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Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of multi-institutional regionalization of primary health care units. The problem consists of deciding where to place new facilities, capacity expansions for existing facilities, and demand allocation in a multi-institutional system to minimize the total travel distance from demand points to health care units. It is known that traditional exact methods as branch-and-bound are limited to solving small- to medium-size instances of the problem. Given that real world-instances can be large, in this paper we propose an iterated greedy algorithm with variable neighborhood descent search for handling large-scale instances. Within this solution framework, several methods are developed. A greedy constructive method and two deconstruction strategies are developed. Another interesting component is the exact optimization of a demand allocation subproblem that is obtained when the location of facilities is previously fixed. An empirical assessment using real-world data from the State of Mexico's Public Health Care System is carried out. The results demonstrate the effectiveness of the proposed metaheuristic in handling large-scale instances. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

Selecting weighting factors for H-infinity controller requires tones of knowledge and experience without guaranteeing obtaining the best results. This paper proposes novel idea for H-infinity controller synthesis using metaheuristics (MHs). The optimisation problem for the H-infinity controller synthesis is posed to find the weighting factors to optimise the control performance subject to stability and control handling constraints. A number of established MHs are used to solve the problem while their performance are investigated. The results indicate that the Artificial Hummingbird Algorithm and Grey Wolf Optimizer is the most efficient algorithm for the optimisation problem of H-infinity flight control design. Based on this study, the performance of several MHs on solving the H-infinity flight control design are examined while the optimum weighting factors for H-infinity flight controller are obtained. This paper is said to be a based line of studying in easy and efficient way for H-infinity controller synthesis using MHs. [ABSTRACT FROM AUTHOR]

• An efficient slime mould algorithm PPMSMA with 3 improved strategies is proposed. • A set of theoretical and experimental analysis methods are proposed and used. • The time complexity, spatial complexity and convergence of PPMSMA are proved. • Multiple experimental methods are used to test the performance of PPMSMA. • The application ability of PPMSMA is tested by solving engineering problems. Metaheuristic intelligent optimization algorithms are effective methods for solving high-dimensional nonlinear complex optimization problems. The slime mould algorithm is a novel intelligent optimization algorithm proposed in 2020. However, the basic slime mould algorithm still has some shortcomings, such as slow convergence rate, easy to fall into local extremum, and unbalanced exploration and development capability. To further improve and expand the optimization ability and application scope of the slime mould algorithm, and enhance its performance in solving large-scale complex optimization problems, this paper proposes a slime mould algorithm (PPMSMA) based on Gaussian perturbation and phased position update, positive variation, and multi-strategy greedy selection. Firstly, Gaussian perturbation and phased position update mechanism are introduced to avoid the difficulty of the algorithm to jump out of the local extrema and also to speed up the convergence of the algorithm. Then, a positive variation strategy based on the sine cosine mechanism is introduced to move the variation of the population towards a better direction. Finally, a multi-strategy greedy selection mechanism is introduced, which effectively improves the search ability of the algorithm. The analysis and research on the optimization ability and performance of metaheuristic algorithms mainly include two aspects: theoretical analysis and experimental testing. Theoretical analysis has always been a relatively weak link in the research of metaheuristic algorithms, and there is currently no clear and effective method formed. For experimental testing, although there are more methods, they often lack systematization and adequacy. In this paper, a more complete, fine-grained and systematic approach to theoretical and experimental analysis is proposed. In the theoretical analysis part, the time complexity and spatial complexity of the PPMSMA algorithm are analytically proved to be the same as the basic slime mould algorithm, and the probability measure method is used to prove that PPMSMA algorithm can converge to the global optimal solution. In the simulation experiment section, the PPMSMA algorithm is compared with multiple sets of 10 representative comparison algorithms on the CEC2017 complex test function set suite for optimization accuracy analysis, Friedman comprehensive ranking analysis, average optimization rate analysis of PPMSMA relative to other algorithms, convergence curve analysis, and Wilcoxon rank-sum test analysis. To further examine the scalability of the improved algorithm in solving large-scale optimization problems, PPMSMA is compared with the above 10 comparative algorithms under 1000 dimensional conditions in the large-scale global optimization test set CEC2010, and the solution stability of each algorithm is analyzed through violin plots. The results show that the PPMSMA algorithm has significantly improved convergence performance, optimization accuracy, and solution stability in both high-dimensional and large-scale complex problems, and has significant advantages compared to multiple sets of 10 representative comparative algorithms. Finally, PPMSMA and 10 other comparative algorithms are used to solve engineering design optimization problems with different complexities. The experimental results validate the universality, reliability, and superiority of PPMSMA in handling engineering design constraint optimization problems. [ABSTRACT FROM AUTHOR]

Nature- and society-inspired metaheuristic algorithms have recently become the most promising technological model. To solve more complex optimization problems and complicated engineering applications, this paper proposes a new people duality psychological tendency and feedback mechanism-based Inherited Optimization Algorithm(IOA), which is inspired by people showing positive-negative duality cognitive tendency and adaptive feedback behavior when selecting information resources with different identity attributes. The IOA algorithm contains the symmetric two exploration phases. The exploitation phase adaptively regulates the dualistic psychological balance of people in inheriting the information resources with better existence value through a feedback regulation mechanism controlled by the profitability awareness to increase population diversity. This paper qualitatively and quantitatively evaluates the optimization performance of IOA on 84 benchmarks, including swarm convergence behavior, effectiveness, convergence, robustness, and significance. The scalability of the IOA is investigated using the CEC2017 suites. The algorithm performance in solving constrained optimization is verified on 8 engineering problems. All statistical results of the IOA are compared with the most promising 12 metaheuristics, which shows that the absolute computational efficiency of IOA on four types of functions is 95%, 96.67%, 80.95%, and 76.92%, respectively, the average rank (rank sum ratio) of IOA is 1.08 (1.19%) among the 13 algorithms, ranking first. The Wilcoxon signed rank test results on the CEC2017 suites show that IOA contains 1437 significance indicators out of 1440 comparisons, with the proportion of significant differences 99.79%, which suggests the proposed IOA maintains efficient search efficiency. [ABSTRACT FROM AUTHOR]

Metal Additive Manufacturing (AM) lattice structure is widely used in various fields because of its lightweight and one-time forming characteristics. Nondestructive reliability evaluation still presents a challenge that requires immediate attention, especially in light of the service setting and when structural failure is used as an evaluation criterion. In this paper, the failure evaluation problem of structure is discussed based on the defects that affect yield stress, and the yield stress as the index. Firstly, a Computed Tomography (CT) scanner is used for non-destructive testing of metal AM lattice structure samples, and improved YOLO V7 is used to identify and count defects in CT scan images. The interval type 2 (IT2) fuzzy model is then enhanced using the irregular Gaussian function, and a meta-heuristic algorithm (particle swarm optimization) is added to enhance the model's predictive capabilities. Finally, simulation and experiment are used to validate the AM lattice structure's failure evaluation. The improved model's superiority in the simulation exercise is demonstrated by contrasts with IT2 and the Type 1 fuzzy model. The yield stress prediction deviation of the sample during application verification is 1.12%, which attests to the validity of the failure prediction technique suggested in this paper. • The IT2 fuzzy model is used to predict the failure of lattice structures. • An irregular Gaussian function is designed as model principal membership function. • Simulation data is used instead of actual testing to complete the yield stress prediction. • Use improved YOLO V7 model to detect internal defects in actual samples. [ABSTRACT FROM AUTHOR]

Airspace surveillance is a significant issue for many countries to control and manage their airspace. The number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with the least possible number of radars in a certain region. The radar placement problem is considered as a multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and make it more suitable for solving this optimization problem. The experimental results show that a coverage rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it exceeds 99%. • This paper solves the radar placement problem using a set of metaheuristic approaches. • The problem is considered as a multi-objective optimization problem. • Two adaptive and enhanced versions of the NSGA-II algorithm are proposed. • The results show that a coverage rate of 98% can be achieved with just a few radars. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

Grey prediction evolutionary algorithm (GPE) is an emerging category of meta-heuristic algorithm based on mathematical model. It generates offspring by predicting the macroscopic evolutionary trend of population series. On the contrary, the mining of local evolutionary trend are flawed. To address this imperfection, this paper introduces a quadratic function as a surrogate model to mine the local evolutionary trend of population series for the first time. Thereby an improved grey prediction evolutionary algorithm called grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (GPE-QI) is designed. The surrogate model based on quadratic interpolation is regarded as an external auxiliary tool to mine the local evolutionary trend of population series by using currently superior individuals. Through utilizing the mined local trend information, a donor population closer to the optimal solution is formed to improve the prediction direction of the algorithm. The performance of GPE-QI is tested on CEC2019, CEC2020 benchmark functions and six engineering design problems. The experimental results demonstrate the stronger competitiveness of GPE-QI to well-established algorithms in terms of solving accuracy and convergence rate. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Improvement can be made by enhancing local search of grey prediction evolution. • Mine local evolutionary trend of population series by quadratic interpolation. • Construct a surrogate model by using the local evolutionary trend. • Design an improved grey prediction evolutionary algorithm with a surrogate model. • The proposed algorithm is more competitive than the well-established. [ABSTRACT FROM AUTHOR]

In most data dimensionality reduction tasks, feature selection (FS) is an essential preprocessing stage. The slime mould algorithm (SMA) is a novel metaheuristic optimization algorithm. This paper proposes a feature selection method for banknote dirtiness recognition based on mathematical functions driven slime mould algorithm. This method is applied to the feature selection of banknote dirtiness recognition, and the Lévy flight operator is used to replace the control parameter vc in SMA, and a series of mathematical functions are added to replace the control parameter vb in the slime mould algorithm to enhance the global search capability. Test results show that this method achieves the highest accuracy of 76.85 % in recognizing the banknote dirtiness. Additionally, by applying the same method to feature selection and recognition of dirtiness for the left middle white area images of banknotes, the highest accuracy reaches 74.99 %. This suggests that the left middle white area can essentially replace the whole banknote. To verify the performance of the proposed feature selection method, comparative tests with four other optimization algorithms are carried out, and the results demonstrate that three improvement strategies can effectively improve the performance of SMA while maintaining a balance between exploration and exploitation. Tests have revealed that these three algorithms have significant advantages over the past SMA in terms of recognition accuracy and convergence speed when it comes to identifying the dirtiness of banknotes. Therefore, this method is considered to be one of the most effective methods to solve the feature selection problem of banknote dirtiness. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming due to the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASA R L) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The implementation of the HH improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition. • A new methodology for solving high-dimensional electric vehicle routing problems. • A HH based on hybridization of self-adaptive SA and RL treating it as a MAB problem. • State-of-the-art results for the CEVRP benchmark proposed for the IEEE WCCI2020. [ABSTRACT FROM AUTHOR]

Gaining-sharing knowledge based algorithm (GSK) is a recently emerged meta-heuristic algorithm based on human behavior and has been successfully applied to solve various optimization problems. However, GSK tends to get trapped in local optimum due to the rapid loss of population diversity during the optimization process, resulting in an imbalance between exploration and exploitation. To overcome this deficiency, this paper proposes an enhancing population diversity based GSK (EPD-GSK) framework. The proposed EPD-GSK framework incorporates three components: (1) The utilization of Sobol sequence with low divergence to initialize the population, enhancing the diversity of initial solutions. (2) The integration of the Cauchy mutation strategy in the junior phase to perturb individuals and expand the search space. (3) The application of the reverse learning update mechanism in the senior phase, increasing the likelihood of escaping local optimum. These techniques promote population diversity throughout the exploration and exploitation stages. The proposed EPD-GSK framework was evaluated on CEC2017, CEC2020, and the latest CEC2022 test suites as well as on four constrained real-world engineering design problems. The experimental results demonstrate that EPD-GSK can effectively improve the performance of various existing GSK algorithms. Furthermore, EPD-GSK also exhibits better performance compared with other state-of-the-art algorithms. • Enhancing population diversity based GSK (EPD-GSK) framework is proposed. • EPD-GSK integrates Sobol sequence, Cauchy mutation and reverse learning update. • EPD-GSK significantly improves the performance of various existing GSK algorithms. • EPD-GSK also outperforms other well-known meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates a novel scheduling problem, the multi-AGV scheduling problem with sudden failure (MASP-SF), which is of great importance in modern intelligent manufacturing systems. The problem considers how to assign tasks to multiple AGVs with the objective of minimizing the total cost under two events of normal production and sudden failure in the workshop. In the event of normal production, a population-based variable neighborhood search (PVNS) algorithm with effective strategies such as rule-based stochastic method in the initialization stage, pancake flipping strategy in the shaking stage, improved neighborhood structures in the variable neighborhood descent stage is presented to solve the multi-AGV scheduling problem. In the event of sudden failure, a mixed-integer linear programming model and two rescheduling strategies are proposed to solve MASP-SF. The two rescheduling strategies are the rapid repair rescheduling strategy and a rescheduling strategy based on an improved NNH. To validate the above ideas, we conduct comprehensive and in-depth experiments on a practical plant and statistical analysis of the resulting data. The results show that the scheduling solutions generated by the rescheduling strategies are feasible, and the proposed PVNS algorithm has a superior performance compared to existing algorithms in multi-AGV scheduling problem. [ABSTRACT FROM AUTHOR]

This paper tackles a flexible job shop scheduling problem with extra resource constraints (FJSP-ERC) which arises in the practical context of aircraft manufacturing. The challenge is to schedule operations on identical computer numerical control (CNC) machining centers, considering the simultaneous availability of machines and tools. We describe the FJSP-ERC as a mixed integer linear programming (MILP) model and then propose an efficient meta-heuristic algorithm ABC II to solve the scheduling problem. This is a novel structured artificial bee colony algorithm incorporated with problem-specific local improvement strategies organized by Adaptive Large Neighborhood Search (ALNS). After applying the initialization techniques to the solution, the main structure of the algorithm is designed in two stages. In the first stage, all machining operations of a part are taken as a whole in order to find some near-optimal job order. The solutions obtained in the first stage will then serve as a warm-up for the second stage, where the operations are treated as independent individuals. After preliminary parameter tuning, comprehensive computational experiments were conducted to verify the effectiveness of the proposed solution approach by comparing with the solutions obtained by five different algorithms and CPLEX. The results show that the proposed ABC II significantly outperforms other methods in terms of efficiency and effectiveness. • A dual-resource constrained flexible job-shop scheduling problem is studied. • An efficient meta-heuristic algorithm is proposed to solve the problem. • The algorithm incorporates a problem-specific local improvement strategy. • Extensive computational experiments have been conducted to verify the algorithm. [ABSTRACT FROM AUTHOR]

Surrogate-assisted meta-heuristic algorithms (SAMAs) have been increasingly popular in recent years for solving challenging optimization problems. However, the majority of recent studies concentrate on low-dimensional problems. In this paper, a scale-free network based multi-surrogate-assisted stochastic fractal search (SF-MSASFS) algorithm is proposed. Specifically, based on the stochastic fractal search (SFS) algorithm, multiple surrogate models, namely RBF and Kriging models, are used to enhance the robustness of the algorithm. The scale-free network is used to build the topology structure of the SFS algorithm, and the offspring particles are generated by means of the connection relationship between the parent particles. In addition, to further enhance adaptability, an adaptive mechanism is implemented, tailoring three distinct update mechanisms based on their corresponding reward values. Finally, the performance of the proposed algorithm is demonstrated by comparing the proposed algorithm with a number of state-of-the-art SAMAs on several well-known benchmark functions, in particular in solving high-dimensional expensive problems (HEOPs). The results underscore the SF-MSASFS algorithm's commendable optimization performance. (The MATLAB code can be found at the authors github: https://github.com/xiaodi-Cheng/SF-MSASFS) [ABSTRACT FROM AUTHOR]

Maintenance planning and scheduling are an essential part of manufacturing companies to prevent machine breakdowns and increase machine uptime, along with production efficiency. One of the biggest challenges is to effectively address uncertainty (e.g., unexpected machine failures, variable time to repair). Multiple approaches have been used to solve the maintenance scheduling problem, including dispatching rules (DR), metaheuristics and simheuristics, or most recently reinforcement learning (RL). However, to the best of our knowledge, no study has ever studied to what extent these techniques are effective when faced with different levels of uncertainty. To overcome this gap in research, this paper presents an approach by analyzing the impact of categorized levels of uncertainty, specifically high and low, on the failure distribution and time to repair. Upon the formalization of the maintenance scheduling problem, the experiments conducted are performed in simulated scenarios with different degrees of uncertainty, and also considering a real-life manufacturing use case. The results indicate that rescheduling based on a genetic algorithm (GA) simheuristic outperforms RL and DR in terms of total machine uptime, but not in terms of the mean time to repair when configured with high re-optimization frequencies (i.e., hourly re-optimization), but rapidly underperforms when the re-optimization frequency decreases. Furthermore, our study demonstrates that GA-simheuristic is highly computationally demanding compared to RL and rule-based policies. • Problem of dynamic maintenance scheduling under uncertainty is assessed. • Reinforcement Learning, Genetic Algorithms and Dispatching rules were compared. • Exploration of uncertainty factors such as time to repair and failure distribution. • Artificial and real-based scenarios were evaluated. [ABSTRACT FROM AUTHOR]

The University Course Scheduling Problem (UCSP) is a complex combinatorial optimization problem. Most universities still use hand-operated scheduling, which usually takes a long time and results in numerous irrationalities. The imperative of conceiving a algorithm to navigate this predicament is undeniable. This paper introduces an advanced mixed integer linear programming paradigm, incorporating constraints encompassing both the temporal coherence of courses and the equitable dispersion of these courses throughout the academic week. To tackle this issue, we design a novel two-stage meta-heuristic algorithm; the first stage harnesses the genetic algorithm to cluster courses into sets by exploiting graph coloring techniques, while the subsequent stage leverages an enhanced tabu search heuristic to assign these clusters into distinct temporal intervals. The effectiveness and efficiency of the proposed heuristic has been validated by extensive computational studies based on authentic data gleaned from Huazhong University of Science and Technology's School of Management and the results are compared with CPLEX and common heuristic. Moreover, our research examines the algorithm's convergence properties and the effects of fast neighborhood evaluation strategy. Sensitivity analysis is performed to probe the algorithm's responsiveness to the new constraints. [ABSTRACT FROM AUTHOR]

Although the distributed permutation flowshop scheduling problem (DPFSP) has recently received extensive research attention, most studies assume that either all jobs have due date constraints or none of them do. Nevertheless, in practice, it is very common to schedule jobs with due dates alongside jobs without a due date. This paper addresses a DPFSP with part of jobs subject to a common deadline (DPFSP-PJCD). The objective is to minimize the total completion time. We establish a mathematical model and propose a Self-adaptive Population-based Iterated Greedy (SPIG) algorithm that is specifically tailored to the characteristics of the problem. We design a hybrid constructive heuristic to generate a population of potentially high-quality solutions. We introduce an insertion-based acceleration method that combines three distinct accelerations to improve operational efficiency. We propose some effective operators to carry out the selection, destruction, and construction of solutions, as well as a local search mechanism, to balance the exploitation and exploration of the algorithm. Additionally, we employ a self-adaptive method to determine a key algorithmic parameter depending on the search phase and search space. We also utilize a self-adjustment insertion procedure to handle infeasible solutions. Through comprehensive experimental evaluations, we demonstrate that the proposed SPIG outperforms five state-of-the-art metaheuristics from the closely related literature, providing effective solutions for the DPFSP-PJCD considered. [ABSTRACT FROM AUTHOR]

In recent years, feature selection research has quickly advanced to keep up with the age of developing expert systems. This is because the applications of these systems sometimes need massive datasets. Researchers who have an interest in creating novel feature selection methods or enhancing existing technologies have grown their interest in this topic. The current version of binary PSO (BPSO) is not developed as well as continuous PSO and does not follow the main principles of the standard PSO algorithm. Unlike the continuous version of the PSO, in BPSO, particle position is restricted to the Hamming space. Thus, there is no risk of swarm divergence, but the problem of premature convergence of the swarm arises. This paper presents an enhanced particle swarm optimization employed for feature selection to tackle the limitations of the current version of binary BPSO. Its primary quality is the application of a position update mechanism to appropriately select optimal features. The position update mechanism is designed to avoid the premature convergence issue suffered by BPSO, by determining the probability of the particles switching positions using position values rather than velocity. Two variant methods for determining the probability of changing the position of a particle element were introduced. These result in the two variants of enhanced binary particle swarm optimization (EBPSO) for feature selection, called EBPSO1 and EBPSO2. Several experiments were performed to analyze the effectiveness of the proposed method and compare it with state-of-the-art feature selection methods based on eighteen frequently used benchmark datasets. The experimental findings demonstrated that the proposed approach generated the most accurate classification in contrast to the alternative feature selection approach on most of the datasets. As a result, it can be inferred based on the outcomes obtained that this proposed approach performs significantly superior to the other state-of-the-art feature selection approaches. [ABSTRACT FROM AUTHOR]

Highlights • The paper focuses on a single machine Jobs-Maintenance scheduling problem. • We enhanced the MHS by Zammori et al. (2014). • We also design a Cuckoo Search (CS) metaheuristics based on a new Lévy Flight. • Performance of the MHS improved, but this metaheuristic remains sub-optimal. • The newly developed CS is, instead, highly effective and robust. A bstract This paper presents and compares new metaheuristics to solve an integrated jobs-maintenance scheduling problem, on a single machine subjected to aging and failures. The problem, introduced by Zammori et al. (2014), was originally solved using the Modified Harmony Search (MHS) metaheuristic. However, an extensive numerical analysis brought to light some structural limits of the MHS, as the analysis revealed that the MHS is outperformed by the simpler Simulated Annealing by Ishibuchi et al. (1995). Aiming to solve the problem in a more effective way, we integrated the MHS with local minima escaping procedures and we also developed a new Cuckoo Search metaheuristic, based on an innovative Lévy Flight. A thorough comparison confirmed the superiority of the newly developed Cuckoo Search, which is capable to find better solutions in a smaller amount of time. This an important result, both for academics and practitioners, since the integrated job-maintenance scheduling problem has a high operational relevance, but it is known to be extremely hard to be solved, especially in a reasonable amount of time. Also, the developed Cuckoo Search has been designed in an extremely flexible way and it can be easily readapted and applied to a wide range of combinatorial problems. [ABSTRACT FROM AUTHOR]

Highlights • A new method to solve vehicle routing through meta-learning techniques. • Two sets of meta-features are used: basic and landmarking meta-features. • A multilayer perceptron classifier is used to select meta-heuristics. • Our proposal statistically improves the overall performances previously reported. Abstract This paper describes a method for solving vehicle routing problems with time windows, based on selecting meta-heuristics via meta-learning. Although several meta-heuristics already exist that can obtain good overall results on some vehicle routing problem instances, none of them performs well in all cases. By defining a set of meta-features that appropriately characterize different routing problem instances and using a suitable classifier, our model can often correctly predict the best meta-heuristic for each instance. The main contributions of this paper are the definition of two meta-feature sets, one based on what we call 'basic' instance properties and another based on the number of feasible solutions found by perturbative heuristics via a greedy process. We use a multilayer perceptron classifier, combined with a wrapper meta-feature selection method, to predict the most suitable meta-heuristic to apply to a given problem instance. Our experimental results show that the proposed method can significantly improve upon the overall performance of well-known meta-heuristics in the field. Therefore, this paper proposes to store, share and exploit in an off-line scheme the solutions obtained in instances of different scenarios such as the academy or industry, with the aim of predicting good solvers for new instances when necessary. [ABSTRACT FROM AUTHOR]

Highlights • Using geographical location in distance matrixes cause extra optimization process. • The result of solved mixed integer programing is the input for metaheuristics. • Ant colony algorithm is selected metaheuristic for applying in promotion stage. • Horizontal collaboration of suppliers would lead to cost saving for these agents. • Shapley value and tau value are reliable payoff sharing technique for the problem. Abstract The impact of the horizontal collaboration of logistic providers as upstream supply chain elements is studied in this paper. The logistic providers can be considered as different players and, in order to analyze different types of coalitions by computing the synergy between the players in each cooperative structure, we developed a mixed integer programming (MIP) formulation for a cooperative inventory routing problem (Co-IRP). In this paper, each player has one depot and some retailers, which are considered as its available market. The model will have a many-to-many network structure once a coalition is formed, where a single product from different depots will be delivered to different retailers. We establish a robust version of Co-IRP, which is called Co-RIRP, considering uncertainty in the network. We developed a few metaheuristic algorithms to promote the gained results once MIP is solved. Finally, in order to share the possible cost saving between players and compare the assignments, we applied a few payoff distribution methods. In addition, a comprehensive analysis of example results and some important managerial insights are presented. [ABSTRACT FROM AUTHOR]

In this paper, we address the Integrated Planning and Scheduling Problem (IPSP) on parallel and identical machines. Planning and scheduling are essential for the efficient management of supply chains. Although both pursue the same general objective, they are usually performed independently mostly because they relate to different timescales. As a consequence, the generated plans and schedules are typically sub-optimal from a global standpoint. The approaches followed in this paper explicitly consider the interdependence between the planning and scheduling activities by solving them simultaneously in an integrated way. We explore different heuristics based on variable neighborhood search procedures with new and specifically designed neighborhood structures relying on the properties of the IPSP. The quality of these approaches is evaluated through extensive computational experiments performed on a large set of benchmark instances. The results show that the proposed methods achieve high-quality solutions, with a substantially low computation time, outperforming other state-of-the-art results reported in the literature. • New approach to solve planning and scheduling simultaneously in an integrated way. • Different metaheuristics with new and specially designed neighborhood structures. • Exhaustive computational study on a large set of benchmark instances. • Low computing time for high-quality solutions, outperforming literature results. [ABSTRACT FROM AUTHOR]

• HeuMetaDEP integrates DEP, ML paradigm, and HOAs to solve TSP problems. • DEP using ML paradigm improves the predictive performance of the ensemble. • Three HOAs select meta-characteristics and optimize hyper-parameters. • Seven sets of meta-characteristics are constructed for addressing the TSP problems. • A closeness degree measurement function is designed for meta-characteristics extraction. The ensemble learning paradigm demonstrates powerful advantages in solving Time Series Prediction (TSP) problems. But static ensemble methods do not always play a positive role in predictive performance. Dynamic Ensemble Pruning (DEP) methods aim to select an optimal subensemble for each test sample. By fusing the Meta-learning paradigm with the Heuristic optimization algorithm, this paper develops a hybrid Dynamic Ensemble Pruning (HeuMetaDEP) framework for TSP. The MetaDEP subalgorithm designed based on Meta-Learning (ML) trains a meta-predictor, which can judge whether each base leaner in the ensemble has enough ability to predict the query sample, to dynamically acquire the proper subensemble for each sample. Based on different aspects of the generalization ability of the base learner, seven sets of meta-characteristics are constructed for the training of the meta-predictor. And the Heuristic Optimization Algorithm (HOA) is used to filter the meta-characteristics and select the optimal parameters for a specific TSP problem. In this paper, we apply three HOAs, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, and Artificial Fish Swarm Algorithm (AFSA) to form a set of three specific algorithms, collectively abbreviated as the HeuMetaDEP algorithms. An experimental evaluation conducted on eight benchmark time series datasets shows that the proposed HeuMetaDEP algorithms are significantly superior on more than half of the datasets, and achieve an average RMSE drop of 12.49% and an average MAE drop of 12.73% on the eight datasets, compared with the five naive algorithms. In addition, the three groups of extended experiments demonstrate that the proposed algorithms have better generalization performance compared with other advanced DEP algorithms, except under the small-sample condition. [ABSTRACT FROM AUTHOR]

This paper presents a new quantum chaotic cuckoo search algorithm (QCCS) for data clustering. Recent researches show the superiority of cuckoo search (CS) over traditional meta-heuristic algorithms for clustering problems. Unfortunately, all the cuckoos have identical search behaviours that may lead the algorithm to converge to local optima. Also, the convergence rate is sensitive to initial centroids seeds that are randomly generated. Therefore, the main contribution of this paper is to extend the CS capabilities using nonhomogeneous update inspired by the quantum theory in order to tackle the cuckoo search clustering problem in terms of global search ability. Also, the randomness at the beginning step is replaced by the chaotic map in order to make the search procedure more efficient and improve the convergence speed. In addition, an effective strategy is developed to well manage the boundaries. The experimental results on six famous real-life datasets show the significant superiority of the proposed QCCS over eight recent well known algorithms including, genetic quantum cuckoo search, hybrid cuckoo search and differential evolution, hybrid K-means and improved cuckoo search, standard cuckoo search, quantum particle swarm optimization, differential evolution, hybrid K-means chaotic particle swarm optimization and genetic algorithm for all benchmark datasets in terms of internal and external clustering quality. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are a crucial part of the Internet of Things (IoT), and research on WSN routing protocols has always been a hot topic in academia. However, traditional WSN routing protocols have limited utilization of available information during the routing decision process, leading to challenges such as insufficient adaptability to network topology changes, high communication delays, and short network lifetimes. To address these issues, this paper proposes an innovative intelligent routing algorithm WOAD3QN-RP, which cleverly integrates swarm intelligence algorithms and deep reinforcement learning. The WOAD3QN-RP not only effectively reduces delay but also balances energy consumption and flexibly adapts to changes in network topology, while simultaneously determining the optimal multi-hop path, effectively extending the lifetime of the network. Firstly, the WOAD3QN-RP algorithm employs the Whale Optimization Algorithm (WOA) to determine the optimal cluster heads (CHs). In the process of selecting CHs, the algorithm comprehensively considers key factors such as the residual energy of nodes, node distance, and communication delay, thereby significantly improving the accuracy and efficiency of CH selection, which contributes to better energy distribution and performance of the network. Secondly, in terms of multi-hop path selection, WOAD3QN-RP uses a dueling double deep Q -network (D3QN) to determine the optimal multi-hop path. Through utilizing neural networks to interact with the environment, intelligent agents are trained to learn routing policies to adapt to dynamic changes in the network topology and ensure the balance between energy consumption and multi-hop routing performance. Experimental results show that WOAD3QN-RP exhibits significant advantages over existing routing protocols in terms of network lifetime, energy efficiency, and communication delay. • Novel cluster head selection algorithm using the whale optimization algorithm. • Novel intelligent multi-hop path selection using deep reinforcement learning. • Enhancing intelligence and performance of wireless sensor networks. • Simulation results reveal the effectiveness and adaptivity of routing protocol. [ABSTRACT FROM AUTHOR]

This paper aims to introduce an artificial intelligence approach to enhance short-term daily streamflow forecasts through the integration of machine learning (ML) techniques with meta -heuristic (MH) algorithms. To maximize performance, a comprehensive analysis involving the autocorrelation and partial autocorrelation functions of the observed streamflow was conducted to derive ten distinct input combinations, from which the most suitable set was selected for investigation. Subsequently, six distinct ML methods, namely, decision trees, support vector regression, long short-term memory, gated recurrent unit, multi-layer perceptron, and radial basis function (as the optimal input combination), were leveraged to predict one-day-ahead streamflow. The most promising outcomes from these six explored methods were synergistically amalgamated to enhance predictive precision. This amalgamation was then optimized by utilizing parameter values hyper-tuned by four different MH algorithms. The algorithms included the shuffled frog leaping algorithm, particle swarm optimization, ant colony optimization (ACO), and gray wolf optimization (GWO). In culmination, a case study centered around the "Wick" river in the UK was conducted to rigorously evaluate the performance of the proposed method, utilizing a range of statistical evaluation indices. The results underscored the efficacy of the proposed combined method in substantially refining streamflow predictions, exhibiting an impressive level of accuracy. Additionally, the outcomes emphasized that ACO and GWO stand out as the optimal algorithms for calibrating the parameters of the amalgamated model. For instance, the R2 values attained by the combined models optimized through the ACO and GWO algorithms were 0.69422 and 0.69444, respectively. These values notably surpass those observed in the initial models, substantiating the superiority of the applied optimization approaches. [ABSTRACT FROM AUTHOR]

Traditional machine learning algorithms face challenges in assessing flood susceptibility reliably due to their low robustness and the inherent 'black-box' nature. This paper utilizes five hyperparameter optimization algoirthms (HPO), namely grid search (GS), random search (RS), gauss process (GP), tree-structured parzen estimator (TPE) and simulated annealing (SA), to tune the traditional random forest's (RF) hyperparameters to improve the robustness of flood hazard mapping (FHM) models at Ningxiang City Hunan Province, China. Additionally, SHapley Additive exPlanations (SHAP) method were used to interpret the decision-mechanisms of these flood hazard models. This study considers 19 pluvial flood influencing factors and 2064 flood locations to create a geospatial database. The performance of each hybrid model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC) and several validation methods. The results demonstrate that the developed hybrid models demonstrated good performance, with RF-TPE achieving the highest AUC (0.9660), followed by RF-GP (0.9648), RF-SA (0.9624), RF-GS (0.9612), RF-RS (0.9600), and RF (0.9539). The RF-TPE model exhibits superior robustness than other models, and the FHM constructed using it is more reliable. HPO is an effective approach to improve the predictive accuracy and robustness of FHM models. When considering limited computational resources, Bayesian optimization (TPE) should be prioritized for optimizing FHM models, followed by metaheuristic algorithms and model-free algorithms. Moreover, the study revealed that distance from river, peak rainfall intensity, continuous rainfall, antecedent effective rainfall, and terrain relief, are the most significant for pluvial FHM modeling in this region. [ABSTRACT FROM AUTHOR]

This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), a metaheuristic algorithm inspired by human evolution. HEOA divides the global search process into two distinct phases: human exploration and human development. Logistic Chaos Mapping is employed for initialization. In the human exploration phase, an initial global search is conducted, followed by the human development phase, in which the population is categorized into leaders, explorers, followers, and losers, each utilizing distinct search strategies. The convergence speed and search accuracy of HEOA are evaluated using 23 well-established test functions. Furthermore, the algorithm's applicability in engineering optimization is assessed with four engineering problems. A comparative analysis with ten other algorithms highlights HEOA's effectiveness, as evidenced by various performance metrics and statistical measures. Consistently, the results demonstrate that HEOA surpasses most current state-of-the-art algorithms in approximating optimal solutions for complex global optimization problems. The MATLAB code for HEOA is available at https://github.com/junbolian/HEOA.git. [ABSTRACT FROM AUTHOR]

Enhancing the accuracy of breast cancer recurrence prediction is crucial, mainly when dealing with genomics data, which presents challenges such as high dimensionality, noise, non-linearity, and limited sample sizes. This paper introduces Semi-Supervised Survival Laplacian Regression (S3LR), a novel feature selection algorithm designed to improve breast cancer recurrence prediction by effectively handling censored, event, and unlabeled data. S3LR modifies the Laplacian Score (LS) by incorporating a distance matrix to calculate the weight matrix, and it integrates heuristic and metaheuristic optimization algorithms to optimize the weighted matrix. These enhancements refine feature selection and overall performance. In our evaluations using three datasets and comparisons with state-of-the-art techniques, S3LR combined with Particle Swarm Optimization (PSO) demonstrates significant improvements in C-index and mean absolute error (MAE). Average C-index values reach 68.80 %, 59.49 %, and 67.66 %, with average MAE values of 15.98, 7.87, and 8.65 months, respectively. These results showcase S3LR's effectiveness in predicting recurrence, even with censored data, for more precise and reliable outcomes. Furthermore, the framework's versatility extends beyond breast cancer and can readily be applied to address other survival and recurrence problems. [ABSTRACT FROM AUTHOR]

• Moisture affects microbial succession and volatiles generation by mediated with Lactobacillus. • The ratio of ethyl acetate to ethyl lactate and content of acid, alcohol positively relate to moisture. • Increase moisture accelerate formation of Lactobacillus -dominated fermentation microbiota. • Increase moisture enhance complexity and stability of baijiu fermentation ecological networks. Today, image segmentation methods are widely used for various applications, including object detection. Multilevel Thresholding Image Segmentation (MTIS) methods are among the efficient methods for image segmentation. In MTIS methods, it is very important to find the thresholds that gives the best performance for the MTIS and better separate and detect the objects on the image from the image background. Meta-Heuristic (MH) algorithms are among the strategies that can achieve good results in obtaining optimal thresholds to solve this problem. In this paper, we use the combination of Arithmetic Optimization Algorithm (AOA) and Harris Hawks Optimizer (HHO) to improve AOA in exploitation phase, and achieve an optimal threshold vector for MTIS. Therefore, our new hybrid AOA-HHO algorithm solves the MTIS problem with better quality than both AOA and HHO algorithms and some other MH algorithms, and can obtain better thresholds that increase the performance of the MTIS system than AOA and HHO. AOA is powerful in the exploration, and HHO in exploitation phase is powerful. Therefore, AOA-HHO uses the features of both algorithms to search the entire search space locally and globally to find the best find the solution, the high power of the AOA exploration phase, and the high power of the HHO exploitation phase. Also, we use a mathematical equation as the fitness function, that is obtained by using image features. A series of experiments were performed using seven different threshold levels on the test images. Experiments show that AOA-HHO method is better than the compared algorithms and even HHO and AOA in terms of image segmentation accuracy, fitness function value, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and execution time. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) has become a significant public health problem affecting national economic and social development, and ranks among the top causes of death in the world. Thus, people pay increasing attention to the prevention, control, and risk assessment of CVD. In this paper, an improved sparrow search algorithm (SSA) is designed to optimize the parameters of Categorical Boosting (CatBoost) model, and it is applied to the risk assessment of CVD. The contributions of this research are mainly in the following aspects: (1) In the position update formula of the discoverer, the salp swarm algorithm is integrated, the global optimal solution of the previous generation is added to improve the global search ability and local development ability of SSA; (2) Using Opposition-based Learning (OBL) and Lateral mutation strategy to improve the search ability of the worst individual; (3) Sparrow search algorithm based on salp swarm algorithm, OBL and Lateral mutation strategy (SOLSSA) is used to optimize parameters of CatBoost to improve the prediction effect, and the experiments are carried out for the proposed model (SOLSSA-CatBoost) using two CVD data sets on Kaggle. The proposed model is compared with six machine learning models, including random forest (RF), logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), light gradient Boosting (LGB) and CatBoost, and is also compared with other four optimization algorithms (whale optimization algorithm (WOA), gray wolf algorithm (GWO), seagull optimization algorithm (SOA) and SSA) in optimizing the performance of the CatBoost. The experimental results show that compared with other comparison algorithms, SOLSSA-CatBoost has better prediction effect on test set, with F1-score reaching 90% and 81.51% in two CVD data sets respectively. The SOLSSA-CatBoost model in this paper can make a more accurate prediction of patients' disease risk, and provide a certain basis for doctors to judge the condition. [ABSTRACT FROM AUTHOR]

• Considering pistachio, a supply chain network is firstly designed in this paper. • A mixed linear mathematical model is formulated based on the proposed problem. • Efficient and well-known old and recent meta -heuristic algorithms are utilized. • Three hybrid meta -heuristic algorithms are developed. • The quality of the proposed algorithms is investigated and compared. Nowadays, recent advances and developments in the agricultural sector have raised concerns regarding the environmental impact of agricultural wastes and the safety of agricultural products. Recently, reverse logistics have been used to address these issues. It is also possible to reduce the environmental impact of these by-products and return them to the network by opening compost centers and designing an optimal supply chain distribution network. In this paper, a new mixed linear mathematical model is developed for an agriculture supply chain network to minimize the total fixed and variable costs of the closed-loop supply chain,. To address the proposed model, efficient and well-known old and recent meta -heuristic algorithms are utilized. Besides, to boost intensification and diversification phases, three hybrid meta -heuristic algorithms are developed. Finally, the quality of the meta -heuristic and hybrid algorithms are investigated and compared. [ABSTRACT FROM AUTHOR]

This paper proposes three feature selection algorithms with feature weight scheme and dynamic dimension reduction for the text document clustering problem. Text document clustering is a new trend in text mining; in this process, text documents are separated into several coherent clusters according to carefully selected informative features by using proper evaluation function, which usually depends on term frequency. Informative features in each document are selected using feature selection methods. Genetic algorithm (GA), harmony search (HS) algorithm, and particle swarm optimization (PSO) algorithm are the most successful feature selection methods established using a novel weighting scheme, namely, length feature weight (LFW), which depends on term frequency and appearance of features in other documents. A new dynamic dimension reduction (DDR) method is also provided to reduce the number of features used in clustering and thus improve the performance of the algorithms. Finally, k-mean, which is a popular clustering method, is used to cluster the set of text documents based on the terms (or features) obtained by dynamic reduction. Seven text mining benchmark text datasets of different sizes and complexities are evaluated. Analysis with k-mean shows that particle swarm optimization with length feature weight and dynamic reduction produces the optimal outcomes for almost all datasets tested. This paper provides new alternatives for text mining community to cluster text documents by using cohesive and informative features. [ABSTRACT FROM AUTHOR]

The Set Covering Problem (SCP) is one of the classical Karp’s 21 NP-complete problems. Although this is a traditional optimization problem, we find many papers assuming metaheuristics for solving the SCP in the current literature. However, while the SCP is a discrete problem, most metaheuristics are defined for solving continuous optimization problems, specially Swarm Intelligence Algorithms (SIAs). Hence, such algorithms should be adapted for working on the discrete scope, but most authors did not perform any study to select a concrete binarization approach. This situation might lead to the conclusion that selecting a concrete binarization technique does not influence the behavior of the algorithm, but rather the general approach of the metaheuristic. This circumstance led us to write this paper focusing on the inherent difficulty in binarization of metaheuristics designed for continuous optimization, when solving a discrete optimization problem, concretely the SCP. To this end, we consider a recent SIA inspired by the behavior of cats and adapted to the discrete scope, which is called Binary Cat Swarm Optimization (BCSO). We replace the binarization technique assumed in the original BCSO by forty different approaches from the current literature. The results obtained while solving a standard SCP benchmark are analyzed through a widely accepted statistical method, concluding that it is crucial to select an adequate binarization approach to ensure that the solving algorithm reaches its full potential. Thus, the task of adapting a metaheuristic to the discrete scope is not a simple matter and should be carefully studied. To this end and as a result of this study, we give some recommendations to perform this task. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

• We address an urban freight transportation alternative using passenger rail network. • We adapt three classic ACO variants to solve the problem. • We develop a specific metaheuristic based on two classic ACO and some improvements. • We set the metaheuristics' parameters using Taguchi method. • We evaluate metaheuristics' performances on a large number of instances. This paper considers a promising alternative for freight transport using urban passenger rail networks. It addresses a freight rail transport scheduling problem (FRTSP), which is one of the key operational issues to integrate freight into passenger transport in urban areas. In this paper, we develop a robust ant colony optimization (ACO) metaheuristic to tackle large-size instances. Taguchi method is used to set control parameters to make the algorithm robust. Computational results clearly highlight the proposed ACO algorithm's capability of yielding near-optimal solutions in short computation times, even in large-size instances. They also show that the proposed algorithm dominates classic ACO variants, even when the parameters of these variants are also set using Taguchi method. [ABSTRACT FROM AUTHOR]

With strong penetrability and high resolution, synthetic aperture radar (SAR) images have been widely used in remote sensing image change detection. With the essence of heuristics, metaheuristics are suitable for the needs of most real-life optimization problems according to the expected solution quality and allowable calculation time. To improve the accuracy of change detection, this paper proposes a novel efficient metaheuristic change detection procedure. More specifically, we develop a superpixel-based multiobjective change detection method based on superpixel-wise feature representation and self-adaptive neighborhood-based binary difference evolution. During the clustering for feature analysis, a multiobjective optimization problem (MOP) is modeled which take the likelihood and Bhattacharyya distance between changed and unchanged classes as two objective functions. To solve the MOP, this paper firstly applies the superpixel binary representation technique into the encoding process of multiobjective evolutionary algorithm so that the dimension of pixel coding space can be substantially reduced. Then a self-adaptive neighborhood-based binary differential evolution strategy is proposed to explore the optimal change detection strategy, where a novel mutation operator integrating neighborhood information is designed for the improvement of change detection performance. Experimental results on three real SAR datasets confirm that the proposed method can make a better performance on change detection and the metaheuristic algorithm has good convergence and stability. • Change detection of synthetic aperture radar images. • Superpixel-based difference feature representation strategy. • Difference analysis in a form of evolutionary multi-objective optimization. • A self-adaptive neighborhood-based binary difference evolution framework. [ABSTRACT FROM AUTHOR]

The motivation of this paper follows strictly from industrial needs to improve competitiveness in railway companies by the efficient rolling stock utilization affected by the obligatory preventive maintenances of vehicles, which cause their temporary unavailability. Nevertheless, it is economically essential to minimize the total time on maintenances of vehicles, which can be achieved in a certain range by the scheduling of transport tasks taking into account maintenance policies of the vehicles, their mileage and date since the last maintenance, and the workload related to the tasks. Therefore, to face the problem, we model the consider issue as the job scheduling problem to maximize the availability of railway vehicles under preventive maintenances. We prove that the problem is strongly NP-hard and we propose efficient heuristic and metaheuristic algorithms that are characterized by a low computational complexity and they are able to find solutions satisfying from the perspective of industrial practice. The simulations based on real-life data reveal that our approach can provide schedules, which significantly improve availability of locomotives resulting in high potential financial benefits for railway companies. • The total time of maintenance can be minimized by the scheduling of transport tasks • The railway vehicle availability problem under preventive maintenance is formulated • The considered problem is proved to be strongly NP-hard • The proposed scheduling algorithms improve availability of railway vehicles • The results obtained on real-life data are satisfying for the industrial practice. [ABSTRACT FROM AUTHOR]

• Proposing a multi-objective boxing match algorithm as human-based algorithm. • Simulating the boxer's behavior to attract a good support from his coach and fans. • Validating the proposed algorithm to solve multi-objective optimization problems. • Developing an efficient search method to produce a variety of acceptable solutions. • Considering solutions to increase the probability of reaching a Pareto-optimal set. In the last two decades, due to having fast computation after inventing computers and also considering real-world optimization problems, research on developing new algorithms for problem having more than one objective have been one of the appealing and attractive topics both for academia and industrial practitioners. By this motivation, we introduce a Multi-Objective Boxing Match Algorithm (MOBMA) in this paper. The proposed algorithm studies the multi-objective version of the Boxing Match Algorithm (BMA) by incorporating a unique search strategy and new solutions-producing mechanism, enhancing the algorithm's capability for exploration and exploitation phases. Besides, its performance is analyzed with famous and capable multi-objective metaheuristics. We consider ten multi-objective benchmarks and three classical engineering problems. Statistical analyses are also conducted on the benchmark test functions from three engineering design problems. This study shows the superior performance of the proposed algorithm, considering both quantitative and qualitative analyses. [ABSTRACT FROM AUTHOR]

During recent years, deep convolutional neural networks (CNNs) have become increasingly popular in sensor-based human activity recognition (HAR), due to powerful feature extraction capacity. However, the required high computational cost has hindered their practical deployment in real-world applications. To address this issue, this paper presents a novel approach named ABCSearch to prune channels through an artificial bee colony (ABC) algorithm, which is a meta-heuristic optimization algorithm inspired by the foraging behavior of honeybees. Unlike traditional pruning strategies that prioritize channel importance, the proposed method seeks to identify the optimal number of channels at each layer, i.e., the optimal pruning structure. To lessen human interference, we transform the search problem of the optimal pruning structure into a simple optimization problem and solve it automatically through the ABC algorithm, which can significantly decrease both model size and computational cost without compromising performance. Specifically, we validate the effectiveness of ABCSearch for a standard five-layer CNN backbone architecture, which achieves 97.05%, 98.45%, 91.71%, 78.89% classification accuracies on four HAR benchmarks, i.e., UCI-HAR, WISDM, PAMAP2, UniMiB-SHAR. Our approach can yield 87.44%, 89.41%, 89.06%, 89.59% FLOPs reductions, and 89.54%, 88.70%, 88.38%, 90.45% less memory consumptions, as well as 4.4 × , 5.0 × , 5.8 × , 6.1 × practical speedups on real hardware devices, while maintaining comparable accuracy. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) are one of the promising technologies for the carbon neutral future, since they can be effectively used for thermal energy storage (TES), and therefore can mitigate the energy supply/demand mismatch characteristic for renewable energy resources. Mathematical models describing thermal behaviour of PCMs require various material properties, such as specific heat capacity, thermal conductivity or density, which are in many cases difficult to obtain experimentally. This paper introduces an inverse identification procedure which utilises heuristic optimisation methods to solve the inverse problem. The air-PCM heat exchanger was considered as a studied device. In order to avoid any uncertainties tied to the experimental measurement, the exact solution was generated by the numerical model directly (referred to as "pre-simulated" scenario). The cost function was constructed as a root mean square difference between the given calculated and pre-simulated outlet air temperatures. In total seven different case studies (five with a base model and two with an extended model) were investigated for the inverse identification of the effective heat capacity of a PCM and seven different heuristic optimisation methods were utilised for each case study. The results have shown that the LSHADE and DE methods were the best performing ones and that one method outperformed the other depending on the problem settings. • Description of difficult inverse identification problem. • Extension of existing model for effective heat capacity curve. • Evaluation of seven metaheuristics on seven case studies. • The best-performing methods were LSHADE and DE. [ABSTRACT FROM AUTHOR]

In this paper, a novel evolutionary optimization algorithm, named Partial Reinforcement Optimizer (PRO), is introduced. The major idea behind the PRO comes from a psychological theory in evolutionary learning and training called the partial reinforcement effect (PRE) theory. According to the PRE theory, a learner is intermittently reinforced to learn or strengthen a specific behavior during the learning and training process. The reinforcement patterns significantly impact the response rate and strength of the learner during a reinforcement schedule, achieved by appropriately selecting a reinforcement behavior and the time of applying reinforcement process. In the PRO algorithm, the PRE theory is mathematically modeled to an evolutionary optimization algorithm for solving global optimization problems. The efficiency of the proposed PRO algorithm is compared to well-known Meta-heuristic Algorithms (MAs) using Wilcoxon and Friedman statistical tests to analyze results from 75 benchmarks of the CEC2005, CEC2014, and CEC-BC-2017 test suits, which include unimodal, multimodal, hybrid, and composition functions. Additionally, the proposed PRO algorithm is applied to optimize a Federated Deep Learning Electrocardiography (ECG) classifier, as a real case study, to investigate the robustness and applicability of the proposed PRO. The experimental results demonstrate that the PRO algorithm outperforms existing meta-heuristic optimization algorithms by providing a more accurate and robust solution. [ABSTRACT FROM AUTHOR]

Despite the inherent stability of Quadratic Approximation based Jaya (JaQA) in solving constrained optimization problems, its effectiveness in exploring the search space is limited. Furthermore, the user-defined penalty parameter utilized to mitigate constraint violation poses another impediment to the efficiency of JaQA. In light of these drawbacks, this paper introduces a new meta-heuristic algorithm, namely Self-adaptive Multi-population Quadratic Approximation guided Jaya (SMP-JaQA). The three additional key features of the SMP-JaQA over JaQA are (i) the inclusion of a multi-population technique to ensure sufficient diversity in the search process (to address the exploration), (ii) a self-adaptation strategy to make it more user-friendly for complex problems in various domains (to address the exploration further), and (iii) a dynamic penalty factor (to automate the penalty parameter). These novel changes help strike a balance between exploration and exploitation too by identifying potential areas in the search space and reduces the likelihood of getting stuck in a local optima. The validity of SMP-JaQA is tested over 18 and 28 benchmark functions of varying dimensions from the CEC-2010 and CEC-2017 test suites, respectively. In order to justify the wide applicability of the proposed algorithm, a set of four real-life constrained problems (includes two mechanical design problems, an electronics design problem and an agricultural problem) are being solved through SMP-JaQA. A thorough analysis of computational outcomes, exploration–exploitation phases, and scalability are presented to shed light on the efficacy and stability levels of SMP-JaQA. The results are compared with those of other state-of-the-art meta-heuristics using Friedman's and Wilcoxon Signed-Rank tests. The outcomes indicate that SMP-JaQA produces reasonable solutions in terms of global optimality and solution quality. [ABSTRACT FROM AUTHOR]

In this paper, we study the personnel task rescheduling problem with time preemption. We assume a baseline task schedule has been composed some time in advance that has been rendered infeasible due to operational uncertainty. When restoring the schedule feasibility, the personnel planner takes several considerations into account encompassing the provided service level, resource stability, and task adherence. The latter stems from the possibility that in the problem under study tasks can be interrupted and resumed later, better known as time preemption. We propose a genetic algorithm complemented with local search techniques to compose an operational schedule in an effective manner. The genetic algorithm operates on a dedicated decoding mechanism to compose high-quality duties for individual workers efficiently. Computational experiments are conducted to benchmark the performance of the proposed procedure with other exact and (meta-)heuristic solution procedures, and to demonstrate the value of the algorithm design choices. Furthermore, the impact of preempting tasks in time is assessed by changing the objective function structure and the proportion of non-preemptive tasks. • Introduction of time preemption as recovery action for personnel task rescheduling. • Dedicated genetic algorithm combined with local search techniques. • Effective encoding–decoding mechanism to compose individual work duties efficiently. • Time preemption and associated retardation of tasks facilitates schedule recovery. • Proposed method and components outperform alternative exact and heuristic methods. [ABSTRACT FROM AUTHOR]

Following the widespread interest of both the scientific community and companies in using autonomous vehicles to perform deliveries, we propose the 'Drone-Assisted Vehicle Routing Problem with Robot Stations' (VRPD-RS), a problem that combines two concepts studied in the autonomous vehicles literature: truck-drone tandems and robot stations. We model the VRPD-RS as a mixed-integer linear program (MILP) for two different objectives, the makespan and operational costs, and analyze the impact of adding trucks, drones, and robots to the delivery fleet. Given the computational complexity of the problem, we propose a General Variable Neighborhood Search (GVNS) metaheuristic to solve more realistic instances within reasonable computational times. Results show that, for small instances of 10 customers, where the solver obtains optimal solutions for almost all cases, the GVNS presents solutions with gaps of 0.7% to the solver for the makespan objective and gaps of 0.0% for the operational costs variant. For instances of up to 50 customers, the GVNS presents improvements of 21.5% for the makespan objective and 8.0% for the operational costs variant. Furthermore, we compare the GVNS with a Simulated Annealing (SA) metaheuristic, showing that the GVNS outperforms the SA for the whole set of instances and in more efficient computational times. Accordingly, the results highlight that including an additional drone in a truck-drone tandem increases delivery speed alongside a reduction in operational costs. Moreover, robot stations proved to be a useful delivery element as they were activated in almost every studied scenario. • This paper studies the 'Drone-Assisted Vehicle Routing Problem with Robot Stations' (VRPD-RS). • We model the VRPD-RS as a mixed-integer linear program. • We develop a General Variable Neighborhood Search (GVNS) algorithm to solve large instances. • The GVNS finds optimal solutions for small instances and beats the solver for large ones. • Our results indicate delivery service and efficiency gains provided by drones and robots. [ABSTRACT FROM AUTHOR]

Image compression has become an indispensable part of the digital world, reducing the costs of storing and transmitting high-quality images. Vector quantization method, which stands out among image compression methods, facilitates the storage and transmission of images without compromising image quality in the process of reducing their size. It uses a codebook to represent similar patterns in a set of images in this method. If the optimum codebook design is achieved, the loss in image quality is minimized by increasing the data compression ratio. It is efficient to use nature-inspired metaheuristic algorithms in the optimization of complex and multidimensional problems such as optimum codebook generation in the image compression process. In this paper, Adolescent Identity Search Algorithm (AISA), which simulates the identity search process of adolescents, is designed for the first time of optimum codebook generation in the block-based image compression process. In addition, improved AISA with various modifications has been proposed to avoid the capture of the conventional AISA by local minimums and to customize the search space. The proposed improved AISA is compared with various well-known metaheuristic algorithms in the literature, and the visual and numerical results are evaluated on various metrics. Based on the simulation results, it has been shown that the proposed IAISA is more efficient than the other metaheuristic algorithms for block-based image compression. [ABSTRACT FROM AUTHOR]

With the progress of science and technology, optimization problems have become complex. Meta-heuristic algorithms have the advantages of high efficiency and strong global search ability in solving optimization problems, so more and more meta-heuristic algorithms are proposed and investigated deeply, and how to improve the universality of an algorithm is an important issue. In this paper, propose an optimization algorithm that simulates human memory behaviour, fully known as human memory optimization Algorithm, abbreviated as HMO, which simulates the way humans behave in production, stores human preferences for success and failure, simulates the way humans behave in their memory, gradually moving towards better directions and outcomes to find a reasonable optimal solution. The results were compared with other meta-heuristic algorithms in the CEC 2013 test set and showed that HMO has better optimization capabilities, and the feasibility of the algorithm was verified from convergence analysis and parametric analysis experiments. In three engineering optimization problems, HMO was able to find optimal solutions within a reasonable range of parameters, verifying the practicality of HMO. [ABSTRACT FROM AUTHOR]

This paper addresses integrated production and distribution scheduling problem in which orders/jobs are undergone a single operation on any one of the identical machines in parallel and upon the completion of the production they are distributed to destined customers by limited number of vehicles. Customers located in dispersed regions place their orders with predetermined demand size and time windows. On production side, machines operate under discrete speed modes, low of which requires less energy cost or vice versa. On distribution side, energy consumed by a vehicle varies depending on the size of load on it. Therefore, objective is minimizing the sum of the weighted cost emanating from early and tardy deliveries plus production and distribution costs. Operational decisions for (i) production are to determine the allocation of jobs to the machines and sequence of jobs on any machine as well as speed mode of each machine for a particular job. As for (ii) distribution: We need to decide vehicle assignment to specific subset of consolidated jobs and the sequence of customer visitation for each vehicle. We develop a formulation for the problem at hand involving parallel machine scheduling and vehicle routing to obtain solutions to optimality. Not surprisingly, however, CPLEX only provides optimum solution for all instances with customer number up to and 6, for which reason we present two metaheuristics, namely Memetic Algorithm (MA) and Iterated Local Search (ILS) for practical sized instances. Computational results indicate that ILS yields better solutions in shorter times as compared to its counterpart. [ABSTRACT FROM AUTHOR]