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Abstract In this study, we extend the traveling repairman problem with profits (TRPP) by considering the use of multiple vehicles, which occurs commonly in practice. We call the resulting problem the multiple traveling repairman problem with profits (MTRPP). In MTRPP, a fleet of identical vehicles is dispatched to visit a subset of nodes in order to collect time-dependent profits. To solve MTRPP, we present a highly effective memetic algorithm, which combines (i) a randomized greedy construction method for initial solution generation, (ii) a variable neighborhood search for local refinement, and (iii) a dedicated route-based crossover operator for solution recombination. Computational experiments performed on a large set of instances reveal the efficiency of the algorithm. Additionally, we demonstrate the relevance of our algorithm for the classic TRPP. The reported computational results serve as a benchmark for future researchers who wish to investigate the problem. Highlights • The multiple traveling repairman problem with profits (MTRPP) is introduced. • A mathematical model of MTRPP is formulated. • A memetic algorithm (MA) is proposed for solving MTRPP. • Computational study shows that the proposed MA effectively solves MTRPP. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a bi-objective hierarchical hub location problem with hub facilities as servicing centers. The objectives are to minimize the total cost (i.e., fixed cost of establishing hub facilities and transportation cost) and the maximum route length, simultaneously. Hub facilities are categorized as central and local ones. The queuing frameworks for these types of facilities are considered as M ∕ M ∕ c and M ∕ M ∕ 1 , respectively. Moreover, density functions for the traveling time and number of entities are assumed to be Exponential and Poisson functions. The presented mathematical model is solved by a new game theory variable neighborhood fuzzy invasive weed optimization (GVIWO) as introduced in this paper. To evaluate the efficiency of this proposed algorithm, some experiments are conducted and the related results are compared with the non-dominated sorting genetic algorithm (NSGA-II) and hybrid simulated annealing (HSA) algorithm with respect to some comparison metrics. The results show that the proposed GVIWO algorithm outperforms the NSGA-II and HSA. Finally, the conclusion is provided. Graphical abstract Highlights • Presenting a new bi-objective hierarchical hub location problem that minimizes the total cost and the maximum route length. • Considering the M ∕ M ∕ c and M ∕ M ∕ 1 queuing frameworks for central and local hub facilities, respectively. • Applying an efficient method based on the ME measure to cope with the uncertainty. • Proposing a new Game theory Variable neighborhood fuzzy Invasive Weed Optimization (GVIWO) method. • Comparing the GVIWO results with two well-known meta-heuristic algorithms by some comparison metrics. [ABSTRACT FROM AUTHOR]

Abstract The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state–action trajectory samples. GPRL is compared to a straightforward method which utilizes genetic programming for symbolic regression, yielding policies imitating an existing well-performing, but non-interpretable policy. Experiments on three reinforcement learning benchmarks, i.e., mountain car, cart–pole balancing, and industrial benchmark, demonstrate the superiority of our GPRL approach compared to the symbolic regression method. GPRL is capable of producing well-performing interpretable reinforcement learning policies from pre-existing default trajectory data. [ABSTRACT FROM AUTHOR]

Abstract Many decisions in operations management (OM) belong to the class of Non-deterministic Polynomial hard problems and thus heuristic search methods have been applied to improve OM decisions. While genetic algorithms (GAs) are promising tools for searching fast and good solutions in diverse OM areas, future research will benefit from a review of the OM problems solved by GAs. The purpose of this paper is to review the literature on OM with GA-based solutions and to suggest possible gaps from the point of view of researchers and practitioners. A total of 119 peer reviewed journal papers published from 2007 to 2017 are reviewed and analysed methodologically. The applications of GAs in OM are categorized into process and product design, operations planning and control, and operations improvement. Observations from the existing literature are presented and future research directions are suggested. Although GAs have been one of the most popular heuristic approaches for optimization, there are OM problems that are yet to be investigated. The findings of this review pave the path for future research to apply GAs to solve OM problems. Highlights • Review literature on applications of genetic algorithms in operations management. • Categorize applications in operations management into 3 themes and 9 decision areas. • Provide research and application agenda for further work. [ABSTRACT FROM AUTHOR]

Abstract Source term estimation (STE) of atmospheric dispersion plays an important role in public safety, environmental protection and many other application fields. In this paper, several new composite cost functions for STE using hybrid genetic algorithm are proposed and compared using Nemenyi test based on 68 STE tasks from Prairie Grass field experiment. Results show one of the new composite cost functions, named as WSD, has outstanding performance in estimating both source location and emission rate. Then the patterns in STE results using different cost functions are analyzed based on the 68 tasks mentioned above, which provides further insights into what to expect from STE. At last, the relationship between composite cost functions and multi-objective optimization is analyzed to facilitate the understanding of composite cost functions. To summarize, composite cost functions such as WSD has the potential to achieve a better balance between sensitivity and robustness of cost functions applied in STE, providing the most accurate estimates. Statistical algorithm comparison techniques like Nemenyi test can help us better understand the characteristics and performance of specific settings in STE methods. Graphical abstract Highlights • New composite cost functions are introduced to source term estimation. • Nemenyi test is used to compare different cost functions using multiple data sets. • One of the new composite cost function WSD has outstanding performance. • Bias patterns of the results using different cost functions are analyzed. • The relationship with multi-objective optimization is demonstrated. [ABSTRACT FROM AUTHOR]

Nowadays, the medical tourism industry encourages many healthcare practitioners to provide high-quality and low-cost medical services for patients worldwide. The development of operations research models and algorithms is one important instrument for improving the medical tourism industry based on the economic, political, social, and cultural aspects. In this paper, a medical tourism trip design problem is developed where patients travel from their city of residence to a destination city that may be in another country to receive high-quality and low-cost medical care. The most important part of this problem is to visit a number of tourist cities for each patient individually in the destination. In addition to the total cost, the patients prefer to increase the attractiveness of trips by referring to the quality of medical services and the attractiveness of visiting tourist cities. As far as we know in the area of medical tourist studies, no study has considered the minimization of total cost and maximization of the attractiveness of trips, simultaneously using utility function. The proposed multi-objective optimization model assigns the patients from the origin country to the hospitals in the destination country while making their routing and scheduling decisions to visit the tourist cities. The proposed model is limited by patients' interests and time restrictions while allocating patients to the hospital and orienteering the patients toward visiting tourist attractions. As a complex optimization problem, another significant novelty of this paper is the proposal of a local search-based non-dominated sorting genetic algorithm (LSNSGA-II) for solving the proposed multi-objective optimization model. The proposed algorithm is compared with the original non-dominated sorting genetic algorithm (NSGA-II) and epsilon constraint (EC) method based on different multi-objective criteria. Finally, one main finding from our analyses is finding a trade-off between the total cost and attractiveness of trips as a challenging decision while proposing high-quality solutions in a reasonable time (i.e., less than one hour). • A new multi-objective medical tourism trip design problem is developed. • Proposed model minimizes the total cost while maximizing the attractivity of trips. • A utility function is used to compute variable staying time for visiting tourist places. • An efficient local search-based non-dominated sorting genetic algorithm is introduced. [ABSTRACT FROM AUTHOR]

This paper presents an Optimal Robust Model Reference Adaptive Proportional–Integral controller for grid-tied power systems, which is automatically parametrised using a Genetic Algorithm. The task of choosing the adaptation rate and the controller's initial gains set is now performed in a computational and automatic way, saving design time and ensuring satisfactory transient regimes.The use of a Genetic Algorithm for complete parametrisation of robust adaptive controllers applied to renewable energy power generation systems with LCL filter is first here explored. Five cost functions are considered in the optimisation process. A comparison of simulation results applying the optimised controller in a grid-tied Voltage Source Inverter with LCL filter points out the benefits and drawbacks of using each cost function. A framework for performance analysis and an in-depth discussion are provided. The function determined as the best one, which is integral of absolute error, is used in the controller implemented experimentally, ensuring suitable tracking of grid current references and rejection of grid voltage harmonics, even under unbalanced grid voltage conditions. An experimental comparison in a three-phase 6.75 kW power converter with the non-optimised controller is also provided, where the optimised controller obtains a reduction in the overshoot in all transient regimes and smaller tracking error, resulting in a reduction in the mean error of 14.31% and 60.30% in α and β coordinates, respectively. In addition, the optimised controller reduces total harmonic content, improving energy quality. This parametrisation procedure can be immediately extended to the control of any other power converter systems. [ABSTRACT FROM AUTHOR]

Teaching Learning Algorithm (TLA) is a recently developed nature-inspired optimization technique applicable to complex optimization problems. This paper proposes an improved TLA version using the Laplacian operator of the Genetic Algorithm (GA), named LX-TLA. The proposed algorithm is tested on benchmark optimization problems, including unimodal and multimodal problems. The numerical results are obtained in the form of objective function values, and a t-test is applied to compare the performance of LX-TLA and basic TLA. Convergence plots are given to provide insight into the convergence behavior of LX-TLA. The results reveal that proposed algorithm provides effective and efficient performance in solving benchmark test functions. The proposed algorithm is also applied to engineering design problems, such as Tuned Mass Damper (TMD), truss structure, welded beam, tension string, and pressure vessel. The results obtained using LX-TLA are compared with other nature-inspired optimization algorithms. The results demonstrate that the proposed algorithm is a robust and effective tool for solving complex optimization problems. • Proposed an improved Teaching Learning Algorithm using Laplacian Operator of Genetic algorithm. • Tested on benchmarks functions of varying complexity. • Applied to real life problems in civil and mechanical engineering problems. [ABSTRACT FROM AUTHOR]

Dropout is an effective method of mitigating over-fitting while training deep neural networks (DNNs). This method consists of switching off (dropping) some of the neurons of the DNN and training it by keeping the remaining neurons active. This approach makes the DNN general and resilient to changes in its inputs. However, the probability of a neuron belonging to a layer to be dropped, the 'dropout rate', is a hard-to-tune parameter that affects the performance of the trained model. Moreover, there is no reason, besides being more practical during parameter tuning, why the dropout rate should be the same for all neurons across a layer. This paper proposes a novel method to guide the dropout rate based on an evolutionary algorithm. In contrast to previous studies, we associate a dropout with each individual neuron of the network, thus allowing more flexibility in the training phase. The vector encoding the dropouts for the entire network is interpreted as the candidate solution of a bi-objective optimisation problem, where the first objective is the error reduction due to a set of dropout rates for a given data batch, while the second objective is the distance of the used dropout rates from a pre-arranged constant. The second objective is used to control the dropout rates and prevent them from becoming too small, hence ineffective; or too large, thereby dropping a too-large portion of the network. Experimental results show that the proposed method, namely GADropout, produces DNNs that consistently outperform DNNs designed by other dropout methods, some of them being modern advanced dropout methods representing the state-of-the-art. GADroput has been tested on multiple datasets and network architectures. [ABSTRACT FROM AUTHOR]

Petroleum production forecasting is the process of predicting fluid production from the wells using historical data. In contrast to the traditional methods of analysing surface and subsurface parameters governing fluid production, machine learning (ML) techniques are being applied to forecast the production. The major drawback of traditional and conventional ML techniques is that they are time-consuming and often lack good forecasting power. In this work, time-series forecast models based on powerful and efficient ML techniques are developed to forecast production with historical data. We have fused the attention mechanism into the long short-term memory network, which is referred as the attention-based long short-term memory (A-LSTM) network. The A-LSTM network is fast and accurate, thus solving the low forecasting power problem. To ensure no data leakage occurs during training, and to build a reliable data-driven forecasting approach, we construct the dynamic floating window with varying window sizes over the entire production data. The dynamic floating window slides one-step forward after every prediction and continues till the last production window enabling the model to fit the new data automatically. We have tested and validated the proposed forecasting models with the ML algorithm using actual production data for three wells from entirely different geographies. We then compared them with statistical, deep learning, hybrid, and ML approaches. The genetic algorithm (GA) is applied to optimize the hyper-parameters of the A-LSTM. The results of a comparative analysis show that the A-LSTM network statistically and computationally outperforms the other models for forecasting petroleum production. [Display omitted] • Attention-based Long Short-Term Memory (A-LSTM) was used for forecasting the production. • A genetic algorithm (GA) was used to optimize the hyper-parameters of A-LSTM. • Seven forecasting models, consisting of several inputs and output were developed and tested. • A well-trained A-LSTM algorithm was observed superior, easy, and computationally efficient. [ABSTRACT FROM AUTHOR]

This paper presents a nondominated sorting genetic algorithm III with three crossover strategies (NSGA-III-TCS) for the combined heat and power dynamic economic emission dispatch (CHPDEED) problems with or without prohibited operating zones. The first strategy updates the individuals of the first nondomination rank by a modified improvisation of the harmony search algorithm, which accelerates the information exchanges of the elite individuals. The second strategy regulates the individuals of the last nondomination rank according to a modified single-point crossover of the genetic algorithm, which strengthens the convergence of the population. The third strategy adjusts the remaining individuals in terms of a modified DE/rand-to-best/1 mutation of the differential evolution algorithm, and it guides these individuals to move towards the ones which have at least one minimum objective function value, which is beneficial for expanding the distribution range of the population. NSGA-III-TCS obtains the largest average hypervolumes and shows a desirable convergence for six CHPDEED problems. NSGA-III-TCS achieves relatively high coverage rates in most cases, indicating the dominance of the Pareto sets of NSGA-III-TCS over those of the other algorithms. Also, it has relatively low average spacings for all problems, suggesting the good evenness of the Pareto sets. • Combined heat and power dynamic economic emission dispatch (CHPDEED) is studied. • A NSGA-III with three crossover strategies (NSGA-III-TCS) is presented for CHPDEED. • The Pareto sets of the NSGA-III-TCS have high hypervolumes and coverage rates. • NSGA-III-TCS obtains good solutions with low generation cost and pollutant emission. [ABSTRACT FROM AUTHOR]

Data science techniques (DST) are the most popular approaches for estimating the discharge coefficient (C d) of labyrinth weirs (LW). This study proposes a hydraulic-informed multi-layer perceptron (HI-MLP) by incorporating the effects of hydraulic phenomena on C d , which were previously neglected due to their immeasurability. The HI-MLP is not a black box and selects its internal parameters considering the nape behavior attributes. HI-MLP is trained using Levenberg–Marquardt and Genetic algorithms, resulting in HI-MLP-LM and HI-MLP-GA, which are compared against adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and standard MLP. Evaluating the models through randomly selected testing data shows that HI-MLP-GA is the most accurate, with an MAEP of 0.732%, while all techniques also had acceptable performances. Conversely, when estimating C d for LWs with excluded intermediate and extrapolated geometries, only HI-MLP-LM and HI-MLP-GA could predict accurate values with average MAEP of 1.94% and 0.62%, respectively. This value in ANFIS and SVR was equivalent to 27.01% and 6.47%, respectively. Furthermore, hydraulic-informed techniques could be trained with at least a 25% smaller dataset, while the robustness analysis indicated that they are less prone to overfitting. HI-MLP-GA has the highest computational effort and is almost five times slower than HI-MLP-LM. Nevertheless, due to the disappearance of the vanishing gradient and considering the higher generalizability of HI-MLP-GA, GA is still a more desirable learning algorithm than LM. [ABSTRACT FROM AUTHOR]

In actual air combat, there is an inevitable risk that an unmanned aerial vehicle (UAV) will be destroyed. However, this risk is rarely considered in the mission planning phase. In this paper, we focus on cooperative mission assignment for heterogeneous UAVs. We develop a multi-objective optimization model to find a balance between mission gains and UAV losses. The objective function is expressed using conditional probability theory by introducing the probabilities of mission success and UAV loss. Munitions loading capacity, time constraints, and priority constraints are modeled as constraints. To solve this combinatorial problem, an improved multi-objective genetic algorithm, which incorporates a natural chromosome encoding format and specially designed genetic operators, is developed. An efficient unlocking method is constructed to address the unavoidable dead-lock phenomenon meanwhile maintaining the population randomness. Numerical simulations for different problem sizes and ammunition stocks are performed, and the proposed algorithm is compared with the Multi-objective Particle Swarm Optimization and the Multi-objective Grey Wolf Optimization, respectively, using different unlocking approaches. The simulation and comparison results demonstrate the practical value and effectiveness of the developed model and the proposed algorithm. • A risk-based multi-objective model for cooperative task assignment is established. • An improved genetic algorithm is proposed with all ammunition situations considered. • A novel fast positioning unlocking method is given to improve unlocking efficiency. [ABSTRACT FROM AUTHOR]

One of the major economic activities of a business concern is transportation of its products without any hassle. Therefore, the research on a transportation problem is very important now-a-days. In a classical transportation problem, the amount incurred for shipping the goods from the source to a destination is determined on the basis of unit transportation cost. But now, in many practical cases, it is seen that the unit transportation cost is settled on the basis of transported amount. In such a direction, this article develops a transportation problem to transport the produced items from a production house to some retailers through some distributors. The system has two cost parameters, one is actual unit transportation cost and other is demanded unit transportation cost. The actual unit transportation cost is the cost incurred during the transportation while the demanded unit transportation cost is the cost demanded by the distributors. Here, the demanded unit transportation cost has been decided depending on the amount of transported item. Since in the system, there are more than one distributor, distributors offer a credit period policy to attract the retailers. On the basis of the deterioration of the loaded amount, the transported items are classified into two categories. For the higher deteriorated items, the transportation cost is relatively larger and for the lower deteriorated items, the transportation cost is relatively lower with the increase in the loading amount. Considering the above two categories, two crisp models are developed. Due to the uncertain information of the actual unit transportation cost, the models are illustrated considering the actual unit transportation cost as a triangular intuitionistic fuzzy number. The main objective of this research is to minimize the total cost of the retailers and to maximize the total profit of the distributors simultaneously under the above circumstances. For crispification, two different methods have been used here — one is an existing method namely ordered weighted average operator and other is a developed average value approach. Also, the elitist non-dominated sorting genetic algorithm (NSGA-II) and the fuzzy programming technique have been used to get the optimal solution of this problem. To justify the impact and significance of the proposed models on this decision making system, some numerical studies have been done and it is observed that distributors and retailers both benefit more under the credit period policy than the policy without credit period. This study discusses the situation under which the uncertain parameters are to be expressed in terms of triangular intuitionistic fuzzy numbers. Also, it is identified that more accurate crispification has been done by the average value approach than the existing approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-vehicle clustered traveling purchaser problem (MVCluTPP). Here, two types of procurement planning are proposed. In the first setup, the purchaser visits the markets clusterwise and, after satisfying the demand, returns to the depot with the purchased products, which are carried on the same path using a different vehicle. The other set up, in which products are purchased clusterwise and transported directly from the center of the cluster to the depot, but with the mandate that the purchaser visits the markets clusterwise. One of the multi-pronged aims of the model is to select the clusters and identify which markets to visit, determine the amount of procurement available in each market in a cluster, and develop an optimal routing plan in such a way that the overall system cost is minimized. The clusters are generated using k-means algorithm, and a variable-length chromosome genetic algorithm (VLC-GA) is proposed to optimize the cluster paths and to use a local heuristic to link the clusters to minimize the system cost. Furthermore, the superiority of the VLC-GA has been established through standard TPP and TSP instances, compared with exact methods, and some statistical tests are presented. • The mathematical development of the clustered traveling purchaser problem. • Cluster-wise procurement planning in different scenarios. • A variable-length genetic algorithm is developed. • The developed algorithms outperform classical algorithms. [ABSTRACT FROM AUTHOR]

Task-oriented design of modular robot topology is a crucial issue to be solved in modular robot research. In this paper, a Genetic algorithm-based optimal design strategy for the modular robot topology is proposed. Four tuples are used to represent the modular robot topology. By establishing a database of motion transmission forms for a modular unit, a distributed parallel kinematic modeling and analysis method for modular robots is introduced. Taking the four-tuple representation of the modular robot topology as decision variables, an optimization model of the topology design for modular robots is established. To solve the model, under the framework of the Genetic algorithm, the genotype and genetic operations are designed to improve the effectiveness of individual generating in the optimization process. Finally, a modular robot is taken as an example to carry out simulations. It indicates that the modular robot under the designed topology can successfully execute tasks with as few modular units as possible, and the computing time for the kinematics modeling and analysis of modular robots can be reduced by at least an order of magnitude. • Topology is a type of information about the connection state of a modular robot. • Topology design can make modular robots more adaptive to tasks. • The application of optimization can improve the efficiency of topology design. • Robot kinematics with modular modeling and parallel computing has high efficiency. [ABSTRACT FROM AUTHOR]

The hub location is a major problem in the systems with high dependence on the transportation of goods, information, and passengers between the constituent parts. The present study is aimed to introduce a multi-commodity multi-model hierarchical hub location problem with uncertain demand. Thus, it is estimated by intuitionistic fuzzy variables. The objective function in the present problem is to minimize the total transportation costs in the network to determine the optimal hub location, allocate non-hub nodes to the hubs and the ground hubs to the air hubs, and identify the type of vehicles required in each route. The model has a hierarchical structure consisting of a three-echelon network (i.e., central hubs, non-central hubs, and demand nodes) connected in the star-tour-star form. A four-index mathematical model was presented and solved by GAMS optimization software. The results indicated that the objective function (cost) has a descending trend with increasing the number of hubs which can be assigned to the decline in the transportation costs. The costs increased by a decrease in the maximum delivery time which could be due to the selection method of the transportation mode. In the multi-product case, the costs declined by increasing the number of hubs as these hubs decremented the network complexity. Regarding the NP-Hard nature of the hub problems, a genetic meta-heuristic algorithm was proposed in the MATLAB software to solve the large-scale problem. According to the results, this algorithm offered close-optimal solutions. Noteworthy, this study employed known AP datasets. [ABSTRACT FROM AUTHOR]

This paper addresses the rescue unit allocation and scheduling problem (RUASP) with fuzzy processing times through an evolutionary approach. The goal of RUASP is to efficiently assign and schedule the rescue units to process the incidents in the event of a natural disaster. This problem can be considered as a variant of the unrelated parallel-machine scheduling problem with sequence and machine-dependent setup times. A steady-state grouping genetic algorithm (SSGGA) approach is presented to minimize the total weighted completion time of the incidents, where the weights correspond to the severity levels of the incidents. The crossover and mutation operators used in this approach are designed as per the characteristics of RUASP and its objective. The proposed approach uses a combination of greedy and random heuristics while generating the initial solutions, thereby yielding superior quality diverse initial solutions. The performance of the proposed approach is compared with the state-of-the-art approach available in the literature. The proposed approach always yields better results in much shorter execution times in comparison to the state-of-the-art approach. Moreover, the robustness analysis test demonstrates the proposed approach to be more robust in comparison to the state-of-the-art approach. [ABSTRACT FROM AUTHOR]

Skin cancer is defined as the rapid growth of skin cells due to DNA damage that cannot be repaired. Melanoma is one of the deadliest types of skin cancer, which originates from melanocytes. While other skin cancer types have limited spreading capabilities, the danger of melanoma comes from its ability to spread (metastasize) rapidly. Fortunately, it can be detected by visual inspection of the skin surface, and it is 100% curable if identified in the early stages. However, detection by “subjective visual inspection” creates an important problem, due to investigators’ different levels of experiences and education. Dermoscopy (dermatoscopy) has significantly increased the diagnostic accuracy of melanoma since late 90’s. In addition, several systems have been proposed in order to assist investigators or to perform an automatic melanoma detection. This survey focuses on the algorithms for automated melanoma detection in dermoscopic images through an extensive analysis of the stages in methodologies proposed in the literature, and by examining related concepts and describing possible future directions through open problems in this domain of research. [ABSTRACT FROM AUTHOR]

In this paper, a methodology has been proposed to decrease the solution time of the joint energy-reserve market clearing problem under Payment Cost Minimization (PCM) mechanism. PCM mechanism is recently proposed to replace the available economically-inefficient clearing mechanisms in electricity markets. While the exact methods have been successfully applied to solve the market clearing problem under available mechanisms, their computational efficiency is poor when applied to solve this problem under PCM mechanism due to special characteristics of PCM formulation. The proposed solution methodology uses linear programming along with Mixed Integer Genetic Algorithm (MIGA) to minimize the payment cost. Most of efforts are focused on speeding up the applied optimization technique, since the available frameworks for solving the PCM problem suffer from very slow convergence. Different custom-designed functions have been added to the basic MIGA to decrease the solution time. The results are compared to those obtained using branch and cut technique. [ABSTRACT FROM AUTHOR]

This novel article presents the multi-objective version of the recently proposed the Grey Wolf Optimizer (GWO) known as Non-Dominated Sorting Grey Wolf Optimizer (NSGWO). This proposed NSGWO algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutions in achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and leadership hierarchy of grey wolfs in nature to guide hunting of wolfs towards the dominated regions of multi-objective search spaces. For validate the efficiency and effectiveness of proposed NSGWO algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSGWO algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS). The results of proposed NSGWO algorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage and faster convergence. [ABSTRACT FROM AUTHOR]

The efficiency and safety of a solar central receiver system depend on the flux distribution reflected by the heliostat field on its receiver. Thus, the field must be carefully controlled to avoid dangerous radiation peaks and temperature gradients while also maximizing the efficiency of the system. Control tasks include deciding which heliostats to activate and where to aim them. The field is usually under direct human supervision, which is a potential limitation, and automatic aiming procedures are of great interest. This work proposes a general aiming methodology for flat-plate receivers. It intends to cover heliostat selection and aim point assignation to replicate any given reference flux distribution on the receiver. The methodology, which addresses this situation as a large-scale optimization problem, defines two consecutive stages. The first one handles heliostat selection by applying a specific genetic algorithm. The second one, based on a local gradient descent, assigns a final aim point to every active heliostat. The proposed methodology, in contrast to other existing methods in the literature, is not limited to achieve any specific target distribution. It exploits the analytical characterization of the considered field to minimize the accumulated squared error between any reference flux distribution and the achieved one. The results show very good replication quality and, considering its execution time, this method is suitable for preliminary and high-resolution field configuration. [ABSTRACT FROM AUTHOR]

Reduction on both switching and leakage power has become a research focus in VLSI design. It makes sense that finite-state machine (FSM) as a main component can contribute to low power of VLSI circuit. Decomposition has been proven an effective strategy for saving power in FSM synthesis. In order to achieve low power in FSM synthesis, a Fuzzy c -mean clustering-based decomposition method, called FCM-D, is proposed in this study. FCM-D used Fuzzy c -mean clustering (FCM) method to partition a set of states of FSM into a collection of c fuzzy clusters; hence, a FSM can be decomposed into a set of c sub machines, of which the inactive ones would not consume the power. For achieving low power, the objective function of FCM-D is defined on minimization of the cross state transition probability between sub machines and increasing the inner state transition probability within each submachine. For overcoming the local optimum, Genetic Algorithm (GA) is employed as an optimizer of FCM-D, which applies selection, crossover and mutation on FCM-D to generate better centers and more appropriate clusters. This hybrid method is denoted as FCM-D+GA. We test FCM-D+GA extensively on more than thirty benchmarks in LGSynth93 library, and compare it with previous FSM synthesis methods from various aspects. The experimental results show FCM-D+GA achieved a significant cost reduction of switching power and leakage power dissipation over the previous publications. [ABSTRACT FROM AUTHOR]

This article discusses the development of an adaptive protection algorithm which is based on a physical approach, with the purpose to keep a closed loop aircraft with manual control laws within the actual safe flight envelope, even in the presence of failures or disturbances. Adaptive estimation of the flight envelope guarantees that not only flap changes, but also damage (e.g. icing) and external disturbances such as wind can be taken into account. This method is robust with respect to uncertainties in the estimates for the aerodynamic properties. This updated information makes the flight control laws more self-preserving and prevents loss of control in flight. This development can extend the functional envelope of the nominal law and reduce the need to switch from nominal to alternate law in the presence of certain failures. This algorithm has been applied on a simulation model of a medium range passenger aircraft and the setup has been implemented and evaluated in the DLR Robotic Motion Simulator at the German Aerospace Center as a proof of concept demonstration. [ABSTRACT FROM AUTHOR]

This work studies one multi-objective immune genetic algorithm with small population to solve a general kind of unconstrained multi-objective interval-valued programming. In this optimization approach, those competitive individuals are discriminated based on interval arithmetic rules and a possibility model; a crowding degree model in interval-valued environments is developed to eliminate redundant individuals; the current population promotes different individuals to evolve towards specific directions by population sorting and immune evolution, while those elitist individuals found accelerate to explore the desired regions through genetic evolution. The theoretical analysis has showed that the computational complexity of the proposed approach depends mainly on the elitist population size. Comparative experiments have illustrated that the approach can take a rational tradeoff between effect and efficiency. It can perform well over the compared approaches as a whole, and has the potential to solving multi-modal and hard multi-objective interval-valued programming problems. [ABSTRACT FROM AUTHOR]

In this paper, an optimal control scheme based on the application of genetic algorithm (GA) is applied to a tracker robot with machine vision capability. First, dynamic equations governing the robot system are extracted and then, kinematic relationships are acquired according to camera specifications. Then, the obtained open-loop model of the robot is simulated and its validity is evaluated through an experimental work. The computed torque control algorithm with conventional tuning technique is initially used to control the robotic system. However, it is found that tuning the mentioned robot controller via the conventional technique is not so effective due to the unseen hard non-linearity presented in the robot controller such as saturation of the actuators. Consequently, a more advanced GA-based optimal control scheme is proposed in this investigation. Thus, GA is used to obtain the optimum values of control constants based on a suitable cost function. Accordingly, a simulation study is conducted to highlight the superiority of the GA-based controller compared to the former algorithm. Besides, the linking approach of the vision subsystem to the robot controller is further described from both mathematical and experimental standpoints. Finally, the proposed visual tracker robot is developed and its performance to track a moving object on a circular as well as a butterfly-shape trajectory in the camera coordinates is investigated. The experimental results clearly reveal the effectiveness of the visual robot incorporating the optimal controller to follow the target trajectories and confirm the stability of the control system. [ABSTRACT FROM AUTHOR]

Heuristic stochastic optimization techniques such as genetic algorithm perform global search, but they suffer from the problem of slow convergence rate near global optimum. On the other hand deterministic techniques such as gradient descent exhibit a fast convergence rate around global optimum but may get stuck in a local optimum. Motivated by these problems, a hybrid learning algorithm combining genetic algorithm (GA) with gradient descent (GD), called HGAGD, is proposed in this paper. The new algorithm combines the global exploration ability of GA with the accurate local exploitation ability of GD to achieve a faster convergence and also a better accuracy of final solution. The HGAGD is then used to train a qubit neural network (QNN), which is a good candidate for enhancing the computational efficiency of conventional neural networks, for two different applications. Firstly, a benchmark function is chosen to illustrate the potential of the proposed approach in dealing with function approximation problem. Subsequently, the feasibility of the proposed method in designing an indirect adaptive controller for damping of low frequency oscillations in power systems is studied. The results of these studies show that the proposed controller trained by the HGAGD can achieve satisfactory control performance. [ABSTRACT FROM AUTHOR]

The difficulty of warship decoy system design problem is twofold. First, we need to find not just one but as many optimal solutions as possible. Second, it demands a heavy computation to evaluate a candidate solution through a long series of underwater warfare simulations. The previous approach tried to reduce the amount of search by heuristically selecting a set of plausible starting points for the search by a simulated annealing algorithm. However, it shows only limited success and cannot easily scale up to larger problems. This paper proposes an efficient and easy-to-scale-up multimodal optimization algorithm named A-NTGA that is based on a genetic algorithm. A-NTGA quickly evaluates candidate solutions by conducting only a small number of simulations, but instead copes with these inaccurate or noisy fitness values by using a noisy optimization technique. To further enhance the efficiency of search by promoting the population diversity, A-NTGA is provided with an archive to which some good-looking solutions are migrated in order to prevent the population from being too crowded with similar solutions. Usually at the end of the search, many optimal solutions are retrieved from the archive as well as the population. The experimental results show that our method can find multiple optimal solutions more efficiently compared to other methods and can be easily scaled up to larger problems. [ABSTRACT FROM AUTHOR]

By introducing a genetic algorithm with a classifier system as a learning mechanism for uninformed traders into a dynamic limit order market with asymmetric information, this paper examines the effect of the learning on traders’ trading behavior, market liquidity and efficiency. We show that the learning is effective and valuable with respect to information acquisition, forecasting, buy–sell order choice accuracies, and profit opportunity for uninformed traders. It improves information dissemination efficiency and reduces the information advantage of informed traders and hence the value of the private information. In particular, the learning and information become more valuable with higher volatility, less informed traders, and longer information lag. Furthermore, the learning makes not only uninformed but also informed traders submit more limit orders and hence increases market liquidity supply. [ABSTRACT FROM AUTHOR]

Floods are natural phenomena that invade different parts of the globe annually, leaving severe adverse effects on the natural landscape, particularly in areas where humans practice large-scale urban development activities. Inadequate corporate strategic stormwater management or maintenance plans make the issue even worse. Therefore, efforts have been consolidated to confront flood hazards by incorporating precautious and proactive strategies toward better forecasting and preventing flood disasters or even mitigating the damage if it occurs. Further, there is inadequacy in incorporating computational intelligence techniques to detect flood susceptibility in road networks. This paper proposes a data-driven flood risk areas detection model incorporating various ensemble and generic machine learning techniques as well as autoML tools. A systematic and iterative approach is followed to examine different solutions to tackle the data balancing problem, including oversampling and undersampling techniques. Also, hyperparameter optimisation is undertaken using both grid search and genetic algorithms, and the models' performance using each designated strategy is reported and assessed. The empirical results from the experiments indicate that the optimised ensembled Extremely Randomized Trees classifier proves an ability to perform well on the given dataset with an averaged ROC AUC value of 90% and outperforms several state-of-the-art generic, ensembled, and autoML classifiers in all evaluation measures. The proposed framework could be possibly generalised to other road networks in similar topographical and geological regional areas. [ABSTRACT FROM AUTHOR]

The accurate and reasonable prediction of natural gas consumption is important for enhancing national energy security and alleviating the environmental problems caused by energy consumption. Starting with the traditional nonlinear grey Bernoulli model, in this paper, we expand the development coefficient a and grey action quantity b, propose a new self-adaptive time-varying grey Bernoulli prediction model, deduce the time response formula of the model, and explore the relationship between model parameters and model accuracy. Then, the nonlinear parameter and power parameter coefficient of the new model are optimized by a genetic algorithm, and the effectiveness of the model is analyzed by the natural gas consumption of China, the United States, and Russia. The results show that, compared with other traditional grey prediction models, the new model has a better simulation effect, higher prediction accuracy, and stronger applicability. Meanwhile, the model was used to forecast the natural gas consumption of China, the United States, and Russia in the period from 2022 to 2026. The prediction results show that China's natural gas consumption will continue to steadily increase in the future, which is consistent with the global goal of adhering to the orderly development of natural gas. The prediction results can provide a theoretical basis for relevant departments to formulate relevant natural gas policies. [ABSTRACT FROM AUTHOR]

Area and power optimization of Fixed Polarity Reed–Muller (FPRM) circuits has received a lot of attention. Polarity optimization for FPRM circuits is essentially a binary multi-objective optimization problem. However, the existing area and power optimization approaches for FPRM logic circuits rarely produce a frontier and a greater number of Pareto optimal solutions. In this paper, a Multi-strategy Multi-objective Artificial Bee Colony (MMABC) algorithm is proposed to solve the binary multi-objective optimization problem. The main innovation of MMABC can be summarized as follows: a flexible foraging behavior strategy for employed bees is proposed to improve the searching ability of the algorithm; a genetic retention evolution for onlooker bees is proposed to improve the quality of the population; an efficient transform strategy is proposed to help the algorithm to jump out the local optimal and increase convergence speed. Moreover, we propose an area and power optimization approach for FPRM logic circuits, which uses the MMABC to search for the polarities (i.e., Pareto optimal solutions) with smaller area and lower power. Experimental results demonstrated the effectiveness and superiority of our approach in optimizing area and power of FPRM logic circuits. [ABSTRACT FROM AUTHOR]

The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model's efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use. [ABSTRACT FROM AUTHOR]

The optimal layout of a complex system such as aerospace vehicles consists in placing a given number of components in a container in order to minimize one or several objectives under some geometrical or functional constraints. This paper presents an extended formulation of this problem as a variable-size design space (VSDS) problem to take into account a large number of architectural choices and components allocation during the design process. As a representative example of such systems, considering the layout of a satellite module, the VSDS aspect translates the fact that the optimizer has to choose between several subdivisions of the components. For instance, one large tank of fuel might be placed as well as two smaller tanks or three even smaller tanks for the same amount of fuel. In order to tackle this NP-hard problem, a genetic algorithm enhanced by an adapted hidden-variables mechanism is proposed. This latter is illustrated on a toy case and an aerospace application case representative to real world complexity to illustrate the performance of the proposed algorithms. The results obtained using the proposed mechanism are reported and analyzed. [ABSTRACT FROM AUTHOR]

Tool path optimization approaches are often used to reduce production time and cost, energy consumption, etc. on Computer Numerical Control Machines. Thus, several artificial intelligence algorithms based on Traveling Salesman Problem (TSP) have been implemented to optimize tool trajectory length in various production sectors mainly the holes drilling process. In this field, this article exhibits first a related works survey detailing the main used algorithms. Then, it suggests a novel approach for optimizing non-productive tool paths for drilling a hole series. The proposed approach entitled Adaptive-Dhouib-Matrix-3 (A-DM3) is based on combining the iterated stochastic Dhouib-Matrix-3 (DM3) with a tabu memory inspired by Tabu Search (TS) metaheuristic. To confirm its ability and stability to find the shortest drilling tool path, A-DM3 method is tested on six practical case studies of a rectangular matrix of holes. Further, it is compared to some commonly used algorithms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and some of its derivates, modified Shuffled Frog Leaping Algorithm (mSFLA), Cuckoo Search (CS), and hybrid Cuckoo Search Genetic Algorithm (CS-GA). Computational results proved the superiority of the proposed A-DM3 compared to these well-known metaheuristics in literature, particularly in a medium and large number of holes. Hence, A-DM3 provided a new shortest path length record with an improvement exceeding 100% in some cases compared to competing algorithms. [ABSTRACT FROM AUTHOR]

Meal delivery services is an enormous competitive market, and the most influential factor in this market is customer experience. As a result, it is crucial to have an efficient delivery system that ensures customer satisfaction regarding on-time, fresh delivery of meals. Accordingly, taking advantage of novel modes of transportation and developing relevant planning approaches can help companies preserve their competitive edge. In this context, the present paper aims to optimize the delivery operations of a homemade meal delivery start-up by adding drones and crowdsourcing, as two innovative modes, to its current system. The addressed problem is an extension of a mixed closed-open pickup and delivery vehicle routing problem. It includes multi-modal transportation fleet and time windows; besides, the meals are time-sensitive and the orders may need to be synchronized. For this purpose, first, a multi-objective mathematical model is devised that considers transportation costs, freshness of the delivered meals, and due-date satisfaction as the objective functions. Afterwards, an efficient self-adaptive hyper-heuristic method is developed to deal with the complexity of the problem. This hyper-heuristic method is based on genetic algorithm and modified particle swarm optimization, and incorporates novel selection and mutation mechanisms. Applying the model to a case study demonstrated that employing drones and crowdsourcing entails 13.7%, 8.5%, and 20.7% improvement in the cost, meal freshness, and weighted due-date satisfaction, respectively. • Developing a multi-objective model for vehicle routing problem for picking up and delivering homemade food. • Prioritizing customers base on their buying behavior and using multi-criteria decision-making techniques. • Employing drone, and crowd-sourced deliverers to deliver homemade food. • Applying a new selection strategy as well as a new mutation strategy to the developed metaheuristics. • Developing a novel efficient self-adaptive hyper-heuristic method for solving large-sized instances. [ABSTRACT FROM AUTHOR]

One major capability of a Deep Reinforcement Learning (DRL) agent to control a specific vehicle in an environment without any prior knowledge is decision-making based on a well-designed reward shaping function. An important but little-studied major factor that can alter significantly the training reward score and performance outcomes is the reward shaping function. To maximize the control efficacy of a DRL algorithm, an optimized reward shaping function and a solid hyperparameter combination are essential. In order to achieve optimal control during the powered descent guidance (PDG) landing phase of a reusable launch vehicle, the Deep Deterministic Policy Gradient (DDPG) algorithm is used in this paper to discover the best shape of the reward shaping function (RSF). Although DDPG is quite capable of managing complex environments and producing actions intended for continuous spaces, its state and action performance could still be improved. A reference DDPG agent with the original reward shaping function and a PID controller were placed side by side with the GA-DDPG agent using GA-optimized RSF. The best GA-DDPG individual can maximize overall rewards and minimize state errors with the help of the potential-based GA(PbGA) searched RSF, maintaining the highest fitness score among all individuals after has been cross-validated and retested extensively Monte-Carlo experimental results. [ABSTRACT FROM AUTHOR]

Traffic signals play a significant role in the urban transportation system. They control the movement of traffic on urban streets by determining the appropriate signal timing settings. Due to the stochastic nature of the traffic flow, deciding on the best signal timing settings is a computationally complex problem, with the result that traditional analytical methods have been found to be inadequate in dealing with real world scenarios. This issue has already been tackled using computational intelligence algorithms such as the genetic algorithm (GA). However, despite good results, GA may experience slow convergence, especially when dealing with constrained optimisation problems. To address this issue, we propose an adaptive memetic algorithm (MA) for optimising signal timings in real world urban road networks using traffic volumes derived from induction loop detectors. The proposed MA combines the strengths of GA with the exploitation power of a local search algorithm, in an adaptive manner, so as to accelerate the search process and generate high quality solutions. In this work, we propose two important techniques for improving the performance of a traditional MA. First, we use a systematic neighbourhood based simple descent algorithm as a local search to effectively exploit the search space around GA solutions. Second, to achieve a proper balance between the exploration of GA and the local search algorithm, we propose an indicator scheme to control the local search application based on the diversity and the quality of the search process. The proposed MA was tested in two different case studies for the cities of Brisbane, Australia, and Plock, Poland, using the well-known microscopic traffic simulator, AIMSUN. Results demonstrate that our MA is better than GA and traditional fixed-time traffic signal settings. [ABSTRACT FROM AUTHOR]

This study proposes a new kernel extreme learning machine (KELM) parameter tuning strategy using a novel swarm intelligence algorithm called grey wolf optimization (GWO). GWO, which simulates the social hierarchy and hunting behavior of grey wolves in nature, is adopted to construct an effective KELM model for bankruptcy prediction. The derived model GWO-KELM is rigorously compared with three competitive KELM methods, which are typical in a comprehensive set of methods including particle swarm optimization-based KELM, genetic algorithm-based KELM, grid-search technique-based KELM, extreme learning machine, improved extreme learning machine, support vector machines and random forest, on two real-life datasets via 10-fold cross validation analysis. Results obtained clearly confirm the superiority of the developed model in terms of classification accuracy (training, validation, test), Type I error, Type II error, area under the receiver operating characteristic curve (AUC) criterion as well as computational time. Therefore, the proposed GWO-KELM prediction model is promising to serve as a powerful early warning tool with excellent performance for bankruptcy prediction. [ABSTRACT FROM AUTHOR]

There are significant challenges related to estimating the source term of the atmospheric release. Urged on by robots in performing emergency responding tasks, a fast and accurate algorithm for this inversion problem is indispensable. Sometimes the NM simplex algorithm is efficient in the optimization problem, but sometimes the quality of convergence is unacceptable as a numerical breakdown, even for smooth and well-behaved functions. In contrast, full convergence might be seen in parallel genetic algorithms with a comparative slower convergence. In this paper we combine the PGA and the NM simplex algorithm by initializing simplex from the final individual of PGA results and obtaining the best vertex through simplex algorithm thereafter. A numerical simulation of the proposed algorithm shows noteworthy improvement of efficiency and robustness, compared with the PGA or the NM algorithm only. [ABSTRACT FROM AUTHOR]

In this paper we investigate the efficiency of two immunological algorithms (CLONALG and opt-IA) in the evolution of Boolean functions suitable for use in cryptography. Although in its nature a combinatorial problem, we experiment with two representations of solutions, namely, the bitstring and the floating point based representation. The immunological algorithms are compared with two commonly used evolutionary algorithms – genetic algorithm and evolution strategy. To thoroughly investigate these algorithms and representations, we use four different fitness functions that differ in the number of parameters and difficulty. Our results indicate that for smaller dimensions immunological algorithms behave comparable with evolutionary algorithms, while for the larger dimensions their performance is somewhat worse. When considering only immunological algorithms, opt-IA outperforms CLONALG in most of the experiments. The difference in the representation for those algorithms is also clear where floating point works better with smaller problem sizes and bitstring representation works better for larger Boolean functions. [ABSTRACT FROM AUTHOR]

Maritime inventory routing problem is addressed in this paper to satisfy the demand at different ports during the planning horizon. It explores the possibilities of integrating slow steaming policy as mentioned in Kontovas et al. (2011) and Norstad et al. (2011) within ship routing. A mixed integer non-linear programming model is presented considering various scheduling and routing constraints, loading/unloading constraints and vessel capacity constraints. Non-linear equation between fuel consumption and vessel speed has been incorporated to capture the sustainability aspects. Several time window constraints are inculcated in the mathematical model to enhance the service level at each port. Penalty costs are incurred if the ship arrives early before the starting of the time window or if it finishes its operation after the ending of the time window. Costs associated with the violation of time window helps in maintaining a proper port discipline. Now, owing to the inherent complexity of the aforementioned problem, an effective search heuristics named Particle Swarm Optimization for Composite Particle (PSO-CP) is employed. Particle Swarm Optimization – Differential Evolution (PSO-DE), Basic PSO and Genetic Algorithm (GA) are used to validate the result obtained from PSO-CP. Computational results provided for different problem instances shows the superiority of PSO-CP over the other algorithms in terms of the solution obtained. [ABSTRACT FROM AUTHOR]

One of the important problems in heterogeneous computing systems is task scheduling. The task scheduling problem intends to assigns tasks to a number of processors in a manner that will optimize the overall performance of the system, i.e. minimizing execution time or maximizing parallelization in assigning the tasks to the processors. The task scheduling problem is an NP-complete and this is why the algorithms applied to this problem are heuristic or meta-heuristic by which we could reach a relatively optimal solution. This paper presents a genetic-based algorithm as a meta-heuristic method to address static task scheduling for processors in heterogeneous computing systems. The algorithm improves the performance of genetic algorithm through significant changes in its genetic functions and introduction of new operators that guarantee sample variety and consistent coverage of the whole space. Moreover, the random initial population has been replaced with some initial populations with relatively optimized solutions to lower repetitions in the genetic algorithm. The results of running this algorithm on the graphs of real-world applications and random graphs in heterogeneous computing systems with a wide range of characteristics, indicated significant improvements of efficiency of the proposed algorithm compared with other task scheduling algorithms. [ABSTRACT FROM AUTHOR]

A major development of the offshore wind energy market is being witnessed. Since the implicated costs are considerably high, it is extremely important to ensure that the energy production is maximum, so that the costs per energy unit are minimized. Thus, the turbines should be strategically positioned to extract as much energy as possible from the wind, considering wake effect losses, as well as internal grid electrical losses. In order to avoid turbines to be placed in unrealistic positions, they should be distributed according to a grid of rectangular shaped cells; each of these is divided in multiple sub-cells. The problem of finding the turbines optimal position among the pre-defined sub-cells so that maximum annual energy is produced could be addressed using a deterministic approach. However, the problem becomes unfeasible when the number of turbines and/or the number of sub-cells increase. To overcome this difficulty, optimization techniques should be used. Genetic Algorithm and Particle Swarm Optimization are approached in this paper. This paper deals with the wind park layout optimization problem. A methodology to position the turbines inside a wind park so that the annual energy production is maximum is proposed. The results proved that the meta-heuristic method is much more CPU time efficient in providing the maximum annual year production as compared to the traditional deterministic approach. [ABSTRACT FROM AUTHOR]

A Home Health Care Routing and Scheduling Problem (HHCRSP) is one of the most practical branches in Home Health Care (HHC) optimization. The main focus of this study is to investigate an HHCRSP with both soft and Hard Time Windows (HTWs) associated with caregivers and patients, respectively. Furthermore, five different types of soft temporal dependency constraints are considered, which specify different relations amongst the starting time of dependent visits. Accordingly, a bi-objective Mixed-Integer Programming (MIP) model is devised to incorporate staff rostering, vehicle routing, and scheduling simultaneously. This model aims at minimizing the system's total cost while maximizing the total satisfaction of patient preference. Since the problem is NP-hard, Iterated Local Search (ILS) is applied to solve large-sized problems in high frequencies and within reasonable computational time. Computational results on some real-world-inspired benchmark instances highlight the overall efficiency of the employed algorithm compared to the Non-dominated Sorting Genetic Algorithm (NSGA-II). • Presenting a bi-objective mixed-integer programming model for a home health care routing and scheduling problem. • Applying patient availability and all types of temporal dependencies in the form of soft constraints. • Considering uncovered visits, patient preference, qualification level of caregivers, and hard and soft time windows. • Adapting an iterated local search (ILS) algorithm to cope with the complexity of the large-sized instances. • Comparing the results obtained by the ILS algorithm with the non-dominated sorting genetic algorithm. • Conducting numerical experiments to evaluate the efficiency of the proposed approach. [Display omitted] [ABSTRACT FROM AUTHOR]

Pumps consume nearly 8% of global electricity as the essential equipment for liquid transportation. A practical method for improving centrifugal pump energy efficiency is accurately predicting and controlling the pump operation status. However, current estimation methods for sensorless flow rate prediction have a significant error at low flow rate conditions. This study adds valve opening as the estimation model input variable, including motor shaft power and speed, to form a back-propagation neural network (BPNN) on an asynchronous motor-driven multistage centrifugal pump. By optimizing the initial weights and thresholds of BPNN, a GA-BPNN model was proposed to improve the prediction accuracy by using a genetic algorithm (GA). The results indicate that, with the addition of the valve opening as an input variable, the accuracy of BPNN-VO and GA-BPNN prediction improves significantly more than BPNN-NVO. Furthermore, the GA-BPNN model produces a significantly lower mean square error (MSE) and root mean square error (RMSE) than the original BPNN model. According to the experimental comparison and analysis, the absolute error of GA-BPNN between predicted flow rate and measured flow rate is less than 0.3 m3/h, the average relative error is less than 2%, and the relative error of low flow rate is less than 5%. This GA-BPNN estimation model significantly improves the accuracy of flow rate prediction, especially at small flow rates, and extends the scope of centrifugal pumps' monitoring and control technology without flow sensors. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are approximate solving methods that have been originally proposed to achieve unconstrained optimization. To handle constrained problems, which is the case for the majority of real-life circumstances, GAs must be equipped with a constraint-handling mechanism. Transformation functions (TFs) are among the constraint-handling approaches that intervene in the phenotypic space. In this paper, we study the impact of considering constraint priorities on the GA performance when it deals with heavily constrained problems. Priorities are set by integrating a constraint order into the TF definition. We consider different TF forms enhanced with a fuzzy inference engine to find the best constraint ordering. Finally, we conduct an experimental study to assess the performance of the proposed approach on the semi-supervised graph partitioning problem. The obtained results show with statistical evidence that the proposed fuzzy method is promising. • This article proposes a fuzzy constraint handling approach for the genetic algorithm (GA) to solve heavily constrained problems. • The fitness calculation uses transformation functions that reorder the constraints. • Constraint reordering allows the GA to penalize infeasible solutions without losing the good schemata they hide. • A fuzzy inference system seeks the best constraint prioritization to boost the genetic algorithm performances. • A comparative study investigates the performance of the proposed approach against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Having passed the primitive phases and starting to revolutionize many different fields in some way, artificial intelligence is on its way to becoming a disruptive technology. It is also foreseen to totally change human-centred traditional engineering design approaches. Although still in the early phases, AI-powered engineering applications enable them to work with ambiguous design parameters and solve complex engineering problems, not otherwise possible with traditional design methods. This work attempts to shine a light on current progress and future research trends in AI applications in design/engineering design concepts, covering the last 15 years which is the ramp-up period for AI. Methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully examined from an engineering design perspective. AI-powered design studies have been categorized and critically reviewed for various design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modelling. As an overview result of this review, we can confidently say that the interest in data-based design methods and Explainable Artificial Intelligence (XAI) has increased in recent years. Furthermore, the use of AI methods in engineering design applications helps to obtain efficient, fast, accurate, and comprehensive results. Especially with deep learning methods and combinations, situations where human capacity is insufficient can be addressed efficiently. However, choosing the right AI method for a design problem under consideration is significantly important for such successful results. Hence, we have given an outline perspective on choosing the right AI method based on the literature outcomes for design problems. [ABSTRACT FROM AUTHOR]

It is a common action that the unmanned aerial vehicles (UAVs) change the formation during the flight to realize different goals and address an emergency. In this process, the UAVs should determine their positions in the new formation to avoid the collision and reduce the flight time. In this paper, the position allocation problem for multiple UAV formations is studied, and the problem is solved considering the different requirements on offline and online cases. In the offline case, the trajectories of UAVs when forming new formations are calculated by the consensus-based trajectory planning (CBTP) method, in which the transient process is introduced to avoid the collision among UAVs. Then a hybrid genetic and simulated annealing (HGSA) algorithm, which utilizes the framework of genetic algorithm (GA) and the operators in simulated annealing (SA) algorithm, is proposed to obtain the optimal position allocation scheme. The CBTP method is treated as a part of the HGSA algorithm to calculate the optimization index for a specific position allocation scheme. In the online case, a two-step minimal cost increase strategy (MCIS)-based method is developed and the UAVs in each new formation and the allocated position of UAV in the new formation are determined successively. Simulation results demonstrate that the CBTP method can generate the safe trajectories for the UAVs when forming new formations, and the optimal position allocation scheme can be obtained by the HGSA algorithm. The HGSA algorithm performs better than other similar algorithms especially in the complicated situation. The MCIS-based method can output the partially optimized position allocation scheme at short notice, but the real trajectories of UAVs are not considered in the online case to reduce the computation time. • The consensus-based trajectory planning (CBTP) method is proposed to calculate the trajectories of UAVs in the formation. • A hybrid genetic and simulate annealing (HGSA) algorithm is developed to allocate UAVs' positions in the new formations. • A two-step minimum cost increase strategy (MCIS) algorithm is designed to deal with the online position allocation issue. [ABSTRACT FROM AUTHOR]

Multi-objective optimization of detailed building models may lead to high computational time expenses. The work presented in this paper presents an alternative of classical approaches based on the usage of reduced sequences in a sequential and an adaptive approach. The sequential approach runs directly the reduced sequence obtained from a typical day selection algorithm which employs a k- medoids clustering algorithm in a multi-objective optimization study. It was applied on a solar combisystem connected to a building model and showed its capability of reproducing the Pareto front 25 times faster with relative errors not exceeding 5% when considering several parametric configurations of the model and focusing on a limited number of selection criteria during the day selection process. On the other hand, the adaptive approach for optimization named OptiTypSS achieves day selection during the optimization process by combining an iterative day selection algorithm (TypSS) with a genetic algorithm of optimization (NSGA-II). The method succeeded in obtaining results with errors inferior to 3% being therefore as efficient as a classical metamodel adaptive approach yet with relatively higher computational time. Further improvements including parallel simulation during the day selection process can be done to enhance the performance. • Optimization of a solar combi system model using short simulation sequences in a sequential and an adaptive approach. • In the sequential approach, the optimization algorithm converged 25 times faster with relative errors of a maximum of 5%. • In the adaptive approach, the Pareto front superposed the reference one recording relative errors inferior to 2.5%. • Results obtained by the adaptive approach were as efficient as using metamodels with relatively higher computational time. [ABSTRACT FROM AUTHOR]