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Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

This paper aims to present a hybrid method to solve nonlinear constrained optimization problems and engineering design problems (EDPs). The hybrid method is a combination of monarch butterfly optimization (MBO) with the cross-over and mutation operators of the genetic algorithm (GA). It is called a hybrid monarch butterfly optimization with genetic algorithm operators (MBO-GAO). Combining MBO and GA operators is meant to overcome the drawbacks of both algorithms while merging their advantages. The self-adaptive cross-over and the real-valued mutation are the GA operators that are used in MBO-GAO. These operators are merged in a distinctive way within MBO processes to improve the variety of solutions in the later stages of the search process, speed up the convergence process, keep the search from getting stuck in local optima, and achieve a balance between the tendencies of exploration and exploitation. In addition, the greedy approach is presented in both the migration operator and the butterfly adjusting operator, which can only accept offspring of the monarch butterfly groups who are fitter than their parents. Finally, popular test problems, including a set of 19 benchmark problems, are used to test the proposed hybrid algorithm, MBO-GAO. The findings obtained provide evidence supporting the higher performance of MBO-GAO compared with other search techniques. Additionally, the performance of the MBO-GAO is examined for several EDPs. The computational results show that the MBO-GAO method exhibits competitiveness and superiority over other optimization algorithms employed for the resolution of EDPs. [ABSTRACT FROM AUTHOR]

FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task requirements of FAGV, this paper proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. The improved A* algorithm can plan the global optimal path more suitable for FAGV. The improved evaluation function of DWA can ensure that the local path of FAGV is closer to the global path. DWA combines the rolling window method for local path planning to avoid sudden unknown static and dynamic obstacles. In addition, this paper verifies the effectiveness of the algorithm through simulation. The simulation results show that the algorithm can avoid obstacles dynamically without being far away from the global optimal path. [ABSTRACT FROM AUTHOR]

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship's trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends. [ABSTRACT FROM AUTHOR]

Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations -low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and genetic algorithms to achieve adaptive and optimal control in real-time scenarios. The neurofuzzy component provides the ability to model and handle complex and uncertain systems, while the genetic algorithm component facilitates the optimization of control parameters. The combination of these techniques enables autonomous systems to adapt and optimize their control strategies based on changing environments and objectives. The paper discusses the underlying principles of hybrid neuro-fuzzy-genetic algorithms, their advantages, and challenges. It also provides a review of the state-of-the-art research in this field, highlighting successful applications and potential future directions. Overall, the integration of neuro-fuzzy-genetic algorithms in autonomous systems holds great promise for achieving optimal control in various domains, including robotics, aerospace, and autonomous vehicles. [ABSTRACT FROM AUTHOR]

In this paper, we propose two new hybrid methods for solving nonlinear equations, utilizing the advantages of classical methods (bisection, trisection, and modified false position), i.e., bisection-modified false position (Bi-MFP) and trisection-modified false position (Tri-MFP). We implemented the proposed algorithms for several benchmark problems. We discuss the graphical analysis of these problems with respect to the number of iterations and the average CPU time. [ABSTRACT FROM AUTHOR]

The job shop scheduling problem (JSSP) is a very popular NP-hard optimization problem that involves assigning jobs to resources. Recent advances in the field of memetic algorithms show that explicitly managing the diversity of the population by taking into account the stopping criterion with the aim of dynamically adapting the balance between exploration and exploitation is key to their success. This is especially the case in long-term executions. However, this design principle has not yet been applied to the JSSP. This paper proposes a novel memetic algorithm that integrates some of the most advanced components devised in the literature for the JSSP with a replacement strategy that explicitly manages the diversity by considering a novel dissimilarity measure. To properly address large instances, a parallel master-worker model is used. Experimental validation shows the important advances attained by our proposal when compared to two state-of-the-art optimizers. The advantages are clear in both sequential and parallel cases, with more impressive achievements appearing in the parallel case. The parallel proposal has yielded new best-known solutions in 30 well-known JSSP instances, matching the lower bound in two of them, meaning that at least two new optimal solutions have been discovered. [ABSTRACT FROM AUTHOR]

In developing countries, the demand for old aged people requiring private health care at home is dramatically growing with the improvement of living standards. Since vehicles are used for transferring the medical staff (or doctors) to patient homes, it may be interesting to select a vehicle type based on the cost, capacity, and environmental sustainability (fuel consumption and CO2 gas emission per unit of distance) to maximize profits and social responsibility. In this paper, the first contribution, a new green home health care network for location, allocation, scheduling, and routing problems is developed with uncertain conditions. Another novelty, the time window to serve patients is also considered. In this regard, a novel grey flexible linear programming model is developed to cope with the uncertain nature of costs and capacity parameters that is as one important novelty. Due to this model's high complexity and difficulty in large-scale instances, this research develops two novel hybrid algorithms. The first hybrid strategy called the HSEOSA algorithm combines the Social Engineering Optimizer algorithm with the Simulated Annealing method. In terms of contribution to the related solution methodology, additionally, the Keshtel Algorithm is incorporated with the Genetic Algorithm called the HGAKA algorithm as the second new hybrid metaheuristic. An extensive comparison among the proposed algorithms is performed to find the most efficient one for the application of home healthcare in real practice. To validate the proposed model, a novel real case study is illustrated in the home healthcare services in Tehran/Iran. [ABSTRACT FROM AUTHOR]

Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle high-dimensional problems. This paper proposes a novel EPD operator to improve the search process. In other words, as the primary EPD mainly improves the fitness of the worst individuals in the population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainly improves the diversity of the best individuals, and hence we name it the Diversity-Based EPD (DB-EPD). The proposed method is applied to the Grey Wolf Optimizer (GWO) and named DB-GWO-EPD. In this algorithm, the three most diversified individuals are first identified at each iteration, and then half of the best-fitted individuals are forced to be eliminated and repositioned around these diversified agents with equal probability. This process can free the merged best individuals located in a closed populated region and transfer them to the diversified and, thus, less-densely populated regions in the search space. This approach is frequently employed to make the search agents explore the whole search space. The proposed DB-GWO-EPD is tested on 13 high-dimensional and shifted classical benchmark functions as well as 29 test problems included in the CEC2017 test suite, and four constrained engineering problems. The results obtained by the proposal upon implemented on the classical test problems are compared to GWO, FB-GWO-EPD, and four other popular and newly proposed optimization algorithms, including Aquila Optimizer (AO), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), and Gradient-based Optimizer (GBO). The experiments demonstrate the significant superiority of the proposed algorithm when applied to a majority of the test functions, recommending the application of the proposed EPD operator to any other meta-heuristic whenever decided to ameliorate their performance. [ABSTRACT FROM AUTHOR]

This work proposes a new metaheuristic technique that combines Differential evolution (DE) with gravity search in a consistent manner. Swarm intelligence benefits and the concept of tensile strength between two particles are combined to suggest superior meta-heuristic approaches for limitless optimization issues. The goal of this paper is to create a new algorithm that overcomes the shortcomings of the Gravitational search algorithm by leveraging the advantages of the Differential evolution algorithm in expanding search areas, overcoming early convergence problems, and improving the attractive algorithm’s ability to converge towards the optimum. The GSA algorithm has been utilized in a searchoriented algorithm, whereas the Differential evolution algorithm is causing a high level of diversification in society, which leads to the establishment of search regions for the GSA algorithm. The effectiveness of the suggested approach was evaluated by solving a collection of 30 Real-Parameter Numerical Optimization problems that were presented at IEEE-CEC 2014. The findings are compared to 5 state-of-the-art unconstrained problem algorithms and 6 state-ofthe-art unconstrained problem algorithms. The winner methods were also deduced from the results using the Wilcoxon signed test. [ABSTRACT FROM AUTHOR]

Parameters of the solar cell equivalent circuit models have a significant role in assessing the solar cells' performance and tracking operational variations. In this regard, estimating solar cell parameters is a difficult task because cells have nonlinear current-voltage characteristics. Thus, a fast and accurate optimization algorithm is usually required to solve this engineering problem effectively. This paper proposes two hybrid variants of honey badger algorithm (HBA) and artificial gorilla troops optimizer (GTO) to estimate solar cell parameters. The proposed algorithms minimize the root mean square error (RMSE) between measurement and simulation results. In the first variant, GTO is used to determine the initial population of HBA, while in the second variant, HBA is used to determine the initial population of GTO. These variants can efficiently improve convergence characteristics. The proposed optimization algorithms are applied for parameter estimation of different equivalent circuit models of solar cells and various photovoltaic (PV) modules. Namely, the proposed algorithms test three solar cell equivalent models: single-diode, double-diode, and triple-diode equivalent circuit models. Different photovoltaic modules are investigated, such as the RadioTechnique Compelec (RTC) France solar cell, Solarex's Multicrystalline 60 watts solar module (MSX 60), and the Photowatt, France solar panel (Photo-watt PWP 201). In addition, the applicability of the proposed optimization algorithms is verified using obtained results from a commercial solar module called Shell Monocrystalline PV module (SM55) with different irradiation and temperature levels. The good results of the proposed algorithms show that they can efficiently improve convergence speed and the accuracy of the obtained results than other algorithms used for parameter estimation of PV equivalent circuit models in the literature, particularly in terms of the values of the RMSE and statistical tests. In addition, the parameters estimated by the proposed methods fit the simulation data perfectly at different irradiance and temperature levels for the commercial PV module. [ABSTRACT FROM AUTHOR]

Balancing the exploration and exploitation in any nature-inspired optimization algorithm is an essential task, while solving the real-world global optimization problems. Therefore, the search agents of an algorithm always try to explore the unvisited domains of a search space in a balanced manner. The sine cosine algorithm (SCA) is a recent addition to the field of metaheuristics that finds the solution of an optimization problem using the behavior of sine and cosine functions. However, in some cases, the SCA skips the true solutions and trapped at sub-optimal solutions. These problems lead to the premature convergence, which is harmful in determining the global optima. Therefore, in order to alleviate the above-mentioned issues, the present study aims to establish a comparatively better synergy between exploration and exploitation in the SCA. In this direction, firstly, the exploration ability of the SCA is improved by integrating the social and cognitive component, and secondly, the balance between exploration and exploitation is maintained through the grey wolf optimizer (GWO). The proposed algorithm is named as SC-GWO. For the performance evaluation, a well-known set of benchmark problems and engineering test problems are taken. The dimension of benchmark test problems is varied from 30 to 100 to observe the robustness of the SC-GWO on scalability of problems. In the paper, the SC-GWO is also used to determine the optimal setting for overcurrent relays. The analysis of obtained numerical results and its comparison with other metaheuristic algorithms demonstrate the superior ability of the proposed SC-GWO. [ABSTRACT FROM AUTHOR]

Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis. [ABSTRACT FROM AUTHOR]

The Sine Cosine Algorithm (SCA) is a recently developed efficient metaheuristic algorithm to find the solution of global optimization problems. However, in some circumstances, this algorithm suffers the problem of low exploitation, skipping of true solutions and insufficient balance between exploration and exploitation. Therefore, the present paper aims to alleviate these issues from SCA by proposing an improved variant of SCA called HSCA. The HSCA modifies the search mechanism of classical SCA by including the leading guidance and hybridizing with simulated quenching algorithm. The proposed HSCA is tested on classical benchmark set, standard and complex benchmarks sets IEEE CEC 2014 and CEC 2017 and four engineering optimization problems. In addition to these problems, the HSCA is also used to train multilayer perceptrons as a real-life application. The experimental results and analysis on benchmark problems and real-life application problems demonstrate the superiority of the HSCA as compared to other comparative optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher. [ABSTRACT FROM AUTHOR]

Considered as one of the emerging evolutionary algorithms, the Dendritic Cell Algorithm (DCA) is based on the behavior of specific immune agents; known as Dendritic Cells (DCs). Studies related with DCA are increasingly becoming popular and this is due to the worthy characteristics expressed by the algorithm as it exhibits several potentially beneficial features for binary classification problems. Yet, according to our best knowledge, there is no study summarizing the basic features of the DCA developed versions all in one paper. Hence, in this paper we aim at summarizing the powerful characteristics of the DCA while making a general review of the algorithm. We aim at studying the various versions of DCA while highlighting their characteristics, advantages and limitations. Based on this study and from the conducted conclusions, we intend to generate a well-studied novel DC classifier based on the positive aspects reflected by the previously proposed DCA versions. Results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

The traditional A* algorithm has several problems in practical applications, such as many path turning points, redundant nodes, and long running time. it is sometimes impossible to plan the theoretical optimal route. To solve the above problem, this paper presents an optimized A* algorithm, the adaptive adjustment step algorithm and the three-time Bezier curve are used to solve the problems of many turning points, large turning angles, and long running time in the search path. Moreover, aiming at the path planning problem of mobile robots facing dynamic obstacle interference in complex environments, an algorithm that integrates the improved A* algorithm with the dynamic window method is proposed, which not only solves the shortcomings of the A* algorithm in which the dynamic obstacles cannot be avoided, but also prevents the mobile robot from falling into local optimization. The results show that the fusion algorithm of the improved A* algorithm and the dynamic window method with the traditional A* algorithm reduces the number of turns by 50% and the path length by 3.62% compared with the original algorithm. In the same environment, compared with the traditional algorithm, the hybrid algorithm in this paper reduces the average time consumption by 10.27%, the number of path inflection points by 57.14%, and the accuracy is higher than 33.33%, which is more effective in complex dynamic environments.

Metaheuristic algorithms have garnered much attention among researchers owing to their robustness, adaptability, independence from a predetermined initial solution, and lack of reliance on gradient computations. The flower pollination algorithm (FPA) and the slime mould algorithm (SMA) are efficient methodologies for addressing global optimization challenges. Nonetheless, tackling large-scale global problems using a single algorithm often proves challenging due to inherent limitations in its mechanism. One effective approach to mitigating this limitation is to hybrid the two algorithms employing suitable strategies. We proposed a hybrid algorithm (GFPSMA) based on FPA and SMA. First, to address the global exploration issue of FPA, a method was proposed that utilized the golden section mechanism to enhance information exchange between random individuals and the best individual. Second, to improve the reliability of the random search phase in SMA, an adaptive step-size strategy was introduced. Furthermore, a dual-competition mechanism, inspired by gaming concepts, was introduced to enhance the integration of the two algorithms. Finally, an elite learning method with adjustment conditions was employed to refine the localization of the best individual. To assess the performance advantage of GFPSMA, 39 benchmark functions were employed, comparing GFPSMA with FPA and SMA along with their six variants, six variants of other metaheuristic algorithms, three CEC competition algorithms, totaling 17 algorithms, and strategic algorithms for testing. Experimental results demonstrated the favorable performance advantage of GFPSMA. Additionally, the feasibility and practicality of GFPSMA were demonstrated in four engineering problems. [ABSTRACT FROM AUTHOR]

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship’s trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends.

Power quality disturbances have adverse impacts on the electric power supply as well as on the customer equipment. Therefore, the detection and classification of such problems is necessary. In this paper, a fast detection algorithm for power quality disturbances is presented. The proposed method is a hybrid of two algorithms, abc-0dq transformation and 90° phase shift algorithms. The proposed algorithm is fast and reliable in detecting most voltage disturbances in power systems such as voltage sags, voltage swells, voltage unbalance, interrupts, harmonics, etc. The three-phase utility voltages are sensed separately by each of the algorithms. These algorithms are combined to explore their individual strengths for a better performance. When a disturbance occurs, both algorithms work together to recognize this distortion. This control method can be used for critical loads protection in case of utility voltage distortion. Simulation and analysis results obtained in this study illustrate high performance of the strategy in different single-phase and three-phase voltage distortions. [ABSTRACT FROM AUTHOR]

This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model's predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction. [ABSTRACT FROM AUTHOR]

A facility with frontroom and backroom operations has the option of hiring specialized or cross-trained workers. Cross-trained workers can be switched between the two rooms depending on demand but are more expensive than specialized ones. Assuming stochastic customer arrival and service times, we seek a smallestcost combination of cross-trained and specialized workers, together with a policy for switching the cross-trained workers between the rooms, which satisfies constraints on the expected customer waiting time and expected number of workers in the back room. Aconstraint programming approach using logic-based Benders' decomposition is presented. Experimental results demonstrate the strong performance of this approach across a wide variety of problem parameters. This paper provides one of the first links between queueing optimization problems and constraint programming. [ABSTRACT FROM AUTHOR]

The frequency assignment problem involves the assignment of discrete frequencies to the transmitters of a radio network, such as a radio broadcasting network. Frequency separation is necessary to avoid interference by other transmitters to the signal received from the wanted transmitter at the reception region. Here, it is of major importance to minimize the interference while at the same time using the spectrum efficiently. In this paper we present two original distributed algorithms implemented on clusters of PCs used to solve the frequency assignment problem in the field of radio broadcasting. The first one is based on the island distributed [1] implementation of our hybrid genetic algorithm [2]. The second one uses a distributed cooperative Tabu Search. Experimental results show that our algorithms, applied to several instances given by TDF-C2R, lead to important time performance improvements. [ABSTRACT FROM AUTHOR]

In this paper, hybrid algorithms are developed for the multisource location-allocation problem in continuous space. Three hybrid algorithms are proposed to solve this problem that combine elements of several traditional metaheuristics (genetic algorithm and variable neighborhood search) and local searches to find near-optimal solutions. Many problems from the literature have been solved with these algorithms and the obtained results confirm the robustness of the proposed hybrid algorithms. Moreover, the results show that in comparison to the best methods in literature (GA and VNS), these algorithms provide some better solutions. [ABSTRACT FROM AUTHOR]

FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task requirements of FAGV, this paper proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. The improved A* algorithm can plan the global optimal path more suitable for FAGV. The improved evaluation function of DWA can ensure that the local path of FAGV is closer to the global path. DWA combines the rolling window method for local path planning to avoid sudden unknown static and dynamic obstacles. In addition, this paper verifies the effectiveness of the algorithm through simulation. The simulation results show that the algorithm can avoid obstacles dynamically without being far away from the global optimal path.

The paper deals with the first phase of Web Service Composition – the abstract planning problem, performed by the tool Plan ics . We discuss three algorithms based on multisets of service types being a concise representation of abstract plans. The first algorithm reduces the abstract planning problem to a satisfiability problem for an SMT-solver (SMT stands for the Satisfiability Modulo Theories), the second one follows a GA-based approach (GA stands for a genetic algorithm), while the third hybrid algorithm combines the two above. All the algorithms are applied to ontologies, which are efficiently reduced using a graph database approach. The paper presents theoretical aspects of the framework together with many examples, and the extensive experimental results of the four algorithms followed by their analysis and comparison with each other and other approaches. [ABSTRACT FROM AUTHOR]

Edge Computing (EC) has emerged as a pivotal paradigm, offering solutions to address the challenges posed by latency-sensitive applications and to enhance overall network performance. In EC environments, efficient task offloading is crucial for minimizing latency and energy consumption while maximizing resource utilization. In this paper, we propose a hybrid task offloading approach (HybridTO) integrating Grey Wolf Optimizer and Particle Swarm Optimization. Our approach aims to optimize energy consumption and fulfil latency constraints in EC environments by taking into account various factors such as capacity constraints, proximity constraints, and latency requirements. Leveraging the collaborative capabilities inherent in EC servers, HybridTO offers a comprehensive solution to the task offloading problem. Through extensive simulations, we evaluate the performance of HybridTO against baseline approaches, demonstrating its superiority regarding energy usage, offloading utility and response delay, especially under conditions of limited resources. These results underscore the effectiveness of HybridTO as a promising solution for energy-efficient task offloading in EC environments, offering valuable insights for further research and development in this field.

Inspired by the core idea of the lattice Boltzmann method (LBM), which is successfully used in complex and nonlinear processes, we developed a lattice Boltzmann method-like (LBM-like) algorithm to effectively solve the maximal covering location problem with continuous- and inhomogeneous-edge demand on the complex network. The LBM-like algorithm developed has three key components, including the basic map, transfer function and effect function. The basic map is responsible for reasonably mapping complex networks with multiple branches and circles. Transfer functions are used to describe the complex covering process of the facility on the network, by splitting the entire covering process into several single-step covering processes, while the effect function is responsible for recording and processing the coverage effect of each covering process, based upon the requirement of an objective function. This LBM-like algorithm has good applicability to a complex network, intuitiveness, relatively low computational complexity, and open developability. Furthermore, the idea of the greedy algorithm was coupled with the LBM-like algorithm, to form two types of hybrid algorithms for improving the computational efficiency for the location problem, with multiple facilities, on a large-scale network. Finally, we successfully applied the LBM-like algorithm to the location problem of an emergency rescue spot on a real railway network, to underline the practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The Job Shop Scheduling Problem (JSSP) consists of finding the best scheduling for a set of jobs that should be processed in a specific order using a set of machines. This problem belongs to the NP-hard class problems and has enormous industrial applicability. In the manufacturing area, decision-makers consider several criteria to elaborate their production schedules. These cases are studied in multi-objective optimization. However, few works are addressed from this multi-objective perspective. The literature shows that multi-objective evolutionary algorithms can solve these problems efficiently; nevertheless, multi-objective algorithms have slow convergence to the Pareto optimal front. This paper proposes three multi-objective Scatter Search hybrid algorithms that improve the convergence speed evolving on a reduced set of solutions. These algorithms are: Scatter Search/Local Search (SS/LS), Scatter Search/Chaotic Multi-Objective Threshold Accepting (SS/CMOTA), and Scatter Search/Chaotic Multi-Objective Simulated Annealing (SS/CMOSA). The proposed algorithms are compared with the state-of-the-art algorithms IMOEA/D, CMOSA, and CMOTA, using the MID, Spacing, HV, Spread, and IGD metrics; according to the experimental results, the proposed algorithms achieved the best performance. Notably, they obtained a 47% reduction in the convergence time to reach the optimal Pareto front. [ABSTRACT FROM AUTHOR]

In the process of selecting a machine learning algorithm to solve a problem, questions like the following commonly arise: (1) Are some algorithms basically the same, or are they fundamentally different? (2) How different? (3) How do we measure that difference? (4) If we want to combine algorithms, what algorithms and combinators should be tried? This research proposes COD (Classifier Output Difference) distance as a diversity metric. COD separates difference from accuracy, COD goes beyond accuracy to consider differences in output behavior as the basis for comparison. The paper extends earlier on COD by giving a basic comparison to other diversity metrics, and by giving an example of using COD data as a predictive model from which to select algorithms to include in an ensemble. COD may fill a niche in metalearning as a predictive aid to selecting algorithms for ensembles and hybrid systems by providing a simple, straightforward, computationally reasonable alternative to other approaches. [ABSTRACT FROM AUTHOR]

This paper deals with a complex optimization problem, more specifically the two-stage transportation problem with fixed costs. In our investigated transportation problem, we are modeling a distribution network in a two-stage supply chain. The considered two-stage supply chain includes manufacturers, distribution centers, and customers, and its principal feature is that in addition to the variable transportation costs, we have fixed costs for the opening of the distribution centers, as well as associated with the routes. In this paper, we describe a different approach for solving the problem, which is an effective hybrid genetic algorithm. Our proposed hybrid genetic algorithm is constructed to fit the challenges of the investigated supply chain network design problem, and it is achieved by incorporating a linear programming optimization problem within the framework of a genetic algorithm. Our achieved computational results are compared with the existing solution approaches on a set of 150 benchmark instances from the literature and on a set of 50 new randomly generated instances of larger sizes. The outputs proved that we have developed a very competitive approach as compared to the methods that one can find in the literature. [ABSTRACT FROM AUTHOR]

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator. [ABSTRACT FROM AUTHOR]

Abstract: Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence; and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community. [Copyright &y& Elsevier]

Abstract: In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithm’s performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology. [Copyright &y& Elsevier]

Abstract: This paper presents a hybrid evolutionary programming approach to solve the worst case tolerance design problem (WCTDP) in magnetic devices. The hybrid algorithm is formed by a basic evolutionary programming approach, mixed with a gradient-guided local search. Two different local searches procedures are tested in the paper, both specially designed to be effective in the WCTDP. Simulations on an example in the design of a magnetic circuit and comparison with several existing bio-inspired heuristics are carried out, and have shown the goodness of our algorithm. [Copyright &y& Elsevier]

Containers delivery management is a problem widely studied. Typically, it concerns the container movement on a truck from ships to factories or wholesalers and vice-versa. As there is an increasing interest in shipping goods by container, and that delivery points can be far from railways in various areas of interest, it is important to evaluate techniques for managing container transport that involves several days. The time horizon considered is a whole working week, rather than a single day as in classical drayage problems. Truck fleet management companies are typically interested in such optimization, as they plan how to match their truck to the incoming transportation order. This planning is a relevant both for strategical consideration and operational ones, as prices of transportation orders strictly depends on how they are fulfilled. It is worth noting that, from a mathematical point of view, this is an NP-Hard problem. In this paper, a Decision Support System for managing the tasks to be assigned to each truck of a fleet is presented, in order to optimize the number of transportation order fulfilled in a week. The proposed system implements a hybrid optimization algorithm capable of improving the performances typically presented in literature. The proposed heuristic implements an hybrid genetic algorithm that generate chains of consecutive orders that can be executed by a truck. Moreover, it uses an assignment algorithm based to evaluate the optimal solution on the selected order chains. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the application of a Hybrid Grouping Genetic Algorithm (HGGA) to solve the problem of deploying metropolitan wireless networks. In particular, the exploitation of the existing broadband infrastructure (e.g., ADSL networks) by “opening up” WiFi-enabled routers to third party users, is considered to produce a complex problem, henceforth call WiFi network Design Problem or WiFiDP. The application of a HGGA to this problem produces cost-effective network deployment plans, considering real life aspects such as budget (the total cost of deployment – i.e. the cost of opening all selected DSL routers for public use – should not exceed the allocated budget) and DSL router characteristics (coverage, DSL capacity at a specific location, unit price, etc.) The hybrid grouping genetic algorithm proposed incorporates a particular encoding to tackle the WiFiDP, in which the group part also includes the type of router to be installed. Also, a modification of this encoding to consider the working frequencies of routers is presented in this paper. Moreover, a repairing and local search procedures are added to the algorithm to obtain better performance and always find viable solutions. The performance and effectiveness of the proposed HGGA is evaluated using two randomly generated WiFiDP instances (considering 1000 and 2000 users), used to perform several experiments. The comparison of the proposed HGGA results against those of a greedy optimization algorithm (previously proposed to solve the WiFiDP) shows the better performance of this approach. Finally, the application of the HGGA to real datasets in the cities of Berlin (Germany) and Torrejón de Ardoz (Spain) is also reported in the experimental part. In real conditions, the HGGA keeps performing better than previous methods. [Copyright &y& Elsevier]

Many real-world visual tracking applications have a high dimensionality, i.e. the system state is defined by a large number of variables. This kind of problem can be modelled as a dynamic optimization problem, which involves dynamic variables whose values change in time. Most applied research on optimization methods have focused on static optimization problems but these static methods often lack explicit adaptive methodologies. Heuristics are specific methods for solving problems in the absence of an algorithm for formal proof. Metaheuristics are approximate optimization methods which have been applied to more general problems with significant success. However, particle filters are Monte Carlo algorithms which solve the sequential estimation problem by approximating the theoretical distributions in the state space by simulated random measures called particles. However, particle filters lack efficient search strategies. In this paper, we propose a general framework to hybridize heuristics/metaheuristics with particle filters properly. The aim of this framework is to devise effective hybrid visual tracking algorithms naturally, guided by the use of abstraction techniques. Resulting algorithms exploit the benefits of both complementary approaches. As a particular example, a memetic algorithm particle filter is derived from the proposed hybridization framework. Finally, we show the performance of the memetic algorithm particle filter when it is applied to a multiple object tracking problem. [ABSTRACT FROM AUTHOR]

In this paper, a fast and robust optimization scheme is presented and applied to the optimal design of planar photonic crystals. It is based on a parallel hybrid (stochastic-deterministic) algorithm coupled to a semi-analytic solution of "full Maxwell" equations by multiple scattering technique. The numerical results of the optimal design of different waveguide bends are presented. [ABSTRACT FROM AUTHOR]

The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research. [ABSTRACT FROM AUTHOR]

Over the last years, researchers have been paying particular attention to scheduling problems integrating production environments and distribution systems to adopt more realistic assumptions. This paper aims to present a new biased random key genetic algorithm with an iterated greedy local search procedure (BRKGA-IG) for open shop scheduling with routing by capacitated vehicles. We propose approximation and exact algorithms to obtain high-quality solutions in acceptable computational times. This paper presents a new integer linear programming model. The proposed integer model has not been addressed in the revised literature. The objective function adopted is makespan minimization, and we use the relative deviation as performance criteria. BRKGA-IG has a new decoding scheme for OSSP-VRP solutions, an intensive exploitation mechanism with an iterated greedy local search procedure, and a restart mechanism to reduce premature population convergence. With these new mechanisms, the extensive computational experience carried out shows that the proposed metaheuristic BRKGA-IG is promising in solving large-sized instances for the new proposed problem, outperforming all other tested methods. • We study an integrated open shop scheduling problem with the vehicle routing problem. • This problem is modeled and solved by a MILP and a metaheuristic approach. • The biased random key genetic algorithm and the iterated greedy algorithm are used. • The proposed metaheuristic gets competitive results. [ABSTRACT FROM AUTHOR]

The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as "path planning optimization." Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work's main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future. [ABSTRACT FROM AUTHOR]

To coordinate the impact of multiple uncertainties on fuzzy flexible job shop scheduling in discrete manufacturing processes, a multi-objective scheduling strategy of fuzzy flexible job shop under three uncertain factors is proposed. Firstly, the general scheduling framework is constructed by combining uncertainty analysis and digital twin technology. Then, the uncertainty of processing time and delivery date is described by fuzzy function, and the uncertainty of equipment maintenance cycle is described by interval number. The scheduling model is constructed with collaborative optimization objectives, including minimizing the fuzzy makespan, fuzzy production cost minimum, fuzzy carbon emission minimum, and customer satisfaction maximum. Then the hybrid PSO with a variable neighborhood search strategy is utilized to solve the problem. The initial population is generated by the process-first encoding method combined with several initialize strategies, the crossover and mutation of genetic algorithm are introduced to reconstruct the position and speed of particles based on a global search of PSO. Then the Pareto feasible solution set is obtained by neighborhood search of excellent particles through three kinds of neighborhood structures, and the satisfactory solution is selected by grey relational analysis. Finally, a production example of an enterprise is utilized to verify the effectiveness and feasibility of this method. [ABSTRACT FROM AUTHOR]