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As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields. [ABSTRACT FROM AUTHOR]

The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called "structures", meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity. [ABSTRACT FROM AUTHOR]

Unconventional events exacerbate the imbalance between regional transportation demand and limited road network resources. Scientific and efficient path planning serves as the foundation for rapidly restoring equilibrium to the road network. In real large-scale road networks, especially during emergencies, it is usually difficult to obtain or predict accurate dynamic traffic network flows in real-time, which is used to support equilibrium path planning. Moreover, the traditional iterative methods cannot meet the real-time demand of emergency equilibrium path planning decision generation. To solve the above problems, this paper proposes a hybrid control architecture for path planning based on equilibrium traffic assignment theory. The architecture introduces the travelers' real-time travel data and constructs a spatio-temporal neural network, which captures the evolution of traffic network loads. Adaptive multi-graph fusion technology is used to mix the background traffic flow data and the traveler's real-time Origin–Destination (OD) data, to mine the dynamic correlation between the traffic state and the travelers' travel. Based on the real-time prediction of dynamic network states, equilibrium mapping learning is carried out to pre-allocate potential travel demands and construct equilibrium traffic graphs based on system optimization traffic assignment. Finally, individual evacuation path strategies are generated online in a data-driven manner in real time to achieve improved resilience in the transportation system. [ABSTRACT FROM AUTHOR]

With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations. [ABSTRACT FROM AUTHOR]

Aiming at the air defense task requirements for an enemy's large-scale aircraft attack, this paper presents a plan generation algorithm which can quickly give an interception scheme. The main contribution of this paper is the modification of the standard A* algorithm and its combination of the optimization algorithm and air-defence mission. Firstly, the enemy's attack weapon and our defense platform are modeled, and kinetic equations and interception efficiency functions are constructed, and the intercepted criterions are established. Then, the interception-cost mixed optimal function is established to clarify the system optimization objective. Secondly, aiming at the characteristics of strong time sensitivity of air defense interception, a modified A* optimization algorithm with fast convergence characteristics is used to solve the optimization problems, the standard A* algorithm is modified and the optimal air defense interception plan under the condition of mixed performance index is given. Finally, the proposed method is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

In the field of many-objective evolutionary optimization algorithms (MaOEAs), how to maintain the balance between convergence and diversity has been a significant research problem. With the increase of the number of objectives, the number of mutually nondominated solutions increases rapidly, and multi-objective evolutionary optimization algorithms, based on Pareto-dominated relations, become invalid because of the loss of selection pressure in environmental selection. In order to solve this problem, indicator-based many-objective evolutionary algorithms have been proposed; however, they are not good enough at maintaining diversity. Decomposition-based methods have achieved promising performance in keeping diversity. In this paper, we propose a MaOEA based on indicator and decomposition (IDEA) to keep the convergence and diversity simultaneously. Moreover, decomposition-based algorithms do not work well on irregular PFs. To tackle this problem, this paper develops a reference-points adjustment method based on the learning population. Experimental studies of several well-known benchmark problems show that IDEA is very effective compared to ten state-of-the-art many-objective algorithms. [ABSTRACT FROM AUTHOR]

Recently, a new swarm intelligence optimization algorithm called the remora optimization algorithm (ROA) was proposed. ROA simulates the remora's behavior of the adsorption host and uses some formulas of the sailfish optimization (SFO) algorithm and whale optimization algorithm (WOA) to update the solutions. However, the performance of ROA is still unsatisfactory. When solving complex problems, ROA's convergence ability requires further improvement. Moreover, it is easy to fall into local optimization. Since the remora depends on the host to obtain food and optimize ROA performance, this paper introduces the mutualistic strategy to strengthen the symbiotic relationship between the remora and the host. Meanwhile, chaotic tent mapping and roulette wheel selection are added to further improve the algorithm's performance. By incorporating the above improvements, this paper proposes an improved remora optimization algorithm with a mutualistic strategy (IROA) and uses 23 benchmark functions in different dimensions and CEC2020 functions to validate the performance of the proposed IROA. Experimental studies on six classical engineering problems demonstrate that the proposed IROA has excellent advantages in solving practical optimization problems. [ABSTRACT FROM AUTHOR]

Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process ("on fly") from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC'2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals. [ABSTRACT FROM AUTHOR]

Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey's position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems. [ABSTRACT FROM AUTHOR]

Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, while the ϵ -dominance relation was used to update the archive population solutions. ϵ -dominance helps provide a good boost to diversity, coverage, and convergence overall. The validation of the proposed MOGSK was conducted using five biobjective (ZDT) and seven three-objective test functions (DTLZ) problems, along with the recently introduced CEC 2021, with fifty-five test problems in total, including power electronics, process design and synthesis, mechanical design, chemical engineering, and power system optimization. The proposed MOGSK was compared with seven existing optimization algorithms, including MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, and GrEA. The experimental findings show the good behavior of our proposed MOGSK against the comparative algorithms in particular real-world optimization problems. [ABSTRACT FROM AUTHOR]

This paper describes a computational procedure to establish the optimal distribution of network reconfiguration by means of a novel gray wolf optimization (GWO) algorithm. The procedure aimed to diminish the system's power loss and produce a better voltage profile while fulfilling the operating constraints described by different operating conditions. Under practical restrictions, the distribution network reconfiguration (DNR) problem is classified as multimodal and highly nonlinear. Constraint breaches were appropriately handled to produce stable convergence characteristics, and high-quality solutions were obtained in a shorter execution time. The 33-bus and 69-bus systems were used to obtain the optimal reconfiguration by incorporating the method developed in this work. The simulation results obtained were collated and compared with the outcomes of other well-known optimization techniques, confirming the efficacy of the GWO algorithm in solving the DNR problem. [ABSTRACT FROM AUTHOR]

The echoes collected by wideband radar systems provide abundant information on target scatterers, which is beneficial to target detection, classification, and recognition. However, as the radar range resolution increases, range cell migration (RCM) during the coherent integration (CI) period happens much easier, which may cause a degradation of target detection probability. In addition, due to the target's orientation and structure relative to the radar, the distribution characteristics of the target scatterers in high-resolution range profiles (HRRPs) and the detection window length may vary from pulse to pulse, which may reduce the performance of traditional energy integration (EI) detectors. To solve those problems, moving range-spread target (RST) detection combining the modified keystone transform (MKT) and improved EI (IEI) is proposed in this paper. Firstly, based on waveform entropy minimization, MKT using hunter–prey optimization (HPO) is introduced to reduce the CI gain loss. The target Doppler ambiguity factor is estimated using such an effective optimization technique. Then, the IEI detector optimized by the adaptive threshold and detection window is utilized to achieve target detection, which minimizes the sensitivity of the traditional EI detector to the detection window length. The proposed method significantly improves the performance of moving RSTs in sea clutter without prior knowledge of the target Doppler ambiguity factor. Experiments are conducted by comparing the proposed method with other competing methods on both simulation data and real sea clutter data. The results demonstrate that the proposed method can obtain the CI more efficiently and has a higher detection probability. [ABSTRACT FROM AUTHOR]

With the aim of solving the problems arising from the low efficiency and low accuracy of fault classification of wind power towers and turbine equipment (referred to as wind power systems for short) using artificial data analysis, this paper takes the operational data for wind power systems as the research object and proposes an improved K-means weighted dynamic clustering fault classification algorithm (DT clustering). First, historical and asymmetrical operational data from wind power systems were pre-processed to construct the data time series matrix and establish the fault classification model; second, the linear approximate constrained optimization algorithm and multiple regression algorithm were combined to build the model parameter optimization model. Finally, the comparative analysis of various algorithms showed the superiority of this algorithm, and the effectiveness of this model should be proved in practical applications. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements. [ABSTRACT FROM AUTHOR]

Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants of the original PSO over the past few years, including the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. In this paper, the performance of PSO is improved by proposing a fragmented swarm optimisation approach known as the PSOα. The PSOα is tested and compared with PSOs over 14 different benchmarking cost functions to validate its efficacy. The analysis is also carried out to see the impact of α on its performance. It is observed that the average value of the cost function over 50 simulations obtained using the fragmented swarm approach is lower than that obtained using the standard PSO in 12 out of 14 benchmark functions. Similarly, the fragmented approach outperforms the standard PSO in 13 out of 14 benchmark functions when compared with the best fitness value achieved out of 50 simulations. Finally, the proposed approach is applied to solve the well-known real-life optimisation problem of load frequency control (LFC) in power systems. A test system comprising both renewable and traditional power sources is considered to evaluate the efficacy of the proposed technique. A fractional order proportional-integral-differential (FOPID) controller is used, whose parameters are optimised using the proposed PSO for achieving the LFC. The proposed fragmentation approach can be applied with other optimisation techniques to improve their performance. [ABSTRACT FROM AUTHOR]

Dynamic weapon target assignment (DWTA) is an effective method to solve the multi-stage battlefield fire optimization problem, which can reflect the actual combat scenario better than static weapon target assignment (SWTA). In this paper, a meaningful and effective DWTA model is established, which contains two practical and conflicting objectives, namely, maximizing combat benefits and minimizing weapon costs. Moreover, the model contains limited resource constraints, feasibility constraints and fire transfer constraints. The existence of multi-objective and multi-constraint makes DWTA more complicated. To solve this problem, an improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed in this paper. Various learning strategies are adopted for the dominated and non-dominated solutions of the algorithm, so that the algorithm can learn and evolve in a targeted manner. In order to solve the problem that the algorithm is easy to fall into local optimum, this paper proposes a search strategy based on simulated binary crossover (SBX) and polynomial mutation (PM), which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. In addition, a dynamic archive maintenance strategy is applied to improve the diversity of non-dominated solutions. Finally, this algorithm is compared with three state-of-the-art multiobjective optimization algorithms, including solving benchmark functions and DWTA model in this article. Experimental results show that IMOPSO has better convergence and distribution than the other three multiobjective optimization algorithms. IMOPSO has obvious advantages in solving multiobjective DWTA problems. [ABSTRACT FROM AUTHOR]

The energy consumption management of small base stations (SBSs) in wireless caching networks with dense deployment of SBSs is an urgent problem to be solved. This paper jointly optimizes the SBS state control and user association problems in caching networks to reduce network energy consumption while taking into account the average service latency of the network to ensure user experience. First, a new definition of three-state SBSs in caching networks is proposed based on their ability to keep content cache updated. Then, a relaxed threshold setting method is designed and the SBS traffic prediction is used to obtain the initial state information of SBSs in the next period. In order to eliminate the impact on the accessing users when the switched-off base station (BS) wakes up, a SBS state asynchronous switching mechanism is proposed to ensure that the users who switch to the waking SBS can carry out communication services normally, and a user association strategy is constructed with the SBS load as the optimization target. Finally, a joint optimization model of user association and SBS state control (SSC-UA) is constructed to admit and correct the initial state of SBSs to maximize the system gain and obtain the final state strategy for each SBS in the next period. The simulation results show that the proposed SSC-UA algorithm can effectively improve the energy efficiency and reduce the network service delay at the same time. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization algorithm combined with an illustration method to select the three best glass materials for the design of a thickened triplet lens and a correction of the paraxial chromatic aberrations. In the thin lens thickening process, chromatic aberration arises from the deviation between the real and paraxial chromatic aberrations. To solve this problem, we propose an optimization algorithm and illustration method, which are integrated into a thickened triplet lens design. We optimize the eight thickened triplet lens groups to obtain the longitudinal chromatic aberration curve for the best group in the visible light range from 0.4861 μm to 0.6563 μm. The chromatic aberration curve area is 3.33 × 10−7 (μm × mm) for the longitudinal chromatic aberration and 2.11 × 10−5 (μm × mm) for the lateral chromatic aberration. Finally, the chromatic aberration of the thicken triplet lens is close to that of the thin triplet lens design, and the proposed method can obtain a good optical performance. [ABSTRACT FROM AUTHOR]

Concentric circular antenna arrays (CCAAs) can obtain better performance than other antenna arrays. However, high overhead and excessive sidelobes still make its application difficult. In this paper, we consider the sparse synthesis optimization of CCAAs. Specifically, we aim to turn off a specific number of antennas while reducing the sidelobe of CCAAs. First, we formulate an optimization problem and present the solution space. Then, we propose a novel evolutionary method for solving the optimization problem. Our proposed method introduces hybrid solution initialization, hybrid crossover method, and hybrid update methods. Simulation results show the effectiveness of the proposed algorithm and the proposed improvement factors. [ABSTRACT FROM AUTHOR]

Sparse arrays with low cost and engineering complexity are widely applied in many fields. However, the high peak sidelobe level (PSLL) of a sparse array causes the degradation of weak target detection performance. Particularly for the large size of underwater low-frequency sensors, the design problem requires a minimum spacing constraint, which further increases the difficulty of PSLL suppression. In this paper, a novel swarm-intelligence-based approach for sparse sensor array design is proposed to reduce PSLL under spacing constrains. First, a global enhancement whale optimization algorithm (GEWOA) is introduced to improve the global search capability for optimal arrays. A three-step enhanced strategy is used to enhance the ergodicity of element positions over the aperture. In order to solve the adaptation problem for discrete array design, a position decomposition method and a V-shaped transfer function are introduced into off-grid and on-grid arrays, respectively. The effectiveness and superiority of the proposed approach is validated using experiments for designing large-scale low-frequency arrays in the marine environment. The PSLL of the off-grid array obtained by GEWOA was nearly 3.8 dB lower than that of WOA. In addition, compared with other intelligent algorithms, the on-grid array designed using GEWOA had the lowest PSLL. [ABSTRACT FROM AUTHOR]

Many bio-inspired coordination strategies have been investigated for swarm robots. Bacterial chemotaxis exhibits a certain degree of intelligence, and has been developed some optimization algorithm for robot(s), e.g., bacterial foraging optimization algorithm (BFOA) and bacterial colony chemotaxis optimization algorithm (BCC). This paper proposes a bacterial chemotaxis-inspired coordination strategy (BCCS) of swarm robotic systems for coverage and aggregation. The coverage is the problem of finding a solution to uniformly deploy robots on a given bounded space. To solve this problem, this paper uses chaotic preprocessing to generate the initial positions of the robots. After the initialization, each robot calculates the area only covered by itself as the fitness function value. Then, each robot makes an action, running or rotating, depending on coordination strategy inspired bacterial chemotaxis. Moreover, we extend this solution and introduce a random factor to overcome aggregation, which is to guide robots to rendezvous at an unspecified point. The simulation results demonstrate the superior performance of the proposed coordination strategy in both success rate and an average number of iterations than other controllers. [ABSTRACT FROM AUTHOR]

Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm. [ABSTRACT FROM AUTHOR]

The variational quantum algorithm (VQA) is a hybrid classical–quantum algorithm. It can actually run in an intermediate-scale quantum device where the number of available qubits is too limited to perform quantum error correction, so it is one of the most promising quantum algorithms in the noisy intermediate-scale quantum era. In this paper, two ideas for solving the learning with errors problem (LWE) using VQA are proposed. First, after reducing the LWE problem into the bounded distance decoding problem, the quantum approximation optimization algorithm (QAOA) is introduced to improve classical methods. Second, after the LWE problem is reduced into the unique shortest vector problem, the variational quantum eigensolver (VQE) is used to solve it, and the number of qubits required is calculated in detail. Small-scale experiments are carried out for the two LWE variational quantum algorithms, and the experiments show that VQA improves the quality of the classical solutions. [ABSTRACT FROM AUTHOR]

This paper studies the minimization of nth (positive integer) order rate matching in high-throughput multi-beam satellite systems, based on one-to-many capacity allocation pairings, for the first time in the literature. The offered and requested capacities of gateways and users' beams are exploited, respectively. Due to the high complexity of the binary optimization problem, its solution is approached with a two-step heuristic scheme. Firstly, the corresponding continuous, in [0, 1], pairing problem is solved applying the difference of convex optimization theory, and then, a transformation from continuous to binary feasible allocation is provided to extract the pairings among gateways and users' beams. Comparing with the exponential-time optimal exhaustive mechanism that investigates all possible pairs to extract the best matching for minimizing the rate matching, extended simulations show that the presented approximation for the solution of the non-convex optimization problem has fast convergence and achieves a generally low relative error for lower value of n. Finally, the simulation results show the importance of n in the examined problem. Specifically, pairings originated by the minimization of rate matching with larger n result in more fair rate matching among users' beams, which is a valuable result for satellite and generally wireless systems operators. [ABSTRACT FROM AUTHOR]

The submarine camera system is one of the most effective detection methods for detecting seabed hydrothermal sulfide. The position fixing of camera equipment is generally achieved by the ultra-short baseline (USBL) positioning systems. The dynamic changes in the marine environment and the loss of acoustic signals lead to many abnormal points in the USBL positioning data. The existing methods cannot perform position fixing correction for USBL positioning data with many scattered abnormal points. This paper selects and focuses on the seabed camera data of the XunMei mineralization area acquired from the China South Atlantic voyage and employs the DTW optimization algorithm to solve this problem. This method is implemented based on Python language and an ArcGIS technical environment, and a total of 97,469 topographic profiles with 10 m spacing are extracted from the topographic data of the object area with 1m accuracy. Moreover, the highest similarity (95.9%) is obtained between the bathymetric profile of the camera survey line and the above topographic profile. The results indicate that the proposed algorithm is fast and effective, and the positioning correction accuracy of the deep-sea near-bottom camera data can reach the meter level (determined by the topographic data accuracy). Moreover, the solution can provide services for accurately position fixing near-bottom exploration equipment and its exploration data. It can also solve the problem of underwater position fixing problem that a GPS cannot solve. [ABSTRACT FROM AUTHOR]

In this paper, we transform the problem of solving the Sylvester matrix equation into an optimization problem through the Kronecker product primarily. We utilize the adaptive accelerated proximal gradient and Newton accelerated proximal gradient methods to solve the constrained non-convex minimization problem. Their convergent properties are analyzed. Finally, we offer numerical examples to illustrate the effectiveness of the derived algorithms. [ABSTRACT FROM AUTHOR]

Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications. Although the existing cross-modal hashing methods have achieved impressive accomplishment, there are still some limitations: (1) some cross-modal hashing methods do not make full consider the rich semantic information and noise information in labels, resulting in a large semantic gap, and (2) some cross-modal hashing methods adopt the relaxation-based or discrete cyclic coordinate descent algorithm to solve the discrete constraint problem, resulting in a large quantization error or time consumption. Therefore, in order to solve these limitations, in this paper, we propose a novel method, named Discrete Semantics-Guided Asymmetric Hashing (DSAH). Specifically, our proposed DSAH leverages both label information and similarity matrix to enhance the semantic information of the learned hash codes, and the ℓ 2 , 1 norm is used to increase the sparsity of matrix to solve the problem of the inevitable noise and subjective factors in labels. Meanwhile, an asymmetric hash learning scheme is proposed to efficiently perform hash learning. In addition, a discrete optimization algorithm is proposed to fast solve the hash code directly and discretely. During the optimization process, the hash code learning and the hash function learning interact, i.e., the learned hash codes can guide the learning process of the hash function and the hash function can also guide the hash code generation simultaneously. Extensive experiments performed on two benchmark datasets highlight the superiority of DSAH over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Optimization is the science that presents a solution among the available solutions considering an optimization problem's limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms. [ABSTRACT FROM AUTHOR]

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering. [ABSTRACT FROM AUTHOR]

This paper investigates the observer-based fuzzy controller design method for nonlinear discrete-time singular systems that are represented by Takagi-Sugeno (T-S) fuzzy models. At first, the nonlinearity can be well-approximated with several local linear input-output relationships. The parallel distributed compensation (PDC) technology and the proportional derivative (PD) feedback scheme are then employed to construct the observer-based fuzzy controller. To solve the problem of unmeasured states, the impulsive phenomenon of singular systems, and the PD scheme's reasonableness, a novel observer-based fuzzy controller is developed. By using the Lyapunov theory and projection lemma, the stability criteria are built in terms of linear matrix inequalities (LMI). Moreover, the gains of fuzzy controller and fuzzy observer can be calculated synchronously by using convex optimization algorithms. Finally, a biological economic system is provided to verify the effectiveness of the proposed fuzzy control method. [ABSTRACT FROM AUTHOR]

Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called "the good, the bad, and the ugly" optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society's movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer's performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms. [ABSTRACT FROM AUTHOR]

Computer-aided process planning represents the main link between computer-aided design and computer-aided manufacturing. One of the crucial tasks in computer-aided process planning is an operation sequencing problem. In order to find the optimal process plan, operation sequencing problem is formulated as an NP hard combinatorial problem. To solve this problem, a novel genetic crow search approach (GCSA) is proposed in this paper. The traditional CSA is improved by employing genetic strategies such as tournament selection, three-string crossover, shift and resource mutation. Moreover, adaptive crossover and mutation probability coefficients were introduced to improve local and global search abilities of the GCSA. Operation precedence graph is adopted to represent precedence relationships among features and vector representation is used to manipulate the data in the Matlab environment. A new nearest mechanism strategy is added to ensure that elements of machines, tools and tool approach direction (TAD) vectors are integer values. Repair strategy to handle precedence constraints is adopted after initialization and shift mutation steps. Minimization of total production cost is used as the optimization criterion to evaluate process plans. To verify the performance of the GCSA, two case studies with different dimensions are carried out and comparisons with traditional and some modern algorithms from the literature are discussed. The results show that the GCSA performs well for operation sequencing problem in computer-aided process planning. [ABSTRACT FROM AUTHOR]

Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach--the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques. [ABSTRACT FROM AUTHOR]

This paper improves the accuracy of a mine robot's positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot's positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot. [ABSTRACT FROM AUTHOR]

In solving the portfolio optimization problem, the mean-semivariance (MSV) model is more complicated and time-consuming, and their relations are unbalanced because they conflict with each other due to return and risk. Therefore, in order to solve these existing problems, multi-strategy adaptive particle swarm optimization, namely APSO/DU, has been developed to solve the portfolio optimization problem. In the present study, a constraint factor is introduced to control velocity weight to reduce blindness in the search process. A dual-update (DU) strategy is based on new speed, and position update strategies are designed. In order to test and prove the effectiveness of the APSO/DU algorithm, test functions and a realistic MSV portfolio optimization problem are selected here. The results demonstrate that the APSO/DU algorithm has better convergence accuracy and speed and finds the least risky stock portfolio for the same level of return. Additionally, the results are closer to the global Pareto front (PF). The algorithm can provide valuable advice to investors and has good practical applications. [ABSTRACT FROM AUTHOR]

As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity. [ABSTRACT FROM AUTHOR]

Due to the the lack of information about parameters in the datasheets of photovoltaic (PV) panels, it is difficult to study their modeling because PV behavior is based on voltage–current (V-I) data, which present a highly nonlinear relationship. To solve this difficulty, this study presents a mathematical three-diode model of a PV panel that includes multiple unknown parameters: photoinduced current, saturation currents of the three diodes, three ideality factors, serial resistance, and parallel resistance. These parameters should be estimated in the three-diode model of a PV panel to obtain the actual values that represent the voltage–current profile or the voltage–power profile (because of its visual simplicity) of the PV panel under analysis. In order to solve this problem, this paper proposes a new application of the salp swarm algorithm (SSA) to estimate the parameters of a three-diode model of a PV panel. Two test scenarios were implemented with two different PV panels, i.e., Kyocera K C 200 G T and Solarex M S X 60 , which generate different power levels and are widely used for commercial purposes. The results of the simulations were obtained using different irradiance levels. The proposed PV model was evaluated based on the experimental results of the PV modules analyzed in this paper. The efficiency of the optimization technique proposed here, i.e., SSA, was measured by a fair comparison between its numerical results and those of other optimization techniques tuned to obtain the best response in terms of the objective function. [ABSTRACT FROM AUTHOR]

One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses Whale Optimisation Algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively. [ABSTRACT FROM AUTHOR]

UAV trajectory planning is one of the research focuses in artificial intelligence and UAV technology. The asymmetric information, however, will lead to the uncertainty of the UAV trajectory planning; the probability theory as the most commonly used method to solve the trajectory planning problem in uncertain environment will lead to unrealistic conclusions under the condition of lacking samples, while the uncertainty theory based on uncertain measures is an efficient method to solve such problems. Firstly, the uncertainties in trajectory planning are sufficiently considered in this paper; the fuel consumption, concealment and threat degree with uncertain variables are taken as the objective functions; the constraints are analyzed according to the maneuverability; and the uncertain multi-objective trajectory planning (UMOTP) model is established. After that, this paper takes both the long-term benefits and its stability into account, and then, the expected-value and standard-deviation efficient trajectory model is established. What is more, this paper solves the Pareto front of the trajectory planning, satisfying various preferences, which avoids the defects of the trajectory obtained by traditional model only applicable to a certain specific situation. In order to obtain a better solution set, this paper proposes an improved backbones particle swarm optimization algorithm based on PSO and NSGA-II, which overcomes the shortcomings of the traditional algorithm such as premature convergence and poor robustness, and the efficiency of the algorithm is tested. Finally, the algorithm is applied to the UMOTP problem; then, the optimal trajectory set is obtained, and the effectiveness and reliability of the model is verified. [ABSTRACT FROM AUTHOR]

This paper features the study of global optimization problems and numerical methods of their solution. Such problems are computationally expensive since the objective function can be multi-extremal, nondifferentiable, and, as a rule, given in the form of a "black box". This study used a deterministic algorithm for finding the global extremum. This algorithm is based neither on the concept of multistart, nor nature-inspired algorithms. The article provides computational rules of the one-dimensional algorithm and the nested optimization scheme which could be applied for solving multidimensional problems. Please note that the solution complexity of global optimization problems essentially depends on the presence of multiple local extrema. In this paper, we apply machine learning methods to identify regions of attraction of local minima. The use of local optimization algorithms in the selected regions can significantly accelerate the convergence of global search as it could reduce the number of search trials in the vicinity of local minima. The results of computational experiments carried out on several hundred global optimization problems of different dimensionalities presented in the paper confirm the effect of accelerated convergence (in terms of the number of search trials required to solve a problem with a given accuracy). [ABSTRACT FROM AUTHOR]

This paper proposes an easily understandable GreyWolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on the minimization of discrete-time objective functions defined as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, and the vector variable consists of the tuning parameters of the T-S PI-FCs. Since the sensitivity functions are introduced with respect to the parametric variations of the process, solving these optimization problems is important as it leads to fuzzy control systems with a reduced process parametric sensitivity obtained by a GWO-based fuzzy controller tuning approach. GWO algorithms applied with this regard are formulated in easily understandable terms for both vector and scalar operations, and discussions on stability, convergence, and parameter settings are offered. The controlled processes referred to in the course of this paper belong to a family of nonlinear servo systems, which are modeled by second order dynamics plus a saturation and dead zone static nonlinearity. Experimental results concerning the angular position control of a laboratory servo system are included for validating the proposed method. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party's behavior, we abstract the "Divide, Conquer, Assemble" (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm's apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy's optimize library and it is found to be competent with it. [ABSTRACT FROM AUTHOR]

In recent years, the electrical and/or electronic architecture of vehicles has been significantly evolving. The new generation of cars demands a considerable amount of computational power due to a large number of safety-critical applications and driver-assisted functionalities. Consequently, a high-performance computing unit is required to provide the demanded power and process these applications while, in this case, vehicle architecture moves toward a centralized architecture. Simultaneously, appropriate software architecture has to be defined to fulfill the needs of the main computing unit and functional safety requirements. However, the process of configuring and integrating critical applications into a vehicle central computer while meeting safety requirements and optimization objectives is a time-consuming, complicated, and error-prone process. In this paper, we firstly present the evolution of the vehicle architecture, past, present, and future, and its current bottlenecks and future key technologies. Then, challenges of software configuration and mapping for automotive systems are discussed. Accordingly, mapping techniques and optimization objectives for mapping tasks to multi-core processors using design space exploration method are studied. Moreover, the current technologies and frameworks regarding the vehicle architecture synthesis, model analysis with regard to software integration and configuration, and solving the mapping problem for automotive embedded systems are expressed. Finally, we propose four research questions as future works for this field of study. [ABSTRACT FROM AUTHOR]

The pursuit of excellent performance in meta-heuristic algorithms has led to a myriad of extensive and profound research and achievements. Notably, many space mission planning problems are solved with the help of meta-heuristic algorithms, and relevant studies continue to appear. This paper introduces a hierarchical optimization frame in which two types of particles—B-particles and S-particles—synergistically search for the optima. Global exploration relies on B-particles, whose motional direction and step length are designed independently. S-particles are for fine local exploitation near the current best B-particle. Two specific algorithms are designed according to this frame. New variants of classical benchmark functions are used to better test the proposed algorithms. Furthermore, two spacecraft trajectory optimization problems, spacecraft multi-impulse orbit transfer and the pursuit-evasion game of two spacecraft, are employed to examine the applicability of the proposed algorithms. The simulation results indicate that the hierarchical optimization algorithms perform well on given trials and have great potential for space mission planning. [ABSTRACT FROM AUTHOR]

Due to the introduction of frequency offsets, the pattern synthesis problem of sparse Frequency diverse array (FDA) becomes more complicated than that of the phased array. A typical way to solve this problem is to use a global optimization algorithm, but this is usually time-consuming. In this paper, we propose an efficient non-iterative beampattern synthesis approach for sparse FDA. For a given reference pattern, which can be generated by other synthesis methods, we first sample it uniformly and construct the Hankel matrix with the sampled data. By low-rank processing, a low-rank approximation version of the Hankel matrix can then be obtained. Finally, the matrix enhancement and matrix pencil (MEMP) and matrix pencil (MP) methods are applied to estimate the antenna positions, frequency offsets, and excitations of the obtained array from the approximated matrix. Besides this, two typical FDA frameworks including multi-carrier FDA (MCFDA) and standard FDA (SFDA) are considered. Numerical simulation results prove that the proposed method outperforms the existing methods in terms of synthesis error, average runtime, and percentage of saving elements. [ABSTRACT FROM AUTHOR]

This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO's performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications. [ABSTRACT FROM AUTHOR]

In this paper, we suggest an improvement to our previously undertaken approach. Briefly, this approach consisted of applying the robust fractional predictive control (RFPC) for a class of constrained fractional systems implementing the min–max optimization technique. The RFPC controller requires resolution of a non-convex min–max optimization problem. The resolution of this problem, however, can only conduce to local solutions. The reason is simple: the objective function to be optimized is non-convex due to the presence of uncertainties. In the present work, we propose a global optimization-based RFPC controller for an uncertain fractional order system. A determinist global optimization method, namely, generalized geometric programming (GGP), is proposed to solve this problem for the uncertain fractional order system. The GGP method consists of converting a non-convex problem into a convex one via the application of variable changes. The technique of the convexification of this method is applied in line with the objective function to be optimized. Consequently, we obtained a new convex criterion and a convex problem. From an experimental point of view, we applied the proposed RFPC to a real thermal system using an STM32 microcontroller in order to control our thermal system. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the application of the zero-order mini-batch optimization method in the problem of finding optimal control of a pencil of trajectories of nonlinear deterministic systems in the case of incomplete information about the state vector. The pencil of trajectories originates from a given set of initial states. To solve the problem, the structure of a feedback system is proposed, which contains models of the plant, measuring system, nonlinear state observer and control law of the fixed structure with unknown coefficients. The objective function proposed considers the quality of pencil of trajectories control, which is estimated by the average value of the Bolz functional over the given set of initial states. Unknown control laws of a plant and an observer are found in the form of expansions in terms of orthonormal systems of basis functions, which are specified on the set of possible states of a dynamical system. The original pencil of trajectories control problem is reduced to a global optimization problem, which is solved using the well-proven zero-order method, which uses a modified mini-batch approach in a random search procedure with adaptation. An algorithm for solving the problem is proposed. The satellite stabilization problem with incomplete information is solved. [ABSTRACT FROM AUTHOR]

Numerous optimization problems have been defined in different disciplines of science that must be optimized using effective techniques. Optimization algorithms are an effective and widely used method of solving optimization problems that are able to provide suitable solutions for optimization problems. In this paper, a new nature-based optimization algorithm called Snow Leopard Optimization Algorithm (SLOA) is designed that mimics the natural behaviors of snow leopards. SLOA is simulated in four phases including travel routes, hunting, reproduction, and mortality. The different phases of the proposed algorithm are described and then the mathematical modeling of the SLOA is presented in order to implement it on different optimization problems. A standard set of objective functions, including twenty-three functions, is used to evaluate the ability of the proposed algorithm to optimize and provide appropriate solutions for optimization problems. Also, the optimization results obtained from the proposed SLOA are compared with eight other well-known optimization algorithms. The optimization results show that the proposed SLOA has a high ability to solve various optimization problems. Also, the analysis and comparison of the optimization results obtained from the SLOA with the other eight algorithms shows that the SLOA is able to provide more appropriate quasi-optimal solutions and closer to the global optimal, and with better performance, it is much more competitive than similar algorithms. [ABSTRACT FROM AUTHOR]