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This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience. Approach. To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning. Main results. To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs. Significance. The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure. [ABSTRACT FROM AUTHOR]

In recent years, Sine Cosine Algorithm (SCA) is a kind of meta-heuristic optimization algorithm with simple structure, simple parameters and trigonometric function principle. It has been proved that it has good competitiveness among the existing optimization algorithms. However, the single mechanism of SCA leads to its insufficient utilization of the information of the whole population, insufficient ability to jump out of local optima and poor performance at solving complex objective function. Therefore, this paper introduces social learning strategy (SL) and elite opposition-based learning (EOBL) strategy to improve SCA, and proposes novel algorithm: enhancing Sine Cosine Algorithm based on elite opposition-based learning and social learning (ESLSCA). Social learning strategy takes full advantage of information from the entire population. The elite opposition-based learning strategy provides a possibility for the algorithm to jump out of local optima and increases the diversity of the population. To demonstrate the performance of ESLSCA, this paper uses 22 well-known benchmark test functions and CEC2019 test function set to evaluate ESLSCA. The comparisons show that the proposed ESLSCA has better performance than the standard SCA and it is very competitive among other excellent optimization algorithms. [ABSTRACT FROM AUTHOR]

An important issue in interconnected microgrids (MGs) is the realization of balance between the generation side and the demand side. Imbalanced generation and load demands lead to security, power quality, and reliability issues. The load frequency control (LFC) is accountable for regulating MG frequency against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer and inner control loops. The proposed controller is based on a cascaded one plus tilt derivative (1+TD) in the outer loop and an FO tilt integrator-derivative with a filter (FOTIDF) in the inner loop, forming the cascaded (1+TD/FOTIDF) controller. The proposed 1+TD/FOTIDF achieves better disturbance rejection compared with traditional LFC methods. The proposed 1+TD/FOTIDF scheme is optimally designed using a modified version of the liver cancer optimization algorithm (MLCA). In this paper, a new modified liver cancer optimization algorithm (MLCA) is proposed to overcome the shortcomings of the standard Liver cancer optimization algorithm (LCA), which contains the early convergence to local optima and the debility of its exploration process. The proposed MLCA is based on three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional based learning (QOBL), and the fitness distance balance (FDB). The proposed MLCA method simultaneously adjusts and selects the best 1+TD/FOTIDF parameters to achieve the best control performance of MGs. Obtained results are compared to other designed FOTID, TI/FOTID, and TD/FOTID controllers. Moreover, the contribution of electric vehicles and the high penetration of renewables are considered with power system parameter uncertainty to test the stability of the proposed 1+TD/FOTIDF LFC technique. The obtained results under different possible load/generation disturbance scenarios confirm a superior response and improved performance of the proposed 1+TD/FOTIDF and the proposed MLCA-based optimized LFC controller. [ABSTRACT FROM AUTHOR]

In this paper, Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) are applied to solve the power loss lessening problem. Protist Algorithm (PA) is modelled based on the Protist's natural activities. Protist exists in moist places. The leading nutritious phase is Plasmodium, the energetic and vibrant phase of Protist. In this segment, the organic substance in Protist search for food in surroundings and conceals enzymes for digestion. Natural actions of Otocyon megalotis are emulated to design the OOA approach. In the projected OOA searching of regions in exploration, for foodstuff the Otocyon megalotis mark the prey in the space is indicated as a global exploration. Real power loss reduction and Voltage stability enhancement are the key objectives of the paper. To solve the problem, Protist algorithm (PA) and Otocyon megalotis optimization algorithm. In the course of the migration procedure, the anterior end outspreads and interconnected arterial system that authorize cytoplasm to stream inside. Then, mutation and cross-over probability are employed to augment the performance of the Protist algorithm (PA). With this integration engendering of the population is done. Mutation classes the population exploration agents (PN) in uphill order conferring to the agents appropriateness (fitness) cost. Consequently, the technique splits the organized agents into three fragments rendering to their fitness value. In which PN/3 denotes to the population possess pre-eminent (aptness) fitness values, subsequently with second pre-eminent and poorest aptness (fitness) values. Then, in this paper, Otocyon megalotis optimization algorithm (OOA) is applied for solving the Power loss lessening problem. In the subsequent segment, navigate during the haunt to seal prey previous to the hit was replicated as a local search. In exploration, the data obtained is shared to all the associates of the family unit for continued existence and growth. Examination of the nearby terrain is modelled with reference to the fitness of all entities. Most excellent entity has investigated the majority fascinating terrain and it will be shared with family unit of Otocyon megalotis. Primarily, Otocyon megalotis show that it not involved in hunting. Conversely, as soon as moving near to prey Otocyon megalotis will perform the attack in quick mode. This approach imitated and designed in the local search segment. Authenticity of the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) is corroborated in 23 benchmark functions, IEEE 30, 57, 300 and 354 test systems. Power Loss reduction achieved with voltage stability enhancement. Real power loss reduction attained. Both the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) performed well in solving the Power loss reduction problem. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity; Sine Cosine Algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness. [ABSTRACT FROM AUTHOR]

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid algorithm based on CMOGWO-ADNSGA-II to solve the vibration stability problem during the operation of a EMS-type maglev train dynamics model subjected to strong non-linear magnetic buoyancy. The proposed algorithm optimizes the control system parameters of EMS-type maglev train suspensions by combining an improved multi-objective chaotic grey wolf algorithm (CMOGWO) with an improved non-dominated Sorting genetic algorithm-II (ADNSGA-II) to enhance the search capability of the algorithm and ensure population diversity. The efficacy of the algorithm is demonstrated by applying it to the EMS-type maglev train suspension frame control system to find the optimal control parameters. Experimental results show that the system with the optimal parameters applied significantly reduces the suspension gap amplitude and the corresponding standard deviation, as well as the vertical acceleration amplitude and the corresponding standard deviation during operation. The proposed algorithm provides a good solution for EMS-type maglev train suspension vibration control, which can improve its performance and safety. [ABSTRACT FROM AUTHOR]

As a significant branch of production scheduling problem, the Flexibility Job Shop Scheduling Problem (FJSP) is a typical NP-hard problem. Most conventional flexible workshop scheduling primarily focuses on performance aspects involving production efficiency such as time and quality. In recent years, due to increased energy costs and environmental pollution, 'low-carbon scheduling' has garnered attention as a new scheduling paradigm among scholars and engineers. This paper investigates a low-carbon flexible Job shop scheduling problem, proposing a Grey Wolf Optimization algorithm (SC-GWO), aiming to minimize the sum of carbon emission costs and makespan costs. This algorithm employs the Grey Wolf Algorithm (GWO) as the fundamental optimization method, adaptively choosing between global and local searches based on the dispersion degree of individuals. Firstly, integrating the Sine Cosine Algorithm (SCA), the sinusoidal cosine search mechanism is applied to GWO to enhance its local search capability. Secondly, a new leader selection mechanism is introduced to prevent leaders from falling into local optima, thus improving the algorithm's global exploration capability. Utilizing a nonlinear convergence factor strategy controls the global exploration and local exploitation capabilities in different algorithm stages, enhancing optimization accuracy and accelerating convergence, achieving a dynamic balance between the two. Finally, validation of the SC-GWO algorithm's ability to solve low-carbon scheduling problems in flexible job shop scheduling instances is conducted. Experimental results demonstrate the superior performance of SC-GWO in solving low-carbon flexible workshop scheduling instances. Comparative experiments against four other advanced algorithms on 22 classic benchmark test functions confirm SC-GWO's better convergence. Through standard test functions like Bandimarte instances applied to solve FJSP, experimental results showcase the excellent optimization performance of SC-GWO. Compared to HGWO and GWO, the makespan time is reduced by 22.25% and 39.27%, respectively. The proposed SC-GWO algorithm demonstrates favorable solving effects on flexible job shop scheduling instances, meeting actual production scheduling needs. [ABSTRACT FROM AUTHOR]

In this paper, we study a discrete momentum consensus-based optimization (Momentum-CBO) algorithm which corresponds to a second-order generalization of the discrete first-order CBO [S.-Y. Ha, S. Jin and D. Kim, Convergence of a first-order consensus-based global optimization algorithm, Math. Models Methods Appl. Sci. 30 (2020) 2417–2444]. The proposed algorithm can be understood as the modification of ADAM-CBO, replacing the normalization term by unity. For the proposed Momentum-CBO, we provide a sufficient framework which guarantees the convergence of algorithm toward a global minimum of the objective function. Moreover, we present several experimental results showing that Momentum-CBO has an improved success rate of finding the global minimum compared to vanilla-CBO and show the stability of Momentum-CBO under different initialization schemes. We also show that Momentum-CBO can be used as the alternative of ADAM-CBO which does not have a proper convergence analysis. Finally, we give an application of Momentum-CBO for Lyapunov function approximation using symbolic regression techniques. [ABSTRACT FROM AUTHOR]

Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches. [ABSTRACT FROM AUTHOR]

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars' attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The technology for path planning of independent mobile robots is mature, but multi-robot path planning for unified scheduling and allocation is much more complex than single-robot path planning. This requires consideration of collision problems between robots, general optimal path problems, etc. This paper proposes the use of the BSO algorithm for unified scheduling and allocation of multiple robots to improve the efficiency of task execution. The BSO algorithm is a new type of intelligent optimization algorithm that uses clustering ideas to search for local optimal solutions and obtains global optimal solutions by comparing local optimal solutions. It also uses mutation ideas to increase the diversity of the algorithm and avoid becoming trapped in local optimal solutions. Using the GA/SA algorithm and the proposed BSO algorithm for computer simulation comparison, we obtained the optimal path planning for the three robots under unified scheduling. The total distance of the optimal path obtained by the BSO algorithm was 27.36% and 25.31% shorter than those of the GA and SA algorithms, respectively. To further test the performance of the BSO algorithm, we conducted additional experiments on the unified scheduling of multiple robots. The experimental results show that the proposed BSO algorithm can significantly improve the efficiency. The multi-robot under unified scheduling performs point-to-point path planning without collisions, and they can traverse all task target points in the shortest path without repetition. This algorithm is suitable for multi-robot tasks in large-scale environments. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm's ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization algorithm for backpropagation (BP) neural networks by fusing integer-order differentiation and fractional-order differentiation, while fractional-order differentiation has significant advantages in describing complex phenomena with long-term memory effects and nonlocality, its application in neural networks is often limited by a lack of physical interpretability and inconsistencies with traditional models. To address these challenges, we propose a mixed integer-fractional (MIF) gradient descent algorithm for the training of neural networks. Furthermore, a detailed convergence analysis of the proposed algorithm is provided. Finally, numerical experiments illustrate that the new gradient descent algorithm not only speeds up the convergence of the BP neural networks but also increases their classification accuracy. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

As a classical combinatorial optimization problem, the traveling salesman problem (TSP) has been extensively investigated in the fields of Artificial Intelligence and Operations Research. Due to being NP-complete, it is still rather challenging to solve both effectively and efficiently. Because of its high theoretical significance and wide practical applications, great effort has been undertaken to solve it from the point of view of intelligent search. In this paper, we propose a two-stage probe-based search optimization algorithm for solving both symmetric and asymmetric TSPs through the stages of route development and a self-escape mechanism. Specifically, in the first stage, a reasonable proportion threshold filter of potential basis probes or partial routes is set up at each step during the complete route development process. In this way, the poor basis probes with longer routes are filtered out automatically. Moreover, four local augmentation operators are further employed to improve these potential basis probes at each step. In the second stage, a self-escape mechanism or operation is further implemented on the obtained complete routes to prevent the probe-based search from being trapped in a locally optimal solution. The experimental results on a collection of benchmark TSP datasets demonstrate that our proposed algorithm is more effective than other state-of-the-art optimization algorithms. In fact, it achieves the best-known TSP benchmark solutions in many datasets, while, in certain cases, it even generates solutions that are better than the best-known TSP benchmark solutions. [ABSTRACT FROM AUTHOR]

Two key challenges in optimal control include efficiently solving high-dimensional problems and handling optimal control problems with state-dependent running costs. In this paper, we consider a class of optimal control problems whose running costs consist of a quadratic on the control variable and a convex, non-negative, piecewise affine function on the state variable. We provide the analytical solution for this class of optimal control problems as well as a Hopf-type representation formula for the corresponding Hamilton-Jacobi partial differential equations. Finally, we propose efficient numerical algorithms based on our Hopf-type representation formula, convex optimization algorithms, and min-plus techniques. We present several high-dimensional numerical examples, which demonstrate that our algorithms overcome the curse of dimensionality. We also describe a field-programmable gate array (FPGA) implementation of our numerical solver whose latency scales linearly in the spatial dimension and that achieves approximately a 40 times speedup compared to a parallelized central processing unit (CPU) implementation. Thus, our numerical results demonstrate the promising performance boosts that FPGAs are able to achieve over CPUs. As such, our proposed methods have the potential to serve as a building block for solving more complicated high-dimensional optimal control problems in real-time. [ABSTRACT FROM AUTHOR]

Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal–noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ0-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy–Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ0-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

Swarm intelligence optimization algorithm has been proved to perform well in the field of parameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate swarm algorithm (IMATSA) based on tunicate swarm algorithm (TSA). IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of local optimal position, and balancing global and local search. Firstly, IMATSA adopts Tent map and quadratic interpolation to initialize population and enhance the diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the convergence of local search. Thirdly, in the process of global development, IMATSA adopts Levy flight and the improved Gauss disturbance method to adaptively improves and coordinates the ability of global development and local search. Then, this paper verifies the performance of IMATSA based on 14 benchmark functions experiment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metrics are convergence speed, convergence value, significance level P-value, Peak Signal-to-Noise Ratio (PSNR) and Standard Deviation (STD). Experimental results show that IMATSA performs better in three kinds of benchmark functions; each component of IMATSA has a positive effect on the performance; IMATSA performs better in parameter optimization experiments of SVM experiment and GBDT; there is significant difference between IMATSA and other algorithms by Wilcoxon signed rank test; in image segmentation, the performance is directly proportional to the number of thresholds, and compared with other algorithms, IMATSA has better comprehensive performance. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication networks and Internet of Things technology (IoT), higher requirements have been put forward for spectrum resource utilization and system performance. In order to further improve the utilization of spectrum resources and system performance, this paper proposes an intelligent reflecting surface (IRS)-assisted fair energy allocation algorithm for cognitive multiple-input single-output (MISO) wireless-powered networks. The goal of this paper is to maximize the minimum energy receiving power in the energy receiver, which is constrained by the signal-to-interference-plus-noise ratio (SINR) threshold of the information receiver in the secondary network, the maximum transmission power at the cognitive base station (CBS), and the interference power threshold of the secondary network on the main network. Due to the coupling between variables, this paper uses iterative optimization algorithms to optimize and solve different variables. That is, when solving the active beamforming variables, the passive beamforming variables are fixed; then, the obtained active beamforming variables are fixed, and the passive beamforming variables are solved. Through continuous iterative optimization, the system converges. The simulation results have verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Conventional wireless sensors have difficulty solving the problem of energy limitation, especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem that it is difficult to charge wireless sensors in the field using conventional energy sources, the energy harvesting wireless senor is designed to use renewable energy sources for power supply. Considering the uncertainty and unknown nature of renewable energy generation, and the need for effective energy management of the sensor. In this paper, an Node Power Control Optimization (NPCO) power allocation algorithm is proposed to adjust the power allocation problem of wireless sensor nodes within each time slot. In addition, to address the unknown and random nature of energy arrival, this paper proposes a CLSTM model based on deep learning to predict the energy arrival. The continuous autonomous energy management of wireless sensor nodes is achieved by combining the CLSTM prediction results using the NPCO algorithm. The algorithm is applicable to continuous states and is able to show good performance in the verification of real solar data. The algorithm achieves better performance in terms of long-term average net bit rate compared to the current DDPG algorithm, AC algorithm, and Lyapunov optimization algorithm. [ABSTRACT FROM AUTHOR]

Geometric numerical integration has recently been exploited to design symplectic accelerated optimization algorithms by simulating the Bregman Lagrangian and Hamiltonian systems from the variational framework introduced by Wibisono et al. In this paper, we discuss practical considerations which can significantly boost the computational performance of these optimization algorithms and considerably simplify the tuning process. In particular, we investigate how momentum restarting schemes ameliorate computational efficiency and robustness by reducing the undesirable effect of oscillations and ease the tuning process by making time-adaptivity superfluous. We also discuss how temporal looping helps avoiding instability issues caused by numerical precision, without harming the computational efficiency of the algorithms. Finally, we compare the efficiency and robustness of different geometric integration techniques and study the effects of the different parameters in the algorithms to inform and simplify tuning in practice. From this paper emerge symplectic accelerated optimization algorithms whose computational efficiency, stability and robustness have been improved, and which are now much simpler to use and tune for practical applications. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect. [ABSTRACT FROM AUTHOR]

As digital communication and storage continue to expand, the protection of image privacy information becomes increasingly critical. To safeguard sensitive visual information from unauthorized access, this paper proposes a novel image encryption scheme that integrates multiobjective Artificial Bee Colony (ABC) optimization algorithm and DNA coding. Multiple evaluation metrics including correlation relationship, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and information entropy are collaboratively optimized by the ABC algorithm. The proposed method begins with the application of the SHA-256 algorithm to generate keys and random sequences using chaotic systems. These sequences are then employed for shuffling, DNA coding, decoding, and diffusion, generating initial encrypted images. Subsequently, the encrypted images serve as individuals within the ABC algorithm to determine optimal parameters of the chaotic systems and the best ciphertext image. Simulation experiments demonstrate that the ciphertext images achieved excellent results in information entropy, pixel correlation coefficient, NPCR, and UACI. The integration of the multiobjective ABC optimization algorithm with DNA coding in our proposed image encryption scheme results in heightened security, as evidenced by superior performance in various metrics. [ABSTRACT FROM AUTHOR]

We propose an adaptive fractional multi-scale optimization optical flow algorithm, which for the first time improves the over-smoothing of optical flow estimation under the total variation model from the perspective of global feature and local texture balance, and solves the problem that the convergence of fractional optical flow algorithms depends on the order parameter. Specifically, a fractional-order discrete L1-regularization Total Variational Optical Flow model is constructed. On this basis, the Ant Lion algorithm is innovatively used to realize the iterative calculation of the optical flow equation, and the fractional order is dynamically adjusted to obtain an adaptive optimization algorithm with strong search accuracy and high efficiency. In this paper, the flexibility of optical flow estimation in weak gradient texture scenes is increased, and the optical flow extraction rate of target features at multiple scales is greatly improved. We show excellent recognition performance and stability under the MPI_Sintel and Middlebury benchmarks. [ABSTRACT FROM AUTHOR]

In response to the low efficiency and poor quality of current seed optimization algorithms for multi-threshold image segmentation, this paper proposes the utilization of the normal distribution in the cluster distribution mathematical model, the Levy flight mechanism, and the differential evolution algorithm to address the deficiencies of the seed optimization algorithm. The main innovation lies in applying the BBO algorithm to image multi threshold segmentation, providing a new perspective and method for image segmentation tasks. The second significant progress is the combination of Levy flight dynamics and differential evolution algorithm (DEA) to improve the BBO algorithm, thereby enhancing its performance and image segmentation quality. Therefore, a multi-threshold image segmentation model based on the optimized seed optimization algorithm is developed. The experimental results showed that on the function f1, the iteration of the improved seed optimization algorithm was 53, the Generational Distance value was 0.0020, the Inverted Generational Distance value was 0.098, and the Spacing value was 0.051. Compared with the other two algorithms, the improved seed optimization algorithm has better image segmentation performance and clearer image segmentation details. In summary, compared with existing multi-threshold image segmentation methods, the proposed multi-threshold image segmentation model based on the improved seed optimization algorithm has a better image segmentation effect and higher efficiency, can significantly improve the quality of image segmentation, has positive significance for the development of image processing technology, and also provides references for the improvement and application of optimization algorithms. [ABSTRACT FROM AUTHOR]

In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology. [ABSTRACT FROM AUTHOR]

Fog Computing paradigm that provisions low-latency computing services at the edge network, is a bonanza for supply chain computing resources in Internet of Things (IoT) applications. In different scenarios such as smart homes/healthcare systems, multiple IoT applications are distributed simultaneously in cloud and fog nodes to provide different IoT-based services. In addition, each program requires resources and has its desired quality of service (QoS) which should be met. One of the key challenges in fog computing environment is how to efficiently allocate resources to IoT applications because inefficient resource allocation leads to burdening providers high costs and it lowers down the delivered QoS to users. Since the majority of IoT applications are time-sensitive, the low delay and near physically allocated resources improve the amount of delivered QoS. Therefore, the resource clustering algorithms with the lowest distance error rate and the lowest delay as a consequence are favorable. The aim is to reduce clustering errors and improve the overall performance of the system. This paper formulates resource allocation to IoT applications in heterogeneous 4-layered fog platforms to an optimization problem. To solve this problem, a fusion approach incorporating a genetic algorithm (GA) and the k-means clustering approach is presented. Firstly, it utilizes the k-means approach and Jaccard measurement to cluster fog nodes with a minimum clustering rate. Then, the resources of fog clusters are allocated to IoT devices with the minimum error rate by incorporating GA algorithm. This selection of processing nodes in a fog layer helps to minimize latency and allows applications to access resources simultaneously. The simulation results in extensive scenarios prove the superiority of the proposed algorithm against other successful meta-heuristic approaches in terms of the objective function and lowest error/delay rate. [ABSTRACT FROM AUTHOR]

Aquila Optimization (AO) is a recently proposed meta-heuristic algorithm, which has been proved to be more competitive than other meta-heuristic algorithms in function optimization and practical applications. However, when solving more complex optimization problems, AO still has the shortcomings of local optimal stagnation and low solving accuracy. To overcome these shortcomings, an improved Aquila Optimization algorithm (IAO) is proposed in this paper. During the initialization of IAO population, a hybrid chaotic mapping mechanism was introduced to initialize the population, improving both the population diversity and the uniformity of the population distribution. The elite dimensional lens imaging learning strategy is introduced for elite individual to improve the optimization quality of the algorithm as elite individual has more useful information than ordinary individuals. Then the probabilistic jump mechanism of simulated annealing algorithm is used to select the position update mode to balance local development and global search. The experimental results on the CEC2005 test function verify the viability and effectiveness of IAO. IAO is used to the multi-threshold segmentation problem based on symmetric cross entropy to demonstrate its capacity to resolve practical optimization problems. The segmentation performance on different reference images shows that IAO has good segmentation performance in most cases. [ABSTRACT FROM AUTHOR]

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs' performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms. [ABSTRACT FROM AUTHOR]

Arithmetic Optimization Algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed Algorithm (TSA), and Gray Wolf Optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method. [ABSTRACT FROM AUTHOR]

Quantum approximate optimization algorithms are hybrid quantum-classical variational algorithms designed to approximately solve combinatorial optimization problems such as the MAX-CUT problem. In spite of its potential for near-term quantum applications, it has been known that quantum approximate optimization algorithms have limitations for certain instances to solve the MAX-CUT problem, at any constant level p. Recently, the recursive quantum approximate optimization algorithm, which is a non-local version of quantum approximate optimization algorithm, has been proposed to overcome these limitations. However, it has been shown by mostly numerical evidences that the recursive quantum approximate optimization algorithm outperforms the original quantum approximate optimization algorithm for specific instances. In this paper, we analytically prove that the recursive quantum approximate optimization algorithm is more competitive than the original one to solve the MAX-CUT problem for complete graphs with respect to the approximation ratio. [ABSTRACT FROM AUTHOR]

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving. [ABSTRACT FROM AUTHOR]

Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks' completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed. [ABSTRACT FROM AUTHOR]

To solve the shortcomings of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fractional order moth–flame optimization with the Otsu image segmentation algorithm. Utilizing the advantages of memorability and heritability in fractional order differentiation, the position updating of moths is controlled by fractional order. Using the adaptive fractional order, the positions of moths are used to adjust the fractional order adaptively to improve the convergence speed. Combining the improved MFO algorithm with the two-dimensional Otsu algorithm, the optimization objective function is achieved by using its dispersion matrix. The experimental results indicate that, compared with traditional MFO, the convergence rate of the proposed algorithm is improved by about 74.62%. Furthermore, it has better segmentation accuracy and a higher fitness value than traditional MFO. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

The Honey Badger Algorithm (HBA) represents a novel swarm intelligence optimization algorithm introduced in recent years. However, its predominant constraints are linked to inadequate convergence accuracy and a vulnerability to entrapment in local optima. In an effort to mitigate these challenges, this paper introduces an Improved Honey Badger Algorithm Based on a Hybrid Strategy (OHBA). Firstly, during the population initialization phase, a method involving the utilization of a good point set is introduced to enhance the diversity and introduce more randomness into the population. Secondly, in the position update phase, the Beta distribution is employed as an alternative to the Uniform distribution, aiming to strike a balance between global exploration and local exploitation capabilities. Thirdly, an improved adaptive density factor strategy is incorporated into both global and local position updates to enhance the algorithm's convergence precision and speed. Lastly, within the global exploration stage, a Cauchy mutation strategy based on the Sine chaotic mapping is introduced to facilitate the algorithm in overcoming local optima and reinforcing its optimization capabilities. The improved algorithm's performance has been evaluated through a comprehensive set of assessments, including CEC-2017 functions, CEC-2022 functions, Wilcoxon rank-sum tests, and practical engineering optimization problems. These evaluations were undertaken to assess the algorithm in comparison to classical intelligent optimization algorithms. The experimental results show that OHBA possesses significant advantages in terms of convergence speed, optimization accuracy, robustness and its practical utility and effectiveness in addressing complex optimization challenges. This establishes OHBA as a highly competitive option in these critical aspects of optimization. [ABSTRACT FROM AUTHOR]

The moth-flame algorithm shows some shortcomings in solving the complex problem of optimization, such as insufficient population diversity and unbalanced search ability. In this paper, an IMFO (Improved Moth-Flame Optimization) algorithm is proposed to be applied in solving the optimization problem of function. First, cat chaotic mapping is used to generate the initial position of moth to improve the population diversity. Second, cosine inertia weight is introduced to balance the global and local search abilities of the algorithm. Third, the memory information in the particle swarm algorithm is introduced into the iterative process of the algorithm to speed up the convergence of the population. Finally, Gaussian mutation strategy is used in the current optimal solution to avoid the algorithm from falling into the local optimum. Simulation experiments are conducted on 11 benchmark test functions, compared with other improved MFO (Moth-Flame Optimization) algorithms and classical optimization algorithms. The results show that the IMFO has higher accuracy and stability in solving the above-mentioned test functions. The proposed algorithm is experimented and verified by optimizing the KELM (Kernel Extreme Learning Machine) in an engineering example and exhibits a better optimization performance. [ABSTRACT FROM AUTHOR]