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In this article, we propose a hybrid iterative scheme with strong convergence property for solving variational inequality problems. The algorithm uses a self-adaptive stepsize defined using a simple updating rule. Therefore, the method does not require prior knowledge of the Lipschitz constant of the underlying operator. We consider a more general set of operators as the underlying operators. Moreover, we derived a fixed stepsize scheme from the proposed method. Under some suitable conditions, we show the strong convergence of the iterates generated by the proposed and the derived algorithms. Furthermore, we present numerical experiments to illustrate the computational performance of the proposed algorithm in comparison with some of the existing algorithms in the literature. Additionally, as an application, we use the proposed algorithm to solve the problem of recovering an original signal from a noisy signal [ABSTRACT FROM AUTHOR]

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In order to achieve a more efficient and accurate correction of near‐field error in the semiphysical radio frequency simulation system, the precise control parameters of the three antenna elements need to be obtained. This article is based on the method of moments electromagnetic simulation, and propose corresponding improvement ideas for the problems of limited optimization accuracy and low calculation efficiency in the near‐field error correction process. From the aspects of high‐precision intelligent inversion algorithm and high‐efficiency electromagnetic forward modeling, systematic optimization design and verification were carried out. The results prove that the control parameter filtering scheme based on PSO‐GA hybrid method has better optimization efficiency and accuracy than single genetic algorithm or differential evolution algorithm, which can provide more ideal initial amplitude and phase parameters for the subsequent selection of electromagnetic simulation and forward verification. In order to solve the problem of time‐consuming in the electromagnetic simulation, the multivariate vector forward model based on GA‐BP network and PSO‐SVM network are established, which can achieve high‐precision positioning of synthetic vector target points. The neural network method has been proved to be feasible on the basis of the current sample size. The paper selects hybrid algorithms to improve the shortcomings of single algorithm and uses algorithms to optimize neural networks, thereby obtaining better optimization results and reducing the time‐consuming of electromagnetic simulations, which can realize efficient correction of near‐field error. [ABSTRACT FROM AUTHOR]

Recently, the progression in Internet of Thing (IoT) has extensively influenced the model of wireless networking. As numerous wireless devices were emerged, the new Medium Access Control (MAC) and routing protocols has been introduced for assuring the end-to-end network efficiency. This paper aims to focus on the IEEE 802.15.4 MAC standards, where the security field is included in MAC header. The sensitive data security authentication on this MAC header is completely based on the method with the introduction of optimum authentication key using partially homomorphic encryption named ElGamal public key cryptosystem. More importantly, in this work, the ElGamal public key cryptosystem for securing IoT data is progressed by generating the optimal private key as well. For this optimal selection of key, this paper aims to establish a new hybrid optimization algorithm that hybrids the concept of Lion Algorithm (LA) and Cuckoo Search Algorithm (CS) named Cuckoo Mated-Lion Algorithm (CM-LA). Finally, the performance of the proposed working strategy is compared over other state-of-the-arts models by concerning various attacks, convergence and key sensitivity analysis, respectively.

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA.

Abstract Background The assembly task is an indispensable step in sequencing genomes of new organisms and studying structural genomic changes. In recent years, the dynamic development of next-generation sequencing (NGS) methods raises hopes for making whole-genome sequencing a fast and reliable tool used, for example, in medical diagnostics. However, this is hampered by the slowness and computational requirements of the current processing algorithms, which raises the need to develop more efficient algorithms. One possible approach, still little explored, is the use of quantum computing. Results We present a proof of concept of de novo assembly algorithm, using the Genomic Signal Processing approach, detecting overlaps between DNA reads by calculating the Pearson correlation coefficient and formulating the assembly problem as an optimization task (Traveling Salesman Problem). Computations performed on a classic computer were compared with the results achieved by a hybrid method combining CPU and QPU calculations. For this purpose quantum annealer by D-Wave was used. The experiments were performed with artificially generated data and DNA reads coming from a simulator, with actual organism genomes used as input sequences. To our knowledge, this work is one of the few where actual sequences of organisms were used to study the de novo assembly task on quantum annealer. Conclusions Proof of concept carried out by us showed that the use of quantum annealer (QA) for the de novo assembly task might be a promising alternative to the computations performed in the classical model. The current computing power of the available devices requires a hybrid approach (combining CPU and QPU computations). The next step may be developing a hybrid algorithm strictly dedicated to the de novo assembly task, using its specificity (e.g. the sparsity and bounded degree of the overlap-layout-consensus graph).

RNA structure prediction with pseudoknots is an NP-complete problem, in which an optimal RNA structure with minimum energy is to be computed. In past decades, several methods have been developed to predict RNA structure with pseudoknots. Among them, metaheuristic approaches have proven to be beneficial for predicting long RNA structure in a very short time. In this paper, we have used two metaheuristic algorithms; Genetic Algorithm (GA) and Simulated Annealing (SA) for predicting RNA secondary structure with pseudoknots. We have also applied a combination of these two algorithms as GA-SA where GA is used for a global search and SA is used for a local search, and conversely SA-GA, where SA is used for a global search and GA is used for a local search. Four different energy models have been applied to calculate the energy of RNA structure. Five datasets, constructed from the RNA STRAND and Pseudobase++ database, have been used in the algorithms. The performances of the algorithms have been compared with several existing metaheuristic algorithms. Here we have obtained that the combination of GA and SA (GA-SA) gives better results than GA, SA and SA-GA algorithms and all other four state-of-art algorithms on all datasets.

Recent years have witnessed the splendid performance of deep learning methods used in gas recognition for electronic noses (E-nose). In addition to effective feature extraction, the architecture of the deep neural network plays a vital role. Currently, most applied network structures are hand-crafted by human experts, which is time-consuming and problem-dependent, making it necessary to design the structures of neural networks according to specific demands. In this work, a genetic algorithm with particle swarm optimization (GA-PSO), which possesses promising optimization capabilities, is applied to search for effective deep convolutional neural networks (CNNs) for gas classification based on E-nose technology. A novel image transformation strategy using Gramian angular field and a hybrid cost-saving method is employed in the search process, enabling adaptive and efficient CNN search on gas datasets. With the proposed methods, we can achieve an average classification accuracy of over 90 % on two public gas datasets, while also significantly reducing the model size compared to state-of-the-art CNNs. By using these novel strategies, our approach surpasses random search and basic PSO algorithm in achieving the global optimal solution, higher and more stable accuracy, and faster convergence in pattern recognition using E-nose. Our work suggests that the proposed method can quickly identify excellent CNN structures for E-nose applications. • A new Gramian Angular Field method transforms 1D time series into 2D images. • Kernel decomposition and depthwise separable convolution optimize CNN training. • A fast and robust hybrid algorithm searches for gas-identification models. [ABSTRACT FROM AUTHOR]

In the defense industry, outsourcing has become crucial during production stages, primarily because certain tasks involve complex operations that may exceed the capabilities of in-house machinery. This is often influenced by factors such as technical competencies and high production costs. On the other side, recent shifts in climate patterns and growing environmental concerns have led to a significant impact on scheduling decisions, especially influenced by the carbon footprint of production factories. Therefore, it is important for manufacturing factories to evaluate both economic and environmental costs. A Mixed Integer Programming (MIP) model is formulated to solve the real-life production scheduling problem including machine costs, operation outsourcing and carbon footprint of the schedule with minimizing total costs. Since the mathematical model has difficulty in finding solutions as the problem size increases, a Hybrid Genetic Algorithm (HGA) which combines Genetic Algorithm (GA) with Iterated Local Search (ILS) procedure is proposed to solve especially medium-sized and large-sized problems. The efficiency of the proposed algorithm is demonstrated through randomly generated test problems. Obtained results indicate the superiority of the proposed HGA over GA, showcasing its ability to generate high-quality solutions and reduce overall costs in a reasonable solution times. [ABSTRACT FROM AUTHOR]

• Hub capacities are considered for the single-allocation Hub Maximal Covering problem. • A new Mixed-Integer Linear-Programming model is proposed and solved for the problem. • A Comprehensive fitness landscape analysis shows the problem space is rugged plain. • A new GARVND algorithm with original Crossover and Mutation operators is proposed. • Experiments showed that GARVND outdid GA, SA, VNS, TS, & GWO+SA methods in objective value. In this paper, a new variation to the Hub Maximal Covering problem called the Capacitated Single-Allocation Hub Maximal Covering Problem (CSAHMCP) is addressed for the first time and a novel binary-integer mathematical model has been proposed. To analyze the features of the problem's solution space and to determine the most effective heuristic approach to solve it, a comprehensive Fitness Landscape Analysis is performed which reveals that the problem is of 'rugged plain' type and can best be solved using an algorithm with good exploration and exploitation capabilities at the same time. Accordingly, we proposed a new algorithm named GARVND, which is a hybrid of the Genetic Algorithm acting as the main framework for diversification, and a variant of the Variable Neighborhood Descent algorithm called 'Reduced VND' acting as a means to improve individual solutions (intensification) by searching in the spaces generated by four types of neighborhood operators. We also introduced two original crossover and mutation operators that can be used for any hub location solution method. The parameters of GARVND are tuned toward less computational times and higher solution quality by using the Taguchi experiment design technique. We solved 114 CSAHMCP instances (several times each) from the Australian Post (AP) dataset using the proposed GARVND, as well as by the Genetic Algorithm, Variable Neighborhood Search, Simulated Annealing, Tabu Search algorithms, and a hybrid algorithm consisting of Grey Wolf Optimizer and Simulated Annealing, in addition to the Gurobi exact solver. Extensive computational results and statistical analyses showed that GARVND yields favorable solutions, outperforming all the compared algorithms by producing smaller gaps with global optimal objective values. In addition, to illustrate the practical value of the CSAHMCP and the GARVND method, we performed a cost-flow sensitivity analysis for the AP Central Sydney network and came up with the best expansion scenarios to be adopted by the management. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop an algorithm named Modified Artificial Bee Colony and Particle Swarm Optimization (MHABC-PSO) to address load frequency control (LFC) challenges in a two-area interconnected power system. The proposed MHABC-PSO algorithm is designed with two key modifications to enhance global exploration capability and improve convergence speed. Hence, a decision block is introduced in the employed bee (EB) phase incorporating a control parameter "limit" to allow each candidate solution (CS) to explore itself up to the "limit" value and boost local exploration. In addition, an introduction of a novel selection mechanism utilizing heuristic information (η) in EBs phase guides the onlooker bee (OB) phase to select better solutions based on success and failure history, thus promoting exploitation and reducing biased exploration. To address the efficacy of the proposed algorithm at the system level, three different two-area power systems are studied, incorporating various complexities, linearity, and non-linearity such as thermal-hydro, reheat thermal, and thermal-hydro-gas turbine configurations with HVDC link, SSSC, and CES. The algorithm is applied to optimize four objective functions (i.e. ITAE, IAE, ISAE, and ITE). The fitness function maximizes controller gains by utilizing the integral time multiplied absolute error (ITAE). Other objective functions like IAE, ISAE, and ITE are employed for a comprehensive analysis. Evaluation of MHABC-PSO effectiveness is conducted through ITAE values, peak deviations, and settling times of frequency and power deviations in different two-area systems. Results demonstrate that MHABC-PSO settles the system more quickly with zero steady-state error under step load perturbations (SLPs) of 1% and 2%. Comparative analysis with ABC, PSO, SFLA-TLBO, and OHABC-PSO using ITAE index and controller settling times shows the superiority of MHABC-PSO for LFC analysis. In conclusion, the proposed MHABC-PSO algorithm proves to be an efficient and effective solution for LFC, outperforming other algorithms in terms of the specified objective functions and exhibiting rapid convergence and optimal gains for controllers, effectively addressing LFC issues by combining exploration and exploitation techniques. • A hybrid approach using the modified ABC and PSO is developed to improve the LFC. • The algorithm was evaluated in three distinct interconnected power system networks. • The proposed algorithm outdid numerous other algorithms in performance metrics. [ABSTRACT FROM AUTHOR]

• The model performs well in image-based screening, collecting sequential dependencies over time and processing spatial information from medical imaging datasets. The TCT's capacity to identify long-range dependencies is essential for molecular interaction research since it helps to clarify the complex pathways associated with vascular disorders. Its adaptability also extends to personalized treatment approaches, which allow for the efficient modeling of patient-specific data, including genetic information. The TCT is a revolutionary tool that will continue to evolve as drug screening models do. By integrating temporal convolutional and transformer mechanisms, its all-encompassing methodology provides a sophisticated and comprehensive approach to deciphering the intricacies of vascular disorders. Across all evaluated metrics, the proposed method achieves near-perfect accuracy of 98.45 %, sensitivity of 93.45 %, specificity of 98.43 %, precision of 84.25, AUC of 99 % and F1 score values 96.01 %, respectively. This abstract sheds light on how the TCT has significantly changed drug development approaches, opening the door for more focused and effective therapies in the field of vascular health. Vascular illnesses include a spectrum of disorders affecting the blood vessels and continue to be major global health challenges. Vascular disorders are major worldwide health concerns that require ongoing development of drug screening models in order to find efficient treatment approaches. The ever-changing field of vascular health demands novel ways to medication screening due to the enduring difficulties presented by a variety of illnesses. This abstract explores a paradigm shift, highlighting the innovative role of the Temporal Convolutional Transformer (TCT) in redefining drug screening techniques and promoting model evolution. With its seamless integration of transformer mechanisms and temporal convolutional layers, the TCT is a revolutionary hybrid algorithm. Because of this special combination, the model can simultaneously identify spatial subtleties and capture temporal dynamics in complicated biological information. The temporal convolutional layers of the TCT are particularly good at revealing the sequential dependencies that are present in the development of vascular disorders. The model ensures a detailed knowledge of the developing nature of these diseases by analyzing temporal dynamics with skill. Simultaneously, the TCT's transformer mechanisms enable a thorough examination of long-range interdependence and global interconnections. The TCT's dual capability establishes it as a comprehensive solution by offering a nuanced perspective on spatial patterns that are essential for identifying the complexity of vascular health. The TCT is hybrid in the sense that it can learn features at several scales, supporting different temporal and spatial resolutions. This flexibility guarantees that the model may extract pertinent information at various scales, enhancing the characterization of the complex nature of vascular illnesses. The TCT is positioned as a holistic solution since its transformer mechanisms allow for a thorough investigation of long-range dependencies and global interconnections at the same time. This combined capacity offers a sophisticated viewpoint on spatial patterns that is essential for understanding the intricacies of vascular health. Moreover, the remarkable flexibility of the TCT to dynamic temporal scales is consistent with the dynamic character of biological processes, providing a strong and adaptable framework for drug screening. The TCT is used in many different fields of vascular disease research. The model performs well in image-based screening, collecting sequential dependencies over time and processing spatial information from medical imaging datasets. The TCT's capacity to identify long-range dependencies is essential for molecular interaction research since it helps to clarify the complex pathways associated with vascular disorders. Its adaptability also extends to personalized treatment approaches, which allow for the efficient modeling of patient-specific data, including genetic information. The TCT is a revolutionary tool that will continue to evolve as drug screening models do. By integrating temporal convolutional and transformer mechanisms, its all-encompassing methodology provides a sophisticated and comprehensive approach to deciphering the intricacies of vascular disorders. Across all evaluated metrics, the proposed method achieves near-perfect accuracy of 98.45%, sensitivity of 93.45%, specificity of 98.43%, precision of 84.25, AUC of 99 % and F1 score values 96.01%, respectively. This abstract sheds light on how the TCT has significantly changed drug development approaches, opening the door for more focused and effective therapies in the field of vascular health. [ABSTRACT FROM AUTHOR]

• Properties of multilayer laser cladding coatings are studied. • A novel hybrid algorithm optimizes process parameters with multiple objectives. • Trends in layer-to-layer properties of multilayer laser cladding are discovered. Laser cladding, a surface treatment technique for metals renowned for its combination of high material properties and superior corrosion resistance, is commonly utilized in laser repair processes. However, heat accumulation and rapid heating and cooling will lead to the formation of defects, such as cracks, pores, and thermal stress concentrations in the clad layer. Multi-objective optimization of the process parameters of 17-4PH/B 4 C coating through multilayer laser cladding was performed with a hybrid approach of RSM–NSGA-II–MOPSO to optimize the efficiency and performance of laser cladding. Numerical investigations arranged using the Box–Behnken design are performed to optimize the design variables, including laser power (A), scanning speed (B), powder feeding speed (C), and lap ratio (D), on the objective functions, including heat-affected zone depth (Z 1), clad layer thickness (Z 2), and microhardness (Z 3). The analysis of correlation and variance demonstrates that the response surface models are sufficiently accurate to predict the objective responses, and the order of influence of each factor on the depth of HAZ is A > B > D > C , that for clad layer thickness is B > D > C > A , and that for microhardness is C > D > A > B. With the regression models constructed by RSM, the NSGA-II–MOPSO is adopted to obtain the Pareto-optimal fronts. Technique for Order of Preference by Similarity to Ideal Solution was used to obtain the optimum solution from Pareto-optimal space. The optimum objective functions are as follows: a HAZ depth of 5.97 mm, a clad layer thickness of 1.20 mm, and a microhardness of 779.2 HV 0.2. Meanwhile, the corresponding design variables include a laser power of 1977 W, a scanning speed of 649 mm/min, a powder feeding speed of 0.4 r/min, and a lap rate of 40 %. The validation test showed good agreement between the optimized results and the actual measured values for clad layer HAZ depth, clad layer thickness, and microhardness. The tissue analysis revealed that the grains develop in alignment with the solidification trajectory of the laser cladding, and the cladding interface evolves from dendritic and coarse columnar crystals to fine, nondirectional equiaxed grains. This work offers significant references to laser cladding utilized in laser repair processes. [ABSTRACT FROM AUTHOR]

During recent years, quantum computers have received increasing attention, primarily due to their ability to significantly increase computational performance for specific problems. Computational performance could be improved for mathematical optimization by quantum annealers. This special type of quantum computer can solve quadratic unconstrained binary optimization problems. However, multi-energy systems optimization commonly involves integer and continuous decision variables. Due to their mixed-integer problem structure, quantum annealers cannot be directly used for multi-energy system optimization. To solve multi-energy system optimization problems, we present a hybrid Benders decomposition approach combining optimization on quantum and classical computers. In our approach, the quantum computer solves the master problem, which involves only the integer variables from the original energy system optimization problem. The subproblem includes the continuous variables and is solved by a classical computer. For better performance, we apply improvement techniques to the Benders decomposition. We test the approach on a case study to design a cost-optimal multi-energy system. While we provide a proof of concept that our Benders decomposition approach is applicable for the design of multi-energy systems, the computational time is still higher than for approaches using classical computers only. We therefore estimate the potential improvement of our approach to be expected for larger and fault-tolerant quantum computers. • Quantum computing for multi-energy system optimization. • Benders decomposition using quantum computers. • Hybrid optimization combining quantum and classical computer. • Master problem solved on quantum computer. • Quantum approach feasible but outperformed by classical computers. [ABSTRACT FROM AUTHOR]

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data.

Aerodynamic shape optimization is frequently complicated and challenging due to the involvement of multiple objectives, large-scale decision variables, and expensive cost function evaluation. This paper presents a bilayer parallel hybrid algorithm framework coupling multi-objective local search and global evolution mechanism to improve the optimization efficiency and convergence accuracy in high-dimensional design space. Specifically, an efficient multi-objective hybrid algorithm (MOHA) and a gradient-based surrogate-assisted multi-objective hybrid algorithm (GS-MOHA) are developed under this framework. In MOHA, a novel multi-objective gradient operator is proposed to accelerate the exploration of the Pareto front, and it introduces new individuals to enhance the diversity of the population. Afterward, MOHA achieves a trade-off between exploitation and exploration by selecting elite individuals in the local search space during the evolutionary process. Furthermore, a surrogate-assisted hybrid algorithm based on the gradient-enhanced Kriging with the partial least squares(GEKPLS) approach is established to improve the engineering applicability of MOHA. The optimization results of benchmark functions demonstrate that MOHA is less constrained by dimensionality and can solve multi-objective optimization problems (MOPs) with up to 1000 decision variables. Compared to existing MOEAs, MOHA demonstrates notable enhancements in optimization efficiency and convergence accuracy, specifically achieving a remarkable 5–10 times increase in efficiency. In addition, the optimization efficiency of GS-MOHA is approximately five times that of MOEA/D-EGO and twice that of K-RVEA in the 30-dimensional test functions. Finally, the multi-objective optimization results of the airfoil shape design validate the effectiveness of the proposed algorithms and their potential for engineering applications.

Abstract The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem (HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles (AGVs) is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.

For the optimization problem of the cold-chain emergency materials (CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization (MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis (IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei Province. Research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. Research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.

Selective Harmonic Elimination Pulse Width Modulation (SHEWM) can eliminate selected low order harmonics by solving nonlinear transcendental equations to obtain the switching angles in advance and achieve precise harmonics control. However, when solving nonlinear equations, the convergence rate and solution accuracy of the existing intelligent algorithms for multilevel inverter SHEPWM will decrease with the increase of the number of switching angles. In this paper, an improved hybrid PSO-TS algorithm (IPSO-TS) is proposed for switching angle selection in PWM inverter. In the proposed IPSO-TS algorithm, both the PSO algorithm and TS algorithm are improved respectively to enhance their convergence when calculating multiple switching angles. Also, these two improved algorithms are combined to obtain the hybrid algorithm IPSO-TS, in which PSO is used to provide global search capabilities, and TS is used to increases the accuracy of solutions. Simulations are carried out using MATLAB/SIMULINK environment to demonstrate the better convergence of the proposed algorithm compared with the other existing methods. Moreover, the simulation results are further verified by experimental results.

In this study, an effective intelligent system based on artificial neural networks (ANN) and a new version of the sine cosine algorithm (SCA) is developed to evaluate and predict the FOS of homogenous slopes under static and dynamic loading. In the first step, an effective hybrid optimization algorithm based on the adaptive sine cosine algorithm (ASCA) and pattern search (PS), namely ASCPS, is proposed and verified using a set of benchmark test functions. Then, the new algorithm, along with the Morgenstern and Price method is applied for seismic slope stability evaluation. To provide a neural network training dataset, a set of 189 slopes with different values of slope height, slope angle, friction angle of soil, soil cohesion, and horizontal acceleration coefficient have been analyzed and their corresponding FOS have been recorded. In the next step, the proposed ASCPS algorithm is implemented for training the ANN model using the collected database. The performance and prediction capacity of the developed model are evaluated using root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with the RMSE value of 0.023 and the R value of 0.984 is a reliable, simple, and valid computational model for estimating the FOS and evaluating the slope stability under static and earthquake loads. In addition, the developed ANN model is applied to a case study of slope stability from previous studies, and the results reveal that the proposed model may provide better optimal solutions and outperform existing methods.

Many new algorithms have been proposed to solve the mathematical equations formulated to describe the real-world problems. But there still does not exist one algorithm that could solve the problems all. And most of the proposed algorithms have defects in some aspects, they need to be improved in application. In order to find a more efficient optimization algorithm and inspired by the better performance of the Arithmetic Optimization algorithm (AOA) and Aquila Optimizer (AO), we proposed a hybridization algorithm of them and abbreviated AOAAO in this paper. Considering the better performance of the Harris Hawk optimization (HHO) algorithm, an energy parameter E was also introduced to balance the exploration and exploitation procedures of individuals in AOAAO swarms, and furthermore, piecewise linear map was introduced to decrease the randomness of the energy parameter. Pseudo code of the proposed AOAAO algorithm was presented, Simulation experiments were carried out on the benchmark functions and three classical engineering problems were also involved in optimization. Nine popular well demonstrated algorithms were included for comparison. Results confirmed the AOAAO would be more efficient in optimization with faster convergence rate, and higher convergence accuracy.

This paper presents a new hybrid metaheuristic algorithm, the hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm (hHHO-AOA), as we have named it. It is proposed for sizing optimization and design of autonomous microgrids. The proposed hybrid algorithm has been developed based on operating the Harris Hawks Optimizer (HHO) and the Arithmetic Optimization Algorithm (AOA) in a uniquely cooperative manner. The developed algorithm is expected to increase the solution accuracy by increasing the solution diversity during an optimization process. The performance is verified with the evaluation metrics and the well-known statistical tests. According to the Friedman ranking test, the new algorithm performs 77.9% better than HHO and 78.6% better than AOA. Similarly, the performance checked with the Wilcoxon signed-rank test has revealed a significant superiority in solution accuracy compared to HHO and AOA alone. Later, the hybrid algorithm is tested on a microgrid that consists of a photovoltaic (PV) system, a wind turbine (WT) system, a battery energy storage system (BESS), diesel generators (DGs), and a commercial type load. For the optimal capacity planning of these components, a problem in which the loss of power supply probability (LPSP) and the cost of energy (COE) are defined as the objective function is formulated. The optimization done by the proposed algorithm has produced the lowest LPSP and the COE along with the highest rate of renewable fraction (RF). In conclusion, it is demonstrated that the new hHHO-AOA has proved itself in designing reliable, economical, and eco-friendly autonomous microgrids in the best optimal way.

Graph coloring is a computationally difficult problem, and currently the best known classical algorithm for $k$-coloring of graphs on $n$ vertices has runtimes $\Omega (2^n)$ for $k\geq 5$. The list coloring problem asks the following more general question: given a list of available colors for each vertex in a graph, does it admit a proper coloring? We propose a hybrid classical-quantum algorithm based on Grover search 12 to quadratically speed up exhaustive search. Our algorithm loses in complexity to classical ones in specific restricted cases, but improves exhaustive search for cases, where the lists and graphs considered are arbitrary in nature.

Currently, the question of state, formation and development of the information source interaction system, the scientific interaction and users' requests in certain fields of activity remains relevant under the conditions of the development of the use of Internet services. Recommendation systems are one of the types of artificial intelligence technologies for predicting parameters and capabilities. Due to the rapid increase in data on the Internet, it is becoming more difficult to find something really useful. And the recommendations offered by the service itself may not always correspond to the user's preferences. The relevance of the topic is to develop a personal recommendation system for searching books, which will not only reduce time and amount of unnecessary information, but also meet the user's preferences based on the analysis of their assessments and be able to provide the necessary information at the right time. All this makes resources based on referral mechanisms attractive to the user. Such a system of recommendations will be of interest to producers and sellers of books, because it is an opportunity to provide personal recommendations to customers according to their preferences. The paper considers algorithms for providing recommender systems (collaborative and content filtering systems) and their disadvantages. Combinations of these algorithms using a hybrid algorithm are also described. It is proposed to use a method that combines several hybrids in one system and consists of two elements: switching and feature strengthening. This made it possible to avoid problems arising from the use of each of the algorithms separately. A literature web application was developed using Python using the Django and Bootstrap frameworks, as well as SQLite databases, and a system of recommendations was implemented to provide the most accurate suggestion. During the testing of the developed software, the work of the literature service was checked, which calculates personal recommendations for users using the method of hybrid filtering. The recommendation system was tested successfully and showed high efficiency.

Evolutionary Algorithms (EAs) has attracted significantly attention with respect to complexity scheduling problems, which is referred to evolutionary scheduling. However, EAs differ in the implementation details and the nature of the particular scheduling problem applied. In order to have an effective implementation of EAs for production scheduling, this paper focuses on making a survey of researches based on using hybrid EAs. Starting from scheduling description, we identify the classification and graph representation of scheduling problems. Then, we present the various representations, hybridisation techniques and machine-learning techniques to enhancing EAs. Finally, we also present successful applications in manufacturing. [ABSTRACT FROM AUTHOR]

As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces an improved mutation and crossover approach (to prevent untimely convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) is inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6- and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10- and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances. [ABSTRACT FROM AUTHOR]

Impact force is the most common form of load which acts on engineering structures and presents a great hidden risk to the healthy operation of machinery. Therefore, the identification or monitoring of impact forces is a significant issue in structural health monitoring. The conventional optimisation scheme based on inversion techniques requires a significant amount of time to identify random impact forces (impact force localisation and time history reconstruction) and is not suitable for engineering applications. Recently, a pattern recognition method combined with the similarity metric, PRMCSM, has been proposed, which exhibits rapidity in practical engineering applications. This study proposes a novel scheme for identifying unknown random impact forces which hybridises two existing methods and combines the advantages of both. The experimental results indicate that the localisation accuracy of the proposed algorithm (100%) is higher than that of PRMCSM (92%), and the calculation time of the hybrid algorithm (179 s) for 25 validation cases is approximately one nineteenth of the traditional optimisation strategy (3446 s). [ABSTRACT FROM AUTHOR]

One of the main challenges in power converters is to adopt a convenient controller that is designed with an appropriate approach. In terms of controllers, linear and nonlinear types are available. Nonlinear controllers may be good for achieving dynamic capabilities; however, designing them involves undesirable complexity. Thus, alternative linear counterparts are desirable to achieve optimum performance. Fractional-order proportional-integral derivative (FOPID) controller stands as a good choice for this purpose since it is a more capable version of one of widely adopted linear controller known as PID. Therefore, in this study, a FOPID controller was used to achieve optimum performance for a buck converter. To obtain the best performance, a novel hybridized metaheuristic algorithm, which combines both Lévy flight distribution and simulated annealing algorithms (LFDSA), was utilized. The developed algorithm involves a balanced structure in terms of explorative and exploitative phases, which was confirmed via performing related analysis on unimodal and multimodal benchmark functions. Non-parametric statistical test has also showed the better capability of the proposed algorithm. Due to its enhanced capability, the proposed algorithm helped achieving optimum values of FOPID parameters such that a better closed-loop output voltage control performance of the buck converter in terms of time and frequency domain responses as well as disturbance rejection have been achieved. The proposed LFDSA-based FOPID controller also tested against other capable and reported state-of-the-art algorithm and the results have also verified the superior capability of the LFDSA over other approaches. [ABSTRACT FROM AUTHOR]

The current study presents a new hybrid algorithm generated by combining advantageous features of Imperialist Competitive Algorithm (ICA) and Biogeography- Based Optimization (BBO) to establish an effective search technique. Although the ICA performs fairly well at the exploration phase, it is less effective at the exploitation stage. In addition, its convergence speed is problematic in some instances. Meanwhile, the migration operator of BBO method strongly emphasizes the local search to find the optimum solution more precisely. The combination of these two algorithms generates a robust hybrid algorithm that enjoys both exploratory and exploitative functionalities. The proposed hybrid algorithm is called Migration-Based Imperialist Competitive Algorithm (MBICA). To validate its performance, MBICA is used to optimize a variety of benchmark truss structures. Compared to some other methods, this algorithm converges to better or at least identical solutions by reducing the required number of structural analyses. Finally, the results from the standard BBO, ICA, and other recently developed metaheuristic optimization methods were compared with those obtained in this study. [ABSTRACT FROM AUTHOR]

An Informed RRT* (IRRT*) algorithm is one of the optimized versions of a Rapidly-exploring Random Trees (RRT) algorithm which finds near-optimal solutions faster than RRT and RRT* algorithms by restricting the search area to an ellipsoidal subset of the state space. However, IRRT* algorithm has the disadvantage of randomness of sampling and a non-real time process, which has a negative impact on the convergence rate and search efficiency in path planning applications. In this paper, we report a hybrid algorithm by combining the Artificial Potential Field Method (APF) with an IRRT* algorithm for mobile robot path planning. By introducing the virtual force field of APF into the search tree expansion stage of the IRRT* algorithm, the guidance of the algorithm increases, which greatly improves the convergence rate and search efficiency of the IRRT* algorithm. The proposed algorithm was validated in simulations and proven to be superior to some other RRT-based algorithms in search time and path length. It also was performed in a real robotic platform, which shows that the proposed algorithm can be well executed in real scenarios. [ABSTRACT FROM AUTHOR]