only 5.3%, the average relative error is only 1.6%, and the correlation coefficient is 99.78%. • The GANN model established in this study has significantly improved prediction accuracy compared to previous models. • The GANN model provides a powerful tool for solving the problem of narrow processing windows for TiAl alloys. • The prediction problem of rolling force with strong coupling characteristics was solved using numerical simulation method. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms built on Pareto-dominance suffer from a loss of selection pressure as the number of objectives increases and the probability of finding non-dominated solutions in the population decreases. Furthermore, Pareto-dominance is computationally expensive because of the pairwise comparisons necessary to rank the individuals of a population. The paper introduces a new genetic algorithm for multi-objective optimization based on Apparent Front Ranking and crowding distance, called Controlled Apparent Front Zones Genetic Algorithm (CAFZGA). To avoid pairwise comparisons, CAFZGA first generates the main Apparent Front Boundary (AFB) with the help of a small set of support vectors and creates secondary AFBs as shifted versions of the main AFB. The space of objectives is divided into zones by the AFBs. The zones play a similar role in CAFZGA as the non-dominated fronts do in the Controlled Non-dominated Sorting Genetic Algorithm CNSGA-II. The zone ranking becomes the main criterion in differentiating individuals, with crowding distance being the tiebreak criterion. The method is shown to be extremely flexible because the AFBs are adjusted for each generation. Computationally, CAFZGA is more efficient than GAs using Pareto-dominance because the set of support vectors for generating the AFBs is significantly smaller than the population. CAFZGA is applied to the problem of optimizing the configuration of a Grid ALU Processor (GAP) and is shown to be competitive and sometimes even better than well-established GAs like CNSGA-II and Fuzzy-Dominance-Driven Genetic Algorithm FDD-GA. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years cryptographic tokens have gained popularity as they can be used as a form of emerging alternative financing and as a means of building platforms. The token markets innovate quickly through technology and decentralization, and they are constantly changing, and they have a high risk. Negotiation strategies must therefore be suited to these new circumstances. The genetic algorithm offers a very appropriate approach to resolving these complex issues. However, very little is known about genetic algorithm methods in cryptographic tokens. Accordingly, this paper presents a case study of the simulation of Fan Tokens trading by implementing selected best trading rule sets by a genetic algorithm that simulates a negotiation system through the Monte Carlo method. We have applied Adaptive Boosting and Genetic Algorithms, Deep Learning Neural Network-Genetic Algorithms, Adaptive Genetic Algorithms with Fuzzy Logic, and Quantum Genetic Algorithm techniques. The period selected is from December 1, 2021 to August 25, 2022, and we have used data from the Fan Tokens of Paris Saint-Germain, Manchester City, and Barcelona , leaders in the market. Our results conclude that the Hybrid and Quantum Genetic algorithm display a good execution during the training and testing period. Our study has a major impact on the current decentralized markets and future business opportunities. [ABSTRACT FROM AUTHOR]

Multi-surrogate assisted evolutionary algorithms, utilizing inexpensive global and local surrogate models to assist evolutionary search, have demonstrated a remarkable ability to solve expensive optimization problems (EOPs). The selected training samples of the local surrogate model used in the current algorithms may not be updated continuously, which could bring about a waste of computing resources. This paper proposes a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy (MS-MTGAwA), in which the local surrogate model is updated adaptively based on information from established local models to enhance the capability in exploiting the optimal solution. The adaptive training sample selection strategy (ATS) applies optimal points found in historical local models as training samples. In addition, MS-MTGAwA inherits the optimization framework of a multi-tasking genetic algorithm. The radial basis function model is chosen as the modeling basis of the global and local surrogate models. A set of common benchmark functions whose dimensions vary from 10 to 100 and the tension/compression spring design problem are adopted to validate the performance of the proposed algorithm. Based on the average best fitness value over thirty independent runs on the benchmark functions, MS-MTGAwA ranks first in Friedman's test compared to five state-of-the-art algorithms including S-JADE, GORS-SSLPSO, SAHO, SAMSO, and MS-MTO. The proposed ATS can be expected to be applied in any algorithms with local surrogate to make full use of historical information of the local model to improve the prediction accuracy of the next local model. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Use the predictive best of historical local surrogate models to update the local model. • Design an adaptive training sample selection strategy for the local surrogate model. • Develop a framework that combined multi-surrogate and multi-population-based multitasking. • Propose a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy. • MSMTGAwA shows strong competitiveness in comparison with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

There is a coupling effect between the liquid and the solid in the hydraulic engine mount (HEM). The accurate estimation and optimization of the dynamic characteristics including dynamic stiffness and lag angle for HEM in the frequency domain are still intractable problems. To this end, a novel model of a deep neural network (DNN)-based on dynamic modeling method is developed by using dataset to estimate the dynamic characteristics, and coupled with a genetic algorithm (GA) for the optimization design. Here, the dataset can be divided into two parts. One part is the input of DNN model and contains feature parameters from simulation; other part is the output of DNN model and composed of dynamic stiffness and lag angle from experiment. They are applied to train, test and validate the DNN model. Besides, the conventional model based on the lumped parameter is also presented to achieve the dynamic characteristics and used for comparison. The performed experiments demonstrate that the estimation accuracy of the DNN model is higher than that of the lumped parameter model. Finally, an optimal design method for the lag angle corresponding to frequency is proposed by combining the DNN model and the GA under the prescribed cost function and constraint conditions. The optimization results are approximately close to the desired values and verify the effectiveness of the proposed method which can improve the isolation performance of HEM. • A novel model of a deep neural network (DNN)-based on dynamic modeling method is developed by using dataset to estimate the dynamic characteristics for hydraulic engine mount (HEM). • A unique data driven-based approach by integrating genetic algorithms into DNN for optimizing HEM is presented. • The integration algorithm allows for fast and effective optimization of dynamic characteristics, providing a practical solution for real-world industrial applications. [ABSTRACT FROM AUTHOR]

Precision Agriculture technology can use high-tech sensors and analytical tools to help farmers better manage their crop yields, allowing them to demonstrate their techniques on the field. The greater use of remote sensing technologies in farm productivity and nitrogen management tools to increase yields and reduce operational costs contributes to reduced energy consumption. This concept describes the need to fulfill nutrient and crop production objectives for the near and long term. Based on our proposed approach, we concluded that the predictors of individual flow occurrences are variables linked to surface temperatures, moisture, and nitrogen availability. We have developed an automated Convolutional Neural Network creation approach genetic algorithms approach to handling image classification problems more efficiently. The best feature of the method is that it is "automated" and doesn't need domain knowledge about CNNs. When compared to conventional encoding, we compare the performance and adaptability of our processing. Moreover, the comparison of individual Genetic algorithms with CNN schemes employed for training the systems was compared to that of other GENCNN schemes ResNet-34, ResNet-50, AlexNet, DenseNet, VGGNet in terms of convergence speed and overall accuracy, among other things. Our studies data indicate that our Soil Nitrogen Management significantly reduced commercial system costs while providing notable outcomes. The experimental results of soil nutrient deficiency 99%and 97% show that the suggested method may be used to automatically identify the most efficient GENCNN model compared to existing models in a wide range of domains. [ABSTRACT FROM AUTHOR]

This study proposed a satellite constellation method to cluster targets using machine learning and achieve the desired revisit performance for each target in an area of interest. Because a typical satellite constellation, such as the Walker method, does not consider the geometrical dynamics between the satellite and Earth's surface target, the target-oriented revisit performance analysis and the application of optimization method are limited. To overcome these limitations, this study developed a satellite constellation method that is customized according to the target using a repeating ground track orbit and machine learning techniques. The proposed model groups aim to maximize satellite revisit performance via K-means clustering and determine the optimal number of satellites required to achieve the desired revisit performance for each target through a quick and accurate accessibility analysis of various target distributions. This study revealed that three to five target clusters are more effective in improving the satellite revisit performance than a typical single target cluster, while identifying that the density of the target distribution is a significant influencing factor. Finally, the proposed model achieved the desired revisit performance, guaranteeing a timely response for 30 major disaster locations in East Asia and validating the model's optimal performance. [ABSTRACT FROM AUTHOR]

A photovoltaic thermal collector (PV/T) is incorporated into the combined cooling, heating, and power (CCHP) system to decrease primary energy consumption, operation cost and carbon dioxide emission. Six CCHP scenarios based on three strategies are investigated for the energy utilization of the CCHP system. Also, a non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning (NSGA-III-CO) is proposed for the six scenarios. The competition crossover determines the starting point of search in the decision space, and more potential regions can be exploited sufficiently. The opposition-based learning (OBL) carries out random searches and facilitates the convergence of the population. In addition, a constraint handling approach (CHA) is presented to decrease the constraint violations of the infeasible individuals and turn them into feasible individuals, and it guarantees the feasibility of the population. Experimental results suggest that each CCHP scenario with PV/T is more efficient than the one with photovoltaic panel (PV) and the one neglecting PV/T and PV, and it achieves the lowest primary energy consumption, operation cost and carbon dioxide emission. Also, NSGA-III-CO and the other three algorithms are applied to the six CCHP scenarios with PV/T, and it obtains the highest hypervolume and coverage rate for each scenario. [Display omitted] • This paper studies dispatch of combined cooling, heating, and power system (CCHP). • The CCHP involves photovoltaic thermal collector. • An improved NSGA-III is proposed for CCHP. • The improved NSGA-III has exhibited good comprehensive performance for CCHP. • A constraint handling approach is presented to satisfy the constraints of CCHP. [ABSTRACT FROM AUTHOR]

The modeling and simulation of engineering materials using constitutive equations requires a large set of optimized coefficients that characterize the hardening or softening behavior. Optimizing this large set of coefficients with multiple constraints is very challenging using conventional optimization methods. This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The proposed framework demonstrates solution convergence, scalability to available data, and high explainability over a wide range of engineering materials, including titanium-based, iron-based, and aluminum-based alloys. The experimental test data for necessary validation of the computational framework was generated using the MTS fatigue testing machine equipped with a highly sensitive extensometer. The Chaboche unified visco-plasticity material model has been implemented as an example in this study which can deal with both cyclic effects, such as combined isotropic and kinematic hardening, and rate-dependent effects associated with visco-plasticity. The experimentally obtained cyclic response of three different classes of materials was compared with their optimized simulated constitutive equations, and the results were in good agreement. Furthermore, the proposed multi-objective optimization using genetic algorithm methodology avoids local optimality and converges to the optimal solution much faster than commercially available software. [ABSTRACT FROM AUTHOR]

Home health care (HHC) is a fast-growing area of research that has received increasing attention in recent years due to the rise in life expectancy and the fall in birth rates. It is expected to reduce length of stay in hospital as well as to allow patients to receive care and assistance in their home. Several models and algorithms have been proposed to manage the various constraints and criteria. However, multi-objectives HHC routing and scheduling problems (HHCRSP) with practical and complicated constraints as well as Pareto dominance based approaches are still scarce in the literature. In this paper, we focus on modeling and solving the multi-objectives HHCRSP with lunch breaks, multiple time windows, duty length, skill requirement and a mix of soft and hard services. A mixed integer linear programming (MILP) model with three objectives is proposed, which aims to minimize tardiness of soft services and caregivers' waiting times and also to balance their workload. Two approaches, Pareto and decomposition based, with multi-objective evolutionary algorithms are proposed to solve the multi-objective HHCRSP. We developed a memetic algorithm (NSGA-LS), resulting from the hybridization of non-dominated sorting genetic algorithm II (NSGA-II) with multidirectional local search (MDLS), to trade-off between exploration and exploitation. Computational results and performance measures inferred that NSGA-LS has a better performance compared to other algorithms. According to the hyper-volume indicator, NSGA-LS obtained 93.75% better solutions for the tested instances in a pairwise comparison between algorithms while multiobjective evolutionary algorithm based on decomposition (MOEA/D) has a better complexity. Sensitivity analysis of the percentage of soft/flexible services and the maximum allowed tardiness showed that increasing them significantly improved the quality and trade-off of solutions. [ABSTRACT FROM AUTHOR]

This study introduces a novel framework called Reliable Pollution Scheduling and Routing with Multi-Cross-Docking. The core idea behind this framework is to optimize the processing and transportation of products through each vehicle, ensuring that each vehicle serves its specified destinations only once before returning to the cross-dock. The objective of this research is to develop a multi-objective mixed integer programming model that efficiently manages scheduling and routing complexities in a multi-cross-docking system, taking into account uncertainties in demand. The primary goals of the model are to minimize the overall costs associated with pollution, loading/unloading, and transportation, while simultaneously reducing distribution and shipping durations. Additionally, the model aims to maximize the reliability of the supply chain for perishable products, contributing to sustainable development in the supply chain domain. To address the inherent complexity of the model, especially in dealing with demand uncertainties, we employ a robust optimization method. The multi-objective challenge is tackled using an innovative hybrid approach that combines goal programming and genetic algorithms. The effectiveness of our proposed solution strategies is rigorously assessed using a variety of metrics and subjected to comprehensive statistical testing. Furthermore, we validate the competence of our methodology by conducting a real-world case study, which includes sensitivity analysis of demand parameters and robustness analysis. Our findings confirm that our solution technique produces high-quality solutions. Notably, our approach optimizes route planning for delivery and pick-up phases, resulting in a significant 36.5% reduction in transportation time and a noteworthy 17.27% decrease in the overall system costs compared to existing conditions. Additionally, we observe a substantial 23.15% reduction in vehicle arrival times at cross-docks, contributing significantly to reduced product expenses. [ABSTRACT FROM AUTHOR]

In recent decades, medical tourism especially cosmetic tourism has increased in many countries. In this study, a bi-objective mathematical model is designed for the cosmetic tourism supply chain for the first time. In the first objective function, the profit margin in the supply chain is maximized. In contrast, the distance between the medical and accommodation centers is minimized in the second objective function. The general revised multi-choice goal programming (GRMCGP) method is an exact method for optimizing the problem. To solve the problem, model is run by Lingo software. Then, a new hybrid genetic algorithm named genetic algorithm-general revised multi-choice goal programming (GA-GRMCGP), to solve large-scale instances, is utilized. Moreover, a case study is considered to validate the proposed mathematical model. Furthermore, sensitivity analyses are carried out to evaluate the effectiveness of the research. Finally, the results of the exact and meta-heuristic algorithms are compared and discussed. [ABSTRACT FROM AUTHOR]

The problem of flexible job shop batch scheduling represents an extension of the flexible manufacturing problem. Different batching strategy significantly affects the effect of subsequent scheduling optimization. Therefore, collaborative optimization of the batching strategy and subsequent scheduling is crucial for improving production efficiency. In this paper, a flexible job shop batch production model considering the set-up time is established by analyzing the coupling relationship between batching strategies and scheduling optimization. Based on the proposed model, an improved genetic algorithm with a three-layer coding mechanism is developed to solve the problem of the solution space expansion caused by batch collaboration. The three-layer coding mechanism includes the batch strategy, operation selection, and machine selection. In addition, an operation overlapping strategy is introduced to improve production efficiency and make production more resilient. The proposed algorithm is tested by experiments on scheduling problems of different sizes. The experimental result shows that the proposed method can significantly improve and efficiently optimize flexible job shop scheduling in batch production. [ABSTRACT FROM AUTHOR]

Optimal aiming strategies are essential for efficient solar power tower technology operation. However, the high calculation complexity makes it difficult for existing optimization methods to solve the optimization problem in real-time directly. This work proposes a real-time optimal heliostat aiming strategy generation method via deep learning. First, a two-stage learning scheme where the neural network models are trained by genetic algorithm (GA) benchmark solutions to produce an optimal aiming strategy is presented. Then, an end-to-end model without needing GA solutions for training is developed and discussed. Furthermore, a robust end-to-end training method using randomly sampled flux maps is also proposed. The proposed models demonstrated comparable performance as GA with two orders of magnitude less computation time through case studies. Among the proposed models, the end-to-end model shows significantly better generalization ability than the pure data-driven two-stage model on the test set. A robust end-to-end model with data enhancement has better robustness on unseen flux maps. [ABSTRACT FROM AUTHOR]

Several optimization algorithms have been developed in the past for application in a variety of complex real world problems. Herein, a new hybrid variant of Genetic Algorithm (GA), utilizing the sample space reduction technique of Cohort Intelligence (CI) Algorithm, referred to as Adaptive Range Genetic Algorithm (ARGA) is presented and discussed. The correctness of the proposed algorithm is established by solving 50 standard test functions. Also, the practical applicability of the proposed algorithm is demonstrated by applying it to the design and economic optimization problem of a Shell and Tube Heat Exchanger. Heat exchangers are critical industrial components used in a wide range of industrial applications. Owing to their high manufacturing costs and complicated design criteria, their economic efficiency of operation depends heavily on optimization of their design. A relative assessment, comparing the performance of the proposed method of solution to other existing algorithmic methods, is conducted. The quality and robustness of the proposed solution method highlights its adeptness in solving real world computational problems in core mechanical engineering applications. [ABSTRACT FROM AUTHOR]

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance. This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed. An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week. [ABSTRACT FROM AUTHOR]

This paper presents a new initialization method that combines adaptive visibility graphs and the A* algorithm to improve the exploration, accuracy, and computing efficiency of hybrid path planning for mobile robots. First, segments/links in the full visibility graphs are removed randomly in an iterative and adaptive manner, yielding adaptive visibility graphs. Then the A* algorithm is applied to find the shortest paths in these adaptive visibility graphs. Next, high-quality paths featuring low fitness values are chosen to initialize the subsequent heuristic optimization in hybrid path planning. Specifically, in the present study, the genetic algorithm (GA) is implemented on a CPU/GPU edge computing device (Jetson AGX Xavier) to exploit its massively parallel processing threads, and the strategy for judicious CPU/GPU resource utilization is also developed. Numerical experiments are conducted to determine proper hyperparameters and configure GA with balanced performance. Various optimal paths with differential consideration of practical factors for robot path planning are obtained by the proposed method. Compared to the other benchmark methods, ours significantly improves the diversity of initial path and exploration, optimization accuracy, and computing speed (within 5 s with most less than 2 s). Furthermore, real-time experiments are carried out to demonstrate the effectiveness and application of the proposed algorithm on mobile robots. • The adaptive visibility graph eliminates the issue of biased path search. • A new approach generates multiple initial paths with enriched diversity. • Judicious utilization of CPU/GPU resources improves computing efficiency. • Implementation on light-weight edge device is well-suited for robot applications. [ABSTRACT FROM AUTHOR]

In the era of internet and big data, recommender systems are necessary to filter out useless information. Collaborative filtering (CF) is one of the most successful technique used in recommender systems, this technique suffers from a large number of users and items, or the scalability issue. In this paper, a new hybrid method based on K-means clustering (KM) and Singular Value Decomposition (SVD) which uses evolutionary algorithms is proposed to deal with scalability issue. On the one hand, KM optimized by Particle Swarm Optimization (PSO) and denoising by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster users and reduce the number of comparisons between the target user and other users. On the other hand, SVD optimized by Genetic Algorithm (GA) is used to reduce the number of items. The proposed method is assessed on three standard datasets and the results are compared with basic CF and other extended versions that use clustering algorithms, evolutionary algorithms and dimensionality reduction techniques. The results show that our proposed method performed better than other methods in terms of precision , recall and MAE , and the scalability problem was improved by reducing the time complexity. Also, the combined clustering method was optimized in terms of Davies-Bouldin and the Dunn's index compared to the basic clustering methods. • The quality of the recommender system was improved by combining model-based and memory-based collaborative filtering methods. • The problem of scalability has been solved by using different clustering methods and matrix dimensionality reduction method. • Combination of K-means clustering and DBSCAN was performed to clean the clusters from noise. • The quality of clustering and matrix dimensionality reduction has been improved by using evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The design of the suspension model of road cars is extremely important for both driver comfort and safety. Road traffic injuries are currently the eighth leading cause of death worldwide and are predicted to become the seventh leading cause of death by 2030, according to the World Health Organization (WHO). Among other factors, road infrastructure is one of the most important factors to consider when designing the suspension model, especially as autonomous driving becomes more popular. In addition, there is a growing need to test and study the impact of road roughness and pavement type on autonomous vehicles (AVs). This work provides the following contributions: a platform for suspension system optimization–flexible, highly parameterizable and rich in applications. Our solution implements, tests and optimises the 2, 3 and 4 degrees of freedom (DOF) quarter-car models (QCM) with linear seat suspension and the 5 DOF half-car model (HCM), respectively, based on random road profiles generated according to ISO 8608 standards. In addition, many multi-objective optimization (MOO) algorithms have been implemented and applied in isolation to see their comparative performance (in terms of Pareto fronts, hypervolume, coverage, spread, and epsilon), but most importantly we have also introduced a meta-optimization technique by super-positioning Non-dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2, where each of the algorithms, running concurrently, produces a percentage of the total number of individuals in a generation, percentage that is calculated according to the solution quality of the individuals in the previous generation. The experimental results were obtained by simulating the optimal configurations generated by the MOO heuristic algorithms on different categories of cars (small/medium). Finally, the Taguchi method and regression analysis were used to evaluate the effects of specific parameters on system responses such as sprung mass acceleration and displacement. The Taguchi simulation was performed to experimentally validate the results obtained by metaheuristic optimization methods. [ABSTRACT FROM AUTHOR]

Upper limb amputation is a widespread problem worldwide, leading to massive loss of functionality for the victims. While a few solutions exist, these are often very expensive and involve expensive and dangerous surgical procedures. In this paper, we propose a low-cost, highly functional upper limb prosthesis controlled via Electroencephalogram (EEG) signals captured in real-time, classified into upper limb motion intention using a novel Genetic Algorithm (GA) optimized Long Short-Term Memory (LSTM) deep learning model to rehabilitate amputees. The proposed 3D-printed prosthetic arm has 3 Degrees-of-Freedom (DOF), which allows it to perform complex movements that can accurately emulate an actual human arm. The results discussed later in this paper conclude that our approach, while being low-cost, is highly accurate and functional. [ABSTRACT FROM AUTHOR]

This paper presents an aircraft conceptual design technique with more than three objective functions, called many-objective optimisation. The selection of aircraft configuration is usually achieved using a system engineering approach. This selection approach has the design variables assigned to remove the configuration decision-making process. The design problem is demonstrated for the conceptual design of a fixed-wing unmanned aerial vehicle. Eight objective functions, including power required, take-off weight, take-off distance, landing distance, endurance, range, lift coefficient at cruise and drag coefficient at cruise, are posed, while the constraints are aircraft stability, performance required and take-off distance. Design variables simultaneously determine an aircraft configuration, shape and sizing parameters. Hence a new, many-objective metaheuristic is developed to increase the design performance. A grid-based many-objective metaheuristic with iterative parameter distribution estimation (MM-IPDE-Gr) is developed. It is an enhanced variant of the MM-IPDE with improved reproduction schemes, adaptive parameters and a grid-based clustering technique. Several additional reproduction schemes in mutation and crossover processes with two additional adaptive parameters are integrated to increase population diversity and improve the exploration ability of the algorithm. In addition, the grid-based method is integrated as a clustering technique to improve the Pareto clustering process in many-objective optimisation. The proposed method, with established newly invented metaheuristics, is used to solve the new design problem and its performance compared with existing design methods. It is shown that the proposed many-objective metaheuristic gives the best results. [ABSTRACT FROM AUTHOR]

The three-fluid heat exchanger is a multi-stream heat exchanger that provides three streams at different temperatures for certain usage objectives. Recently, optimizing heat exchangers generally and three-fluid heat exchangers especially have received great attention in the energy field. The current study objective is to get the optimal mass flow rate of the three streams to minimize the entropy generation number and maximize effectiveness. Optimization is performed on the inserted coiled tube type for 24 cases with different usage objectives and different mass flow rate constraints. The optimization is done using an analytical approach for the temperature profiles and a regressed model for the overall heat transfer coefficients. Single-objective and multi-objective genetic algorithms are the optimization algorithms. The result shows that cooling the hot fluid is the most efficient usage objective; however, heating the intermediate-temperature fluid is the less efficient usage objective. Also, the study shows that the entropy generation number is dependent on the mass flow rate constraint. The maximum effectiveness of 0.693 occurs when the usage objective is cooling the hot fluid. In this case, the mass flow rate of the cold and the intermediate-temperature fluids are maxima while the mass flow rate of the hot fluid is minimum. The minimum entropy generation number 0.0486 occurs when the mass flow rate of the cold fluid is maximum while the mass flow rate of the hot fluid and intermediate-temperature fluid are minimal. [Display omitted] • Heat transfer of a three-fluid heat exchanger is investigated. • A model for optimizing the inserted coiled tube three-fluid heat exchanger is developed. • The maximum errors for the regression models of heat transfer coefficients are 1.72% and 2.81%. • The maximum effectiveness reached about 0.69323. [ABSTRACT FROM AUTHOR]

Assessing project portfolio risk (PPR) is essential for organizations to grasp the overall risk levels of project portfolios (PPs) and realize PPR mitigation. However, current research is inadequate to effectively assess PPR, which brings challenges to managing PPR. In this context, the purpose of this study is to develop a PPR assessment model via an enhanced backpropagation neural network (BPNN). First, PPR assessment criteria considering project interdependencies are determined. Second, fuzzy logic is used to obtain original data for assessment criteria. Principal component analysis (PCA) is then employed to reduce the dimensionality of assessment criteria and derive the input and output of BPNN. Third, an improved genetic algorithm (IGA) is designed to optimize the initial weights and thresholds of BPNN. On this basis, the PCA-IGA-BPNN assessment model is constructed, followed by training and testing, possessing a test accuracy of 98.6%. Finally, comparison experiments are conducted from both internal and external perspectives. For internal comparison, the proposed model yields less mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) than PCA-GA-BPNN, IGA-BPNN, PCA-BPNN and BPNN and offers the largest convergence speed (γ). As for external comparison, the presented model produces lower MAPE, MSE, and RMSE than Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) and has the largest coefficient of determination (R 2). Results indicate that the established model performs more satisfactorily in assessing PPR. This research enriches PPR assessment methods and provides managers with a useful tool to evaluate PPR. • A hybrid project portfolio risk assessment model based on an enhanced BPNN is established. • GA is improved by adaptively adjusting the probability of crossover and mutation operations. • PCA and improved GA are integrated with BPNN to enhance its evaluation performance. • The effectiveness and superiority of the proposed model are validated through experiments. [ABSTRACT FROM AUTHOR]

Solar PV generation is renewable energy source that in the last years has contributed to reduce the use of fossil fuels. Controlling the efficiency of photovoltaic (PV) installations is essential in order for its use to spread. Deep learning (DL) models have demonstrated their efficiency in this context. The purpose of this work is to show a methodology to optimised deep learning models and show a specific application of the optimised model to characterise PV installations. The estimations yielded by this model are obtained without the need for real PV data to the training process; synthetic data are used. The built multivariate neural network is optimised through the use of a multiobjective genetic algorithm and the use of a feature engineering tool based on functional data analysis (FDA) clustering. This method was applied on synthetic data and on a PV installation located in north-western Spain, where the number of parallel modules, the azimuth and the slope in the installation are estimated. The results show that the model optimised using the non-dominant sorting genetic algorithm (NSGA-II) and the customised dataset with FDA, achieve lower errors or in the same range by reducing the complexity of the model or the complexity of the dataset used. Specifically, the bests models generates estimations with an average error between 6% and 18% on synthetic data and between 6% and 12% on real data. • An optimisation methodology is proposed to select the best LSTM architecture. • The optimisation is based on the multiobjective genetic algorithm NSGA-II. • A feature engineering tool based on FDA clustering is also proposed. • The models are evaluated based on the PV installations characterisation. • The results show the improvements provided by the optimisations presented. [ABSTRACT FROM AUTHOR]

The proportional–integral regulator is one of the most famous and widely used linear controllers in the industrial field because of its easy adjustment of the results, simplicity, and inexpensive. The negative of this controller is that it is affected by the change of system parameters, which causes several problems, including the high value of ripples and low quality of the power/current. To reduce this defect, a new proportional–integral regulator is suggested based on the use of the terminal sliding surface technique and genetic algorithm. This suggested nonlinear controller is discussed for the first time in this work to use it to ameliorate the quality of energy output from a dual-rotor wind power system using an induction generator. The use of a new controller to regulate the generator energy will inevitably lead to an increase in power errors, power ripples, and harmonic distortion of current especially if the system parameters are changed. Also, the traditional direct power control based on the proposed nonlinear controllers of the induction generator is a robust control, does not need the mathematical form of the generator, is simple, and does not need a specialist. Moreover, the suggested method is applied to the rotor inverter of the 1.5 MW generator using Matlab software. The suggested technique takes full advantage and the power regulation objective of the suggested power system is confirmed by the numerical results compared to the conventional technique and other published controls. [ABSTRACT FROM AUTHOR]

The efficient coordination of directional overcurrent relays is a challenging problem. Recent papers in the literature have focused on developing efficient algorithms to solve this issue. However, there is a lack of reporting practical procedures that affects the mathematical formulation to ensure accurate outputs by any optimization algorithm. In this paper, we show a comprehensive procedure that considers mutually coupled circuits via Graph Theory, the requirements of short-circuit current calculation, contingencies to be considered as well as the setting of Instantaneous Overcurrent (IOC) and Time Overcurrent (TOC) units, both for phase and ground protection. The simulation has been carried out by using a real electric network and C++ language programming. Computational simulation has shown the performance of the method, which provides high-quality solutions, with adequate coordination of the selected protective devices that increase the efficiency in setting this type of protection with safety. Evolutionary Particle Swarm Optimization (EPSO) outperformed conventional Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the best metaheuristic for optimally minimizing the proposed problem, achieving faster convergence times for the phase overcurrent protection scenario (16.36 s vs. 22.27 s and 17.03 s, respectively), and also for the ground overcurrent protection scenario (19.87 s vs. 20.44 s and 20.11 s, respectively). [Display omitted] • Relay coordination considering devices from different manufactures. • Contingencies in transmission lines formed by several mutually coupled circuits. • Inclusion of mutual impedances in the relay coordination and they are processed using Graph Theory. • An optimization model that combines three performance indices using metaheuristics. • A new strategy to determine pick-up current values for phase IOC unit. [ABSTRACT FROM AUTHOR]

The segmentation of multivariate temporal series has been studied in a wide range of applications. This study investigates a challenging segmentation problem on traffic engineering, namely, identification of time-of-day breakpoints for pre-fixed traffic signal timing plans. A large number of urban centres have traffic control strategies based on time-of-day intervals. We propose a bilevel optimization model to address simultaneously the segmentation problems and the traffic control problems over these time intervals. Efficient memetic algorithms have been developed for the bilevel model based on the hybridization of the particle swarm optimization, genetic algorithms or simulated annealing with the Nelder–Mead method. Numerically the effectiveness of the algorithms using real and synthetic data sets is demonstrated. We address the problem of automatically estimating the number of time-of-day segments that can be reliably discovered. We adapt the Bayesian Information Criterion, the PETE algorithm and a novel oriented-problem approach. The experiments show that this last method gives interpretable results about the number of reliably necessary segments from the traffic-engineering perspective. The experimental results show that the proposed methodology provides an automatic method to determine the time-of-day segments and timing plans simultaneously. • A bilevel model for simultaneously determining TODs and traffic signal optimization. • Memetic algorithms based on PSO, SA, GA and Nelder Mead are proposed for bilevel model. • A set of computational tests on synthetic and real data sets. • A novel oriented-problem approach to determine optimal number of TODs. [ABSTRACT FROM AUTHOR]

Variable Selection in large datasets is a commonly procedure in multivariate calibration, which is a field of study from chemometrics. Selecting the most informative variables becomes an important step to build mathematical models through statistical techniques in order to predict some property of interest from an analyzed sample. Recombination-based search methods such as Genetic Algorithms (GAs) have been widely used as variable selection techniques to solve several optimization problems. However, previous works from literature have emphasized the schemata disruption problem caused by genetic operators. Therefore, this paper proposes two versions of an epistasis-based implementation (EbFSA) as a novel approach for variable selection in multivariate calibration problems, where each version is deterministic and performs a different strategy. The use of epistasis concepts becomes important to assess the genes (variables) interdependence. Based on our experimental results, we are able to claim EbFSA can select the most informative variables and overcome some state-of-the-art algorithms. • A novel approach for variable selection in multivariate calibration problems is presented. • We present two versions of an Epistasis-based implementation (EbFSA). • A comparison with traditional algorithms is performed. • We obtained advantages by reducing the number of variable as well as the prediction error. • We experimentally demonstrated that EbFSA can be a good choice for the variable selection problem. [ABSTRACT FROM AUTHOR]