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In this paper, a supervisory interval type-2 fuzzy adaptive sliding mode control scheme is addressed for cable robots. In the proposed control scheme, intelligent methods are combined with conventional sliding mode control to achieve optimal adjustment of control parameters. This approach ensures accurate tracking performance, despite the structural constraints of cable robots. These constraints include the production of purely tensile forces and the effects due to inherent elasticity, which will make the design of the controller challenging. For this purpose, the internal force concept is utilized to ensure the tension style of cables. Additionally, considering a compensator part in the proposed controller and applying the singular perturbation theorem, the vibration effects of elastic cables are handled, and stability is proved through the second Lyapunov method. Therefore, the desired performance of the control system will be guaranteed for the movements that stimulate the vibration modes of cables. An interval type-2 fuzzy logic controller is proposed to adjust the control gain, effectively reducing the chattering level. Moreover, a supervisory interval type-2 fuzzy logic control system is introduced to regulate the gains within the sliding surface. The Grasshopper Optimization Algorithm is employed to select the optimal parameters for the membership functions of the fuzzy system. The results of the conducted simulations show that the desired tracking performance is provided, despite different uncertain parameters and the structural constraints of the considered cable robot. [ABSTRACT FROM AUTHOR]

Purpose: This study addresses resilient mixed supply chain network design (SCND) and aims to minimize the expected total cost of the supply chain (SC) considering disruptions. The capacity of facilities is considered uncertain. In order to get closer to real-world situations, competition between SCs is considered. Design/methodology/approach: A two-stage stochastic programming model is developed for designing the SC network. The location of facilities and selection of suppliers are considered first-stage decisions, and the determination of materials and products flows are second-stage decisions. Some resilience strategies are applied to mitigate the negative impacts of disruptions. Findings: The results indicate that considering resilience and applying the related strategies are vitally important, and resilience strategies can significantly improve the SC objective and maintain market share. Also, it is confirmed that unrealistic decisions will be made without considering the competition. Originality/value: This study contributes to the literature by proposing a novel mathematical model for the resilient mixed SCND problem. The other contribution is considering the chain-to-chain competition in collecting returned products and selling recycled products to other SCs in a mixed SC under disruptions. Also, a novel hybrid metaheuristic is developed to cope with the complexity of the model. [ABSTRACT FROM AUTHOR]

Bonobo optimizer (BO) is a recent metaheuristic algorithm inspired by the social behavior of bonobos. BO maintains a robust search strategy based on two distinct phases that involve interesting mechanisms, such as the fission–fusion method for selection and an efficient process for producing new candidate solutions. Despite its remarkable capacity, BO presents a critical flaw that corresponds to an inappropriate balance between exploration and exploitation. Under such conditions, suboptimal or even poor solutions can be obtained. This paper proposes a modified BO algorithm in which the trajectory of each search agent is modified dynamically through the adaptation of the main parameters of the algorithm. With this new mechanism, the algorithm allows for the exploration and exploitation of different regions of the solution space and determines the global optimum in a faster manner. The performance of the proposed algorithm is evaluated by considering two scenarios. One is the optimization of 23 benchmark functions and the second is the problem of power allocation in wireless networks. The results show that the proposed scheme enhances the performance of the basic BO method by increasing the robustness and providing a better solution quality. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to present a unique hybrid metaheuristic approach to solving the nonlinear analysis of hall currents and electric double layer (EDL) effects in multiphase wavy flow by merging the firefly algorithm (FA) and the water cycle algorithm (WCA). Design/methodology/approach: Nonlinear Hall currents and EDL effects in multiphase wavy flow are originally described by partial differential equations, which are then translated into an ordinary differential equation model. The hybrid FA-WCA technique is used to take on the optimization challenge and find the best possible design weights for artificial neural networks. The fitness function is efficiently optimized by this hybrid approach, allowing the optimal design weights to be determined. Findings: The proposed strategy is shown to be effective by taking into account multiple variables to arrive at a single answer. The numerical results obtained from the proposed method exhibit good agreement with the reference solution within finite intervals, showcasing the accuracy of the approach used in this study. Furthermore, a comparison is made between the presented results and the reference numerical solutions of the Hall Currents and electroosmotic effects in multiphase wavy flow problem. Originality/value: This comparative analysis includes various performance indices, providing a statistical assessment of the precision, efficiency and reliability of the proposed approach. Moreover, to the best of the authors' knowledge, this is a new work which has not been explored in existing literature and will add new directions to the field of fluid flows to predict most accurate results. [ABSTRACT FROM AUTHOR]

Purpose: Although, numerous optimization algorithms have been devoted to construct an electrical ladder network model (ELNM), they suffer from some frail points such as insufficient accuracy as well as the majority of them are unconstrained, which result in optimal solutions that violate certain security operational constraints. For this purpose, this paper aims to propose a flexible-constraint coyote optimization algorithm; the novelty lies in these points: penalty function is introduced in the objective function to discard any unfeasible solution, an advanced constraint handling technique and empirical relationship between the physical estimated parameters and their natural frequencies. Design/methodology/approach: Frequency response analysis (FRA) is very significant for transformer winding diagnosis. Interpreting results of a transformer winding FRA is quite challenging. This paper proposes a new methodology to synthesize a nearly unique ELNM physically and electrically coupled for power transformer winding, basing on K-means and metaheuristic algorithm. To this end, the K-means method is used to cluster the setting of control variables, including the self-mutual inductances/capacitances, and the resistances parameters. Afterward, metaheuristic algorithm is applied to determine the cluster centers with high precision and efficiency. Findings: FRA is performed on a power transformer winding model. Basing on the proposed methodology, the prior knowledge in selecting the initial guess and search space is avoided and the global solution is ensured. The performance of the abovementioned methodology is compared using evaluation expressions to verify its feasibility and accuracy. Originality/value: The proposed method could be generalized for diagnosis of faults in power transformer winding. [ABSTRACT FROM AUTHOR]

In the present work, four flat-plate solar collectors of similar dimensions have been simulated using the CFD approach. Model 1 has been considered as circular with a U-shaped zigzag channel, while model 2 is a square-shaped plate. In the 3rd model, the number of turns in the channel has been increased by keeping the geometry of the pipe constant, while in the 4th model, multiple straight channels have been incorporated instead of a single U-shaped zigzag channel. Three-dimensional simulations have been primarily done to understand the effect of channel geometry area and turnings on the collector's performance. A steady-state incompressible forced convective heat transfer analysis has been performed here. Water is used as a fluid in the channel to carry heat. The results of the numerical analysis led to the conclusion that model 3 is the best flat plate solar collector where turnings are maximum. With a maximum collector performance of 41.57, model 3 performs 36.7% better than model 1. Additionally, when water flows at a rate of 8 m/s inside the collector's channel, the rate of heat transfer in model 3 is 2.5% faster than in model 1. Moreover, a global optimization study has been performed using two swarm-based metaheuristic algorithms, Gravitational Search Algorithm (GSA) and Firefly Algorithm (FA), with the aim of determining good optimum values of the design variables that will maximize collector efficiency for model 3 at different flow velocities. For model 3, the percentage variation in collector efficiency between CFD and optimised findings using metaheuristic algorithms is found to be (a) 41.08% for v = 2 m/s; (b) 28.41% for v = 4 m/s; (c) 21.71% for v = 6 m/s; and (d) 13.09% for v = 8 m/s. Therefore, it is possible to anticipate that as flow velocity increases, there will be a steady decrease in the variation between computed and optimized results. [ABSTRACT FROM AUTHOR]

Purpose: The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA). Design/methodology/approach: The central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors. Findings: The obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine's parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests. Originality/value: After performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine's parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters. [ABSTRACT FROM AUTHOR]

Aiming at the problem that Harris hawks optimizer (HHO) has poor population diversity and is easy to fall into local optimum when solving complex optimization problems, a multi-leader Harris Hawks optimizer with adaptive mutation (MLHHO-AM) is proposed, in which multiple leaders are used to guide the Harris hawks to improve the population diversity. An adaptive mutation is introduced to enhance the ability of the algorithm to jump out of the local optimum. The key feature of the strategy is that Gaussian mutation occurs in some dimensions of the optimal solution, and the number of mutation dimensions is adaptively changed. To verify the numerical optimization performance of MLHHO-AM, the parameter sensitivity, the role of two improved mechanisms, and population diversity are analyzed based on 23 classical test functions. Then, MLHHO-AM is compared with 12 state-of-the-art variants and 11 basic metaheuristic algorithms on IEEE CEC2017 and IEEE CEC2022 benchmark suites. The above test results show that the proposed MLHHO-AM is an effective algorithm with better numerical optimization performance than most competitors. In order to verify ability of MLHHO-AM to handle the practical problem, the parameters of the Elman neural network (ENN) used to build the prediction model of silicon content in liquid iron of blast furnace are optimized by MLHHO-AM. The simulation results based on actual data show that the ENN prediction model based on MLHHO-AM has higher prediction accuracy with R 2 = 0. 7563 and R M S E = 0. 0598. Therefore, the proposed MLHHO-AM has excellent numerical optimization performance and can be used to predict the silicon content in liquid iron of blast furnace. [Display omitted] • A multi-leader Harris Hawks optimizer with adaptive mutation (MLHHO-AM) is proposed. • The performance of proposed MLHHO-AM is validated on several benchmark functions. • The MLHHO-AM is applied to solve the prediction of silicon content in liquid iron of blast furnace. [ABSTRACT FROM AUTHOR]

This paper proposes a novel swarm intelligence optimizer inspired by human behavior called the Special Forces Algorithm (SFA). SFA is inspired by the missions of modern Special Forces in real life. First of all, in the initial stage of algorithm iteration, the speed of light mechanism is proposed for simulating the UAV-assisted search of special forces, and the loss probability mechanism is proposed for simulating the loss of contact due to force majeure during the search of special forces. The algorithm can more fully explore the solution space and enhance the ability of an algorithm to jump out of a local optimum. Secondly, by simulating the combat tactics of special forces, the algorithm uses command parameters to control the three stages of large-scale search, transitional stage search and small-scale precise search, making the balance between the exploration stage and the exploitation stage more reasonable. Finally, SFA inspired by special forces operations are presented. To verify the effectiveness of the proposed algorithm, SFA is compared with 6 classical algorithms in 23 benchmark functions. In addition, the algorithm is applied to practical engineering problems. Experimental results show that SFA has shown great potential and competitive results. SFA can achieve good search performance and optimization accuracy based on a better balance of exploration and exploitation capabilities. • A new swarm intelligence optimizer called SFA is proposed. • An index Instruction which is used to change exploration and exploitation phases is created. • Three novel search mechanisms are designed. • Thresholds have been designed during the optimization operation. [ABSTRACT FROM AUTHOR]

The chef-based optimization algorithm (CBOA) is a human-based method inspired by the relationship between culinary students and chef instructors. The original CBOA does not have a process that separately controls population generation and updating. For this reason, in this study, a novel improved CBOA with diffusion process (DP) algorithm with the Gaussian walk (CBOADP) is proposed. In this way, it is aimed to transfer promising members to the next iteration. In the original CBOA algorithm, the selection of the chefs is performed randomly. The oppositional-based learning (OBL) approach aims to find the current best chef by updating the chef from the chef candidates selected from opposite regions. Because the selection of the chef plays an important role in the selection of students. To develop the method with optimal parameters, parameter analysis is performed as the DP's walk and maximum diffusion parameters. The new method's performance is evaluated through the CEC2019 and CEC2022 test suites and traditional and advanced methods. Results for real design problems are also reported. All results are supported by the Wilcoxon sign and Friedman rank statistical tests. As a result, in general, the best convergence has been achieved with CBOADP in about 75% of the benchmark test data, and statistically, it has first rank among other methods on average. In addition, CBOADP reached the best fitness value in five of the six compared engineering problems. Accordingly, it is seen that DP and OBL approaches contribute positively to the performance of CBOA and show a successful convergence trend among other compared methods. [ABSTRACT FROM AUTHOR]

The flow direction algorithm (FDA) is a new physics-based meta-heuristic optimization algorithm that is being used successfully in a variety of applications. However, FDA lacks theoretically rigorous convergency analysis and suffers from several drawbacks, such as premature convergence, lack of population diversity, and imbalance between exploitation and exploration. In this paper, the supermartingale convergence theorem is used to analyse the global convergence of FDA in a continuous search space. We first demonstrate that FDA's global convergence is determined by the accumulation of the minimum probability (P t ∗) that the flow swarm fall into the global optimal region in each iteration. Then, an improved flow direction algorithm, namely, guided flow direction algorithm (GFDA), is proposed to increase the minimum probability by making full use of the neighbourhood information. Comprehensive experimental studies were conducted to test and validate the proposed GFDA. Twenty competitive meta-heuristic optimization algorithms, twenty-three classical benchmark functions, ten recently single objective bound constrained numerical optimization problems(CEC2020), and four constrained engineering problems were used. Finally, the experimental results and statistical tests (Friedman test and Wilcoxon test) demonstrate the superiority of GFDA compared to other algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Currently, more renewable energy resources with advanced technology levels are incorporated in the electric power networks. Under this circumstance, the attainment of optimal economic dispatch is very much essential by the power system as the system requires more power generation cost and also has a great demand for electrical energy. Therefore, one of the primary difficulties in the power system is lowering the cost of power generation, which includes both economic and environmental costs. This study/paper aims to introduce a meta-heuristic algorithm, which offers an solution to the combined economic and emission dispatch (CEED). Design/methodology/approach: A novel algorithm termed Levy-based glowworm swarm optimization (LGSO) is proposed in this work, and it provides an excellent solution to the combined economic and emission dispatch (CEED) difficulties by specifying the generation of the optimal renewable energy systems (RES). Moreover, in hybrid renewable energy systems, the proposed scheme is extended by connecting the wind turbine because the thermal power plant could not control the aforementioned costs. In terms of economic cost, emission cost and transmission loss, the suggested CEED model outperforms other conventional schemes genetic algorithm, Grey wolf optimization, whale optimization algorithm (WOA), dragonfly algorithm (DA) and glowworm swarm optimization (GSO) and demonstrates its efficiency. Findings: According to the results, the suggested model for Iteration 20 was outperformed GSO, DA and WOA by 23.46%, 97.33% and 93.33%, respectively. For Iteration 40, the proposed LGSO was 60%, 99.73% and 97.06% better than GSO, DA and WOA methods, respectively. The proposed model for Iteration 60 was 71.50% better than GSO, 96.56% better than DA and 95.25% better than WOA. As a result, the proposed LGSO was shown to be superior to other existing techniques with respect to the least cost and loss. Originality/value: This research introduces the latest optimization algorithm known as LGSO to provide an excellent solution to the CEED difficulties by specifying the generation of the optimal RES. To the best of the authors' knowledge, this is the first work that utilizes LGSO-based optimization for providing an excellent solution to the CEED difficulties by specifying the generation of the optimal RES. [ABSTRACT FROM AUTHOR]

Purpose: Topology optimization is a state-of-the-art technique for the innovative design of electromagnetic devices. The ON/OFF method is a typical approach for this purpose. However, the drawbacks of long iteration time and poor ability to express curved surfaces make the industry not shown their due interest so far in the ON/OFF method. The purpose of this paper is to study a novel ON/OFF method for topology optimization, which can bring feasible optimized shapes that are more friendly for industrial realization in a shorter time. Design/methodology/approach: The proposed improved ON/OFF method uses structured triangular elements for finite element modeling because the triangular elements can more freely express shape features. Every four triangular elements are pieced together to form a square cell, each quadrilateral cell is associated with a binary value indicating the material state of the four triangular elements. The binary metaheuristic algorithms are used to optimize the material distribution. After the material filling for the elements based on the output of the metaheuristic algorithm, a two-step surface smoother will be performed as the postprocess to make the shapes more friendly for manufacturing. Findings: The comparative numerical results on a benchmark topology optimization problem show that the proposed method can bring feasible optimized shapes that are more friendly for industrial realization in a shorter time. In addition, the speed and robustness of convergence, especially in the case of multiobjective topology optimization problem, are significantly improved. Originality/value: A novel ON/OFF method for topology optimization is proposed. Compared with the traditional ON/OFF method, the proposed method is better in terms of searching efficiency and robustness. Moreover, the proposed method can provide feasible optimized shapes that are more friendly for industrial realization. [ABSTRACT FROM AUTHOR]

This research work deals with the queueing analysis and strategic behavior of primitive users (PUs) and ordinary users (OUs) in cognitive radio system (CRS). Whenever fusion center (FC) is occupied, the ordinary users must decide whether to join a retrial orbit and make re-attempts or depart from the system. The OUs residing in the retrial orbit try to access the spectrum through FC. The FC becomes inactive and considered on vacation in case if there are no users in the CRS. Returning back from the vacation, the FC renders service to the primitive users. The service of ordinary users (OUs) may be interrupted due to breakdown of FC. The probability generating function approach is used to derive the performance indices related to the queueing characterization. The joining strategic behavior of the primitive as well as ordinary users has been investigated by considering a linear reward cost function. The social welfare by formulating the cooperative strategy is studied. The hybrid soft computing technique viz. adaptive network fuzzy inference system (ANFIS) is employed for the numerical simulation and sensitivity analysis of various system metrics. The optimal service rates are determined by minimizing the total cost involved in various activities using the quasi-Newton method (QNM) and metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Although many intelligent optimization algorithms have been applied to the multimodal multi-objective optimization problems (MMOPs) which are complex and difficult, challenges of MMOP such as loss of PS in decision space and low efficiency have not been well solved. To better solve these problems, an altruistic population algorithm (APA) which is based on the altruism behavior in some animal populations, is proposed in this paper. The proposed APA has five major operations: parent selection, procreation variation, altruistic nurturing, crowd competition and archive updating. A few important features of the proposed APA are: (1) The nurturing cost according to a pair of parents' condition is introduced. It can accelerate the convergence speed while maintaining the diversity of the Pareto optimal solutions (PS). (2) The application of altruism allows the transfer of nurturing cost between descendant siblings to improve the efficiency and decrease the unnecessary variations. (3) A selection strategy called neighboring selection based on the distance in the objective space is proposed. It is an effective way to delete the redundant individuals in the objective space. The experimental results reveal that APA preforms better than other existing algorithms for solving various MMOPs. • Based on the altruism, a novel meta-heuristic algorithm is proposed. • The nurturing process can balance the convergence speed and diversity of PS. • The application of altruism can improve the efficiency of evolutionary cost. • The proposed selection strategy is an effective way to delete the redundant. [ABSTRACT FROM AUTHOR]

The gravitational search algorithm (GSA) is one of the most promising algorithm in the physics-based metaheuristics category. However, GSA suffers from premature convergence due to rapid reduction in diversity, whereas a chaotic gravitational search algorithm (CGSA) can degrade the convergence speed and exploitation power. To address these issues, the current study proposes an algorithm that enhances the exploration capability of GSA using a disruption strategy with chaotic dynamics. If no significant change is observed in diversity values during the initial stages of the search process, disruption is performed using a sigmoid function. Then, the search process executes gradual exploitation using a sigmoid function without chaotic dynamics. The proposed algorithm is tested with GSA, CGSA, and PSO (particle swarm optimization) on 28 benchmark functions. It is observed that the algorithm outperforms GSA and PSO in 19 cases and CGSA in 20 cases. Diversity analysis shows that the algorithm generates superior exploration versus exploitation percentage with improved mean diversity values. To determine its robustness, the algorithm is applied to four unconstrained engineering problems. The results suggest that the algorithm can solve practical engineering problems in a reasonable number of iterations. [ABSTRACT FROM AUTHOR]

In this study, an application of green preservation technology to obtain optimal pricing and replenishment policies is developed. The decreasing value in waste is considered along with retailer dynamic promotional and green preservation technology investments under a price-promotion model with ramp-type demand. To maximize total profits, the study aims to clean the environment while simultaneously optimizing selling price, replenishment schedule, order quantity, green preservation technology investment, and dynamic investment rate. Pontryagin's maximum principle is adopted to obtain the optimal dynamic investment rate for waste reduction. Additionally, simulated annealing, particle swarm optimization, and BAT algorithms are individually applied to obtain an optimal decision for waste reduction via green preservation technology. Results showed that the investment rate in promotion is significantly affected by price changes and that the investment in green preservation technology is affected by the nature of the product. Extensive computational experiments are performed to validate the proposed model. Under Ramp-type demand, price differentiation is more rewarding than the uniform pricing of the retailer. Results demonstrate the significant role of dynamic investment strategy. It has invested more based on the low-price sensitivity in the second period, and the optimal investment follows a reverse trend. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been widely used to solve different optimization problems. There have always been ongoing efforts to develop new and efficient algorithms. In this paper, the Orchard Algorithm (OA) is designed and introduced, inspired by fruit gardening. In this process, various actions such as irrigation, fertilization, trimming, and grafting lead to a fruit orchard where most trees grow and produce fruit adequately. In OA, both explorations of the search space and exploitation of the best solutions are achieved using personal and social behavior. By introducing various operators such as annual growth, screening, and grafting, the algorithm can efficiently search and explore the search space. The performance of the proposed OA algorithm was evaluated on CEC2005, IEEE CEC06 2019,test functions, and five real-world engineering problems compared with 13 widely used and competitive algorithms. Thirty benchmark functions were used to compare the capabilities of the OA algorithm with other research. The OA yields far better results in many aspects than the other algorithms. The results show the OA's superiority and this algorithm's capability in solving optimization problems. • We proposed new meta-heuristic algorithm for solving optimization problems. • The orchard algorithm is introduced, which is inspired by fruit gardening process. • In OA, using personal and social behavior, exploration and exploitation are achieved. • At 60 test functions, OA performed better than other algorithms. • The orchard algorithm performed very well in five engineering benchmark problems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most efficient evolutionary algorithms for solving numerical optimization problems; however, it still suffers from premature convergence and stagnation. To address these problems, we propose a population state evaluation (PSE)-based improvement framework that can be freely embedded into various existing DE variants and population-based metaheuristic algorithms. The PSE framework comprises two population state evaluation mechanisms: one for tracking the optimization state of the population during evolution and the other for evaluating the distribution state of individuals in the population to identify the specific problem (premature convergence or stagnation) encountered by the corresponding algorithm. In addition, we design two intervention operations (dispersion and aggregation) to address premature convergence and stagnation. To verify the effectiveness of the PSE framework, we conduct comparison experiments using nine algorithms (including two basic DE algorithms, six state-of-the-art DE variants, and one non-DE algorithm) to optimize four real-world problems and 59 test functions from the IEEE CEC 2014 and IEEE CEC 2017 testbeds. The experimental results show that the PSE framework can significantly improve the optimization performance of the existing algorithms. [ABSTRACT FROM AUTHOR]

Selection hyper-heuristics have emerged as high level general-purpose search methodologies that mix and control a set of low-level (meta) heuristics. Previous empirical studies over a range of single objective optimisation problems have shown that the number and type of low-level (meta) heuristics used are influential to the performance of selection hyper-heuristics. In addition, move acceptance strategies play an important role and can significantly affect the overall performance of a hyper-heuristic. In this paper, we introduce an adapted variant of an existing learning automata based multiobjective hyper-heuristic from the literature. We investigate the performance and generality level of the proposed method, and another learning automata based selection hyper-heuristic, operating over a search space of multiobjective evolutionary algorithms (MOEAs) across two well-known multiobjective optimisation benchmarks. The experimental results demonstrate that, regardless of the number and type of low-level metaheuristics available, the learning automata based hyper-heuristics outperform each constituent MOEA individually, and an online learning and random choice selection hyper-heuristic from the literature. This performance and generality is shown to be consistent across a number of different move acceptance strategies. [ABSTRACT FROM AUTHOR]

Feature selection (FS) in data mining and machine learning has attracted extensive attention. The purpose of FS in a classification task is to find the optimal subset of features from given candidate features. Recently, more and more meta-heuristic algorithms have been used to deal with the FS problems. However, meta-heuristic algorithms suffer from certain issues, such as large search space for solutions and huge time consumption. Moreover, most of existing meta-heuristic algorithms focus only on the selection of an optimal feature subset, and pay little attention to the optimal design of the classifier. In this article, we propose a joint multiobjective optimization method for both feature selection and classifier design, called JMO-FSCD. The proposed approach uses neural network as a classifier and introduces a non-iterative algorithm for training the classifier so as to ensure good performance and fast learning. A new coding scheme is also designed for optimizing FS and classifier simultaneously. For demonstrating the superiority of the proposed approach, its performance is compared with those of six state-of-the-art FS algorithms. Experimental results on thirty-five benchmark data sets reflect the superior performance of the proposed JMO-FSCD. [ABSTRACT FROM AUTHOR]

Purpose: Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network. Design/methodology/approach: The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model. Findings: The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation. Originality/value: There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered. Highlights: Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm). [ABSTRACT FROM AUTHOR]

Many real-world optimization problems, called computationally expensive optimization problems, often require a time-consuming fitness evaluation through computer simulation or neural network training. A persistent challenge is to obtain reasonable solutions to these problems quickly. In this study, we design a p a r all e l t ransgenera t ional learning-assisted evolutionar y algorithm, called PRETTY, to accelerate the search process using three novel mechanisms. First, we fuse the transgenerational learning mechanism with metaheuristic algorithms to reduce unnecessary fitness evaluations. Then, a Hypervolume-Cauchy (HV-Cauchy) algorithm is proposed to balance population convergence and diversity adaptively. Finally, a parallel-computing framework is proposed to fuse the transgenerational learning mechanism and the HV-Cauchy algorithm into a multipopulation evolutionary algorithm. To verify the performance of the proposed algorithm, empirical experiments are conducted on nine benchmarks and four real-world problems, including feature selection, critical node detection, flocking, and neural network training. In terms of execution time, the experimental results demonstrate that PRETTY has significant advantages over several expensive multi-objective algorithms. Furthermore, PRETTY can maintain population diversity and fast convergence while accelerating the computation. In particular, in tackling real-world problems that require expensive fitness calculations, PRETTY can reduce the execution time by more than five times while ensuring high-quality solutions. [ABSTRACT FROM AUTHOR]

Template matching (TM) is a technique used in computer vision that helps us to identify small pieces of a predefined sub image known as template and a corresponding area inside a large image. There are several approaches in the literature that solve this problem. In spite of their acceptable results, the existing methods fail to detect templates when they are rotated in a large image. On the other hand, the structural similarity (SSIM) index is a resemblance model that allows us to evaluate the affinity between two images. Different from other similarity indexes, SSIM considers important structural information while also incorporating essential elements such as luminance and contrast terms. This paper presents a new approach for template matching when an image is rotated. Our method considers the complete process as an optimization problem where a metaheuristic algorithm is used to find the best resemblance between the template and its rotated or unrotated image. In order to obtain a robust detection, our approach considers the use of the SSIM index as an objective function. Although metaheuristic approaches produce interesting results, there is not an ideal algorithm that can competitively solve all problems. Under such conditions, our study analyzes the performance of five different metaheuristic techniques for solving the problem of TM. In the comparison, several complicated computer experiments have been conducted considering different performance indexes. The results show that the proposed technique solves the problem of TM competitively for rotated instances in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Aquila Optimization Algorithm (AQUILA) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard AQUILA to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base AQUILA to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base Aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with AQUILA has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems. • A manipulation scheme composed of a wavelet mutation ensemble is proposed. • The proposed scheme (DAQUILA) is implemented into the standard Aquıla algorithm. • Algorithm is tested on constrained an unconstrained test functions. • Superiority of DAQUILA is validated by the statistical results of the benchmark problems. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate a reliability-level demand-oriented pharmaceutical supply chain design with maximal anticipated demand coverage. Different hospitals with the particular reliability value associated with the various pharmaceutical items (PIs) are considered. An inter-connected multi-period supply chain comprising manufacturers, distribution centers, hospitals and patients is assumed for the smooth flow of health-care items, enhancing supply chain reliability. A reliability index for PIs is depicted to highlight product preference and facilitate hospitals' service levels for patients. Design/methodology/approach: A mixed-integer multi-objective programming problem that maximizes maximal demand coverage minimizes the total economic costs and pharmaceutical delivery time is depicted under intuitionistic fuzzy uncertainty. Further, a novel interactive neutrosophic programming approach is developed to solve the proposed pharmaceutical supply chain management (PSCM) model. Each objective's marginal evaluation is elicited by various sorts of membership functions such as linear, exponential and hyperbolic types of membership functions and depicted the truth, indeterminacy and falsity membership degrees under a neutrosophic environment. Findings: The proposed PSCM model is implemented on a real case study and solved using an interactive neutrosophic programming approach that reveals the proposed methods' validity and applicability. An ample opportunity to generate the compromise solution is suggested by tuning various parameters. The outcomes are evaluated with practical managerial implications based on the significant findings. Finally, conclusions and future research scope are addressed based on the proposed work. Research limitations/implications: The propounded study has some limitations that can be addressed in future research. The discussed PSCM model can be merged with and extended by considering environmental factors such as the health-care waste management system, which is not included in this study. Uncertainty among parameters due to randomness can be incorporated and can be tackled with historical data. Besides, proposed interactive neutrosophic programming approach (INPA), various metaheuristic approaches may be applied to solve the proposed PSCM model as a future research scope. Practical implications: The strategy advised is to provide an opportunity to create supply chains and manufacturing within India by helping existing manufacturers to expand, identifying new manufacturers, hand-holding and facilitating, teams of officers, engineers and scientists deployed and import only if necessary to meet timelines. Thus, any pharmaceutical company or organization can adopt the production and distribution management initiatives amongst hospitals to strengthen and enable the pharmaceutical company while fighting fatal diseases during emergencies. Finally, managers or policy-makers can take advantage of the current study and extract fruitful pieces of information and knowledge regarding the optimal production and distribution strategies while making decisions. Originality/value: This research work manifests the demand-oriented extension of the integrated PSCM design with maximum expected coverage, where different hospitals with pre-determined reliability values for various PIs are taken into consideration. The practical managerial implications are explored that immensely support the managers or practitioners to adopt the production and distribution policies for the PIs to ensure the sustainability in supply chain design. [ABSTRACT FROM AUTHOR]

Grey Wolf Optimizer (GWO) is a swarm intelligent optimization algorithm that simulates the leadership and social behavior of grey wolves to prey. GWO is extensive used in various fields since it has the advantage of being simple and easy to implement. However, when solving complex optimization problems, GWO has insufficient population diversity and unbalanced exploration and exploitation. To overcome these shortcomings of GWO, a multi-swarm improved grey wolf optimizer (MIGWO) is proposed. In MIGWO, firstly, chaotic grouping mechanism is utilized to improve population diversity and dynamic regrouping mechanism to further improve population diversity and balance exploration and exploitation. Secondly, double adaptive weights and dimension learning are utilized to ameliorate the hunting behavior of grey wolves, which can improve search performance. The MIGWO is verified on 53 test problems. The test results show that it is better to other metaheuristic algorithms and GWO variants in solving the optimal solution and convergence accuracy. In addition, MIGWO is utilized to solve 4 engineering design problems. [ABSTRACT FROM AUTHOR]

Lévy flight distribution is a recent meta-heuristic inspired by lévy flight random walk for exploring unknown large search spaces. Similar to other original metaheuristic algorithms, Lévy flight distribution can suffer from drawbacks, such as being trapped in minimum local areas and imbalance between the exploitation and exploration. To overcome these weaknesses and enhance the ability of Lévy flight distribution in solving high-dimensional numerical optimization problems, a modified Lévy flight distribution, called MLFD, is presented. Firstly, Lévy flight distribution has good exploration ability; secondly, the symbiosis organisms search has a strong exploitation capability in the mutualism phase. By introducing the mutualism phase, the exploitation ability of the algorithm is improved effectively and help avoid premature convergence. Moreover, a new differential variation strategy is proposed to enhance the diversity of the population and make the algorithm jump out of the local optimum in time. Seventeen well-known high-dimensional unconstrained problems are utilized to compare the proposed algorithm with other nine classical algorithms. The experimental results and statistical analysis demonstrate that MLFD algorithm has promising effectiveness and performance compared with other nine classical algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the optimal robust control of chaotic systems subject to parameter perturbations and measurement noise. Three novel dynamic tracking controllers are designed herein to suppress the chaotic behaviour in Lorenz systems under practical constraints. The dynamic controllers allow for extra improvements towards optimal and robust tracking but their design is challenging in the chaotic system. Given a single measured state only, the single-state feedback controllers are optimally tuned using high-performance heuristic Particle Swarm Optimization (PSO) algorithm with a constrained multi-objective function focusing mainly on the Integral Absolute Error (IAE). The controlled system stability is ensured through Routh–Hurwitz stability criteria where control parameters are further tuned using a metaheuristic PSO algorithm to ensure faster transients with minimum errors when tracking desired state trajectories and also to ensure robustness to parameter variations and external disturbances in the chaotic system. Different designs are tested through extensive simulations and comparisons where the proposed PSO-tuned PD-tracking controller is proved superior for not only suppressing the chaotic behaviour and reducing the transients but also for their high robustness to parameter uncertainties and external disturbances. • Novel optimal robust controllers are introduced to suppress the chaotic behaviour in Lorenz systems. • The dynamic controllers ensure system stability and fast convergence under practical conditions. • The single-state feedback controllers are optimally tuned using particle swarm optimization. • High robustness to parameter variations and external disturbances in the chaotic system is attained. • The designs are validated through three scenarios and they have shown superior performance. [ABSTRACT FROM AUTHOR]

Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheuristic performance. There is growing interest in cross-domain search methods, which consider a range of optimisation problems rather than being specialised for a single domain. Metaheuristics and hyper-heuristics are typically used as high-level cross-domain search methods, utilising problem-specific low-level heuristics for each problem domain to modify a solution. Such methods have a number of parameters to control their behaviour, whose initial settings can influence their search behaviour significantly. Previous methods in the literature either fix these parameters based on previous experience, or set them specifically for particular problem instances. There is a lack of extensive research investigating the tuning of these parameters systematically. In this paper, F-Race is deployed as an automated cross-domain parameter tuning approach. The parameters of a steady-state memetic algorithm and the low-level heuristics used by this algorithm are tuned across nine single-objective problem domains, using different training strategies and budgets to investigate whether F-Race is capable of effectively tuning parameters for cross-domain search. The empirical results show that the proposed methods manage to find good parameter settings, outperforming many methods from the literature, with different configurations identified as the best depending upon the training approach used. [ABSTRACT FROM AUTHOR]

Archimedes optimization algorithm (AOA) is a recent metaheuristic algorithm that offers several advantages, including a few parameters, an easy-to-understand interface, and easy implementation. Still, some drawbacks exist, e.g., lack of diversity for search-exploring capacity, drop-trap local optimum. This study suggests a new variant of AOA based on the parallel and Taguchi method (TPAOA) for the global optimization problems and the wind turbine parameter adjust-tuning variable pitch controller problem. The parallel mechanism with communication strategy and the Taguchi orthogonal combination deal with the AOA's drawbacks. The experimental results show that the proposed algorithm is more competitive than the other algorithms under the CEC2017 test suite. The wind turbine problem of parameter tuning difficulty of variable pitch controller is solved by applying the TPAOA. Compared solution results show that the TPAOA proves the feasibility smooth the output power of wind turbines and reducing the impact of wind speed fluctuations on the power grid, which has high feasibility. [ABSTRACT FROM AUTHOR]

Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA's U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases. [ABSTRACT FROM AUTHOR]

A recently developed metaheuristic optimization algorithm, Salp Swarm Algorithm (SSA), has manifested its capability in solving various optimization problems and many real-life applications. SSA is based on salps' swarming behaviour when finding their way and searching for food in the oceans. Nonetheless, like most metaheuristic algorithms, SSA experiences low convergence and stagnation in local optima and rate. There is a need to enhance SSA to speed its convergence and effectiveness to solve complex problems. In the present study, we will introduce chaos into SSA (CSSA) to increase its global search mobility for robust global optimization. Detailed studies are carried out on real-world nonlinear benchmark systems and CEC 2013 benchmark functions with chaotic map (Tent). Here, the algorithm utilizes a Tent map to tune the salp leaders' attractive movement around food sources. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of CSSA for 12 nonlinear systems and 28 unconstrained optimization problems CEC 2013. Two nonparametric statistical tests, the Friedman test and Wilcoxon Signed-Rank Test, are conducted to show the superiority of CSCA over other states of the art algorithms and our results' significance. • This paper introduces chaos into Salp Swarm Algorithm. • Solve 28 unconstrained optimization problems, CEC and 12 nonlinear systems. • The effectiveness and efficiency of our algorithm are provided. • Experimental results prove superiority of our algorithm over the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this sudy is to provide a complete optimization-based methodology to design waveguides with metamaterial walls. The present methodology is based on optimization. Indeed, the inverse problems of design are formulated as nonlinear black-box optimization problems with constraints. Two inequality black-box constraints are taken into account as penalized terms that are added to the objective function when the constraints are not satisfied. The numerical steps are done by using a finite element method solver (GetDP). Thus, different optimization software are tested to solve the nonlinear black-box optimization problems such as IPOPT, NLOPT and NOMAD from the Opti ToolBox in MatLab. Design/methodology/approach: In this work, a methodology to design waveguides with metamaterial walls is proposed. The aim is to solve an inverse problem to find the best design where the electric field cartography is the closest to an imposed one. Findings: The present methodology is applied to solve inverse problems of design and satisfactory results were provided by the three solvers IPOPT, NLOPT and NOMAD. Those numerical experiments show that NOMAD is the most efficient method to optimize the design of those cylindrical waveguide structures with metamaterial walls. Research limitations/implications: The model is set to find solutions using a specific pattern of metamaterials. This is promising to take those geometries as variables of the optimization problems. Moreover, in this exploratory work, no constraint on the fabrication limits has been taken into account. Originality/value: The originality is to formulate design problems of waveguide with metamaterial walls into optimization problems. These optimization problems are difficult to solve because the objective function and two inequality constraints are computed via a numerical simulation code based on finite element methods. Thus, an original approach based on penalization is implemented and three optimization software are used. Hence, the authors propose an optimization-based methodology and apply to solve two inverse problems of design. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithms, which are developed for solving complex optimization problems designed by focusing on simulating the social behavior of one species of simple animals. However, simple animals utilize cooperation to work together that result in more complex and smarter behaviors. This paper proposes a novel population-based optimization paradigm for solving NP-hard problems called "Aphid–Ant Mutualism (AAM)" which is inspired by a unique relationship between aphids and ants' species. This relationship is called 'mutualism'. Despite the previous studies that the social behaviors of aphids and ants were simulated, AAM models mutual interaction among aphids and ants in nature. Thus, AAM has new features by incorporating heterogeneous individuals consisting of aphids and ants that live in various colonies together and have different decentralized learning behaviors and objectives. Inspired by nature, colony-based information exchange and using different search strategies including focusing on the individual's personal knowledge, learning from other colony's members and information sharing with adjacent colonies are used. This mutualism leads to converging to the global optimum and avoids premature convergence. Performance of AAM is assessed using statistical evaluation, convergence analysis, and a non-parametric Wilcoxon rank-sum test with a 5% significance degree on forty-one benchmarks selected from well-known functions of recent studies and more challenging benchmark functions called CEC 2014, CEC 2017 and also CEC-C06 2019 test suite. Statistical results and comparisons with other meta-heuristic algorithms demonstrate that the AAM algorithm provides promising and competitive outcomes. Furthermore, it can produce more accurate solutions with a faster convergence rate to the global optima. • A novel population-based metaheuristic algorithm is developed which is called AAM. • AAM is inspired by interaction between aphids' and ants' species called "Mutualism". • AAM has new features by including heterogeneous individuals that live in the colonies. • AAM has few free parameters that make it versatile for diverse optimization problems. • Numerical simulations show that AAM outperforms six other comparative algorithms. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are effective in solving complex optimization problems with advantages of flexibility for coding, robustness and global optimization capability. An enhanced Black Widow Optimization called QIWBWO algorithm with three improvement strategies is proposed in this paper. At the beginning of search, the theory of good points set is used to obtain the better initial population, which helps the algorithm to quickly determine the correct search direction. Then, quadratic interpolation strategy is used to improve the solution accuracy and accelerate the convergence. Meanwhile, to avoid the algorithm falling into a local optimum, wavelet mutation is introduced to improve population diversity and helps the algorithm to search the global optimum rather than local optimums. The proposed BWO algorithm is compared with other different kinds of meta-heuristic algorithms on 25 traditional benchmark functions and CEC2017 competition suite. The statistical results show the improved BWO algorithm delivers better performance in accuracy, stability and convergence rate. Finally, QIWBWO also obtains the best results on four classical optimization problems in engineering application, which verifies its practicality and effectiveness. The source code of QIWBWO is publicly available in the supplementary material related to this article. • An enhanced Black Widow Optimization (BWO) algorithm called QIWBWO is proposed. • Three efficient strategies are first introduced into BWO algorithm. • The extensive results on 55 benchmark functions reveal the excellent performance. • QIWBWO has been applied to engineering problems and get good results. • QIWBWO can provide effective assistance in solving global optimization problems. [ABSTRACT FROM AUTHOR]

A study conducted by researchers at VIT-AP University in India has developed an effective IoT and deep learning-based system for predicting and classifying chronic diseases. The research proposes a technique that collects patient data using IoT devices, eliminates missing values, performs data preprocessing, selects necessary attributes, and utilizes a three-layer stacked bidirectional long short-term memory technique for disease classification. The proposed method achieved high accuracy rates of 99.87% for chronic kidney disease and 99.84% for cardiovascular disease datasets. This research provides valuable insights into the early detection and prevention of chronic diseases. [Extracted from the article]

A study conducted by researchers at Wenzhou University in China has developed a new algorithm to improve the accuracy of medical image segmentation for diagnosing and treating Lupus Nephritis (LN), a prevalent form of systemic lupus erythematosus. The algorithm, called GTMWOA, combines non-local means, 2D histogram, and 2D Renyi's entropy to enhance the performance of existing image segmentation methods. The researchers conducted extensive tests to demonstrate the algorithm's superior capabilities in handling complex optimization problems. The study concludes that the algorithm shows promise in improving the accuracy of diagnosing LN through medical imaging. [Extracted from the article]

The metaheuristic method is effective in solving complex optimization problems. Among these methods, the Salp Swarm algorithm (SSA), inspired by the sailing and foraging behavior of the deep-sea salps population, has proven to be an effective method. However, in practice, SSA is prone to the problems of reduced population diversity and falling into local minima. In order to solve this problem, this paper combines several new techniques and introduces an improved version of SSA called Genetic Salp Swarm Algorithm (GSSA). Specifically, GSSA generates an initial population using a chaotic dyad-based learning method, reduces the dimensionality by a population partitioning technique and performs crossover and mutation operations on the reduced subspace, and finally acts on the optimal solution through three mutation operators to prevent the algorithm from stagnating in the local optimum. This novel GSSA algorithm is tested on 23 benchmark function test sets, CEC2017 and CEC2022. The results show that the GSSA algorithm converges faster and has higher accuracy. [ABSTRACT FROM AUTHOR]

A research report from the University of Tehran discusses the importance of vaccination in battling infectious diseases and achieving herd immunity. The report focuses on the need for an integrated vaccine supply chain network that considers sustainability and resiliency. The researchers propose a model that incorporates mix-and-match vaccine regimens, sustainability principles, and scenario-based optimization to handle uncertainties. The outcomes of the study suggest that the model effectively manages uncertainties and addresses sustainability and resiliency aspects. The report provides a case study on the COVID-19 vaccine in Iran to validate the accuracy of the model. [Extracted from the article]

A report from Changchun Normal University in Jilin, China discusses a study on the optimization of artemisinin, a medication used in malaria therapy. The study proposes an algorithm called the Artemisinin Optimization (AO) algorithm, which draws inspiration from the process of artemisinin therapy for malaria. The algorithm is tested on benchmark function sets and breast cancer pathology image segmentation applications, demonstrating its efficiency and potential in real-world optimization applications. The research concludes by providing access to the source codes for further exploration. [Extracted from the article]

A new report discusses research conducted by the Tianjin Institute of Environmental & Operational Medicine in Tianjin, People's Republic of China, on the topic of robotics. The study focuses on the issue of indoor particulate matter (PM) pollutants and the need for efficient source localization due to the health risks they pose. The researchers used a multirobot system to explore real-world PM releases in dynamically ventilated environments. They evaluated two optimization methods and found that both were successful in locating constant PM sources, with one method offering slightly higher efficiency. The study emphasizes the importance of flexible pollutant control strategies for public health protection. [Extracted from the article]

A study conducted by researchers at North China University of Science and Technology proposes a new integrated scheme for accurately predicting breast cancer, aiding in early diagnosis and treatment planning. The study utilizes five machine learning models and applies feature selection and optimization techniques to improve the models' predictive performance. The proposed model, called FS-WOA-Stacking, achieves high accuracy levels compared to existing breast cancer prediction models. This research provides valuable insights for improving breast cancer diagnosis and treatment. [Extracted from the article]

A recent study conducted by researchers at Wenzhou University in China focuses on improving the accuracy of image segmentation for breast cancer diagnosis. The study proposes a refined whale optimization algorithm that combines a quantum phase interference mechanism and an enhanced solution quality strategy. The algorithm is compared to other state-of-the-art algorithms and is tested on benchmark datasets to validate its performance. The results demonstrate the effectiveness of the segmentation method in both low and high threshold levels, making it a valuable technique for practical applications. The study has been peer-reviewed and provides potential avenues for further research in breast cancer diagnosis. [Extracted from the article]

The article presents findings from Wuhan University in China, on developing a classification algorithm to enhance motor imagery brain-computer interfaces (MI-BCIs). It proposes a multi-feature fusion approach using an SVM-AdaBoost algorithm to improve the recognition accuracy of electroencephalography signals related to motor imagery.

A recent study conducted at Prince Sattam bin Abdulaziz University has developed a new method called RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution) to address the challenge of gene selection in high-dimensional RNA-Seq data for cancer classification. The study utilized the RBAVO-DE algorithm to select the most informative genes from a dataset of over 20,000 genes and assessed their relevance across twenty-two cancer datasets. The algorithm demonstrated superior performance compared to other widely recognized meta-heuristic algorithms, achieving up to 100% classification accuracy and reducing the feature size by up to 98% in most datasets. This research has the potential to enhance the precision of gene selection for cancer research and improve the identification of key genetic markers. [Extracted from the article]

A recent study conducted by researchers at Erciyes University explores the use of an algorithm called the Best Individual Guided Immune Plasma Algorithm (BIGIPA) for path planning in unmanned combat aerial vehicles (UCAVs). The study highlights the importance of determining optimal flight paths for UCAVs considering various constraints such as enemy threats and fuel limitations. The researchers found that the BIGIPA algorithm outperformed other path planners based on meta-heuristic algorithms in terms of convergence performance and the quality of final solutions. This research provides valuable insights into the development of more efficient and safe UCAV paths. [Extracted from the article]

Researchers at Wenzhou University in China have developed an advanced optimization algorithm called Biogeography-based Learning Rime Optimization Algorithm (BLRIME) to improve the image segmentation of lupus nephritis (LN). LN is the most common symptom of systemic lupus erythematosus, and the demand for efficient image segmentation techniques has grown with its increasing prevalence. The BLRIME algorithm integrates multi-threshold image segmentation with metaheuristic algorithms to enhance the efficiency of LN image segmentation. Experimental studies have shown that BLRIME demonstrates superior global search capabilities and produces remarkable outcomes in the segmentation of LN images. [Extracted from the article]

Researchers from Manonmaniam Sundaranar University in Tamil Nadu, India, have developed a framework to enhance breast cancer diagnosis using image processing and machine learning techniques. The framework includes a denoising strategy, feature extraction using a fine-tuned MobileNetV3 model, and a novel feature selection process based on the Artificial Hummingbird Algorithm. The proposed model achieved an impressive classification accuracy of 99.51% when evaluated using the Breast Cancer Histopathological Image Classification dataset. This research contributes to the urgent need for advanced diagnostic methodologies in breast cancer. [Extracted from the article]

A recent study conducted by researchers at the School of Computational Science in Nanded, India, focuses on the early detection of breast cancer, which is the most common type of tumor in women and a leading cause of death. The study aims to develop a sophisticated diagnostic method that can differentiate between benign and malignant breast cancers without the need for a surgical sample. The researchers propose a model that utilizes pre-processing techniques, feature extraction, and an optimized deep belief network (DBN) for classification. The proposed model achieves an accuracy of over 93% for two datasets, outperforming existing models. The research has been peer-reviewed and published in the International Journal of Adaptive Control and Signal Processing. [Extracted from the article]