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Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm's efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm's superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems. [ABSTRACT FROM AUTHOR]

The directed energy deposition-arc (DED-arc) process is gaining popularity for cost-effective production of large parts, especially in the marine industry. However, precise shaping control, especially for parts with sharp corners, has been challenging. Nickel aluminum bronze (NAB) alloy, widely used in the marine field, is an expensive material, making it crucial to improve its forming accuracy. To tackle this challenge, the Angle-Driven Parameter Control Model (APCM) has been introduced. This model is designed to enhance corner paths in the DED-arc process for NAB alloy. By adjusting the travel speed, the APCM aims to reduce height errors at corners. To validate the effectiveness of the APCM, single bead depositions and a multi-layer thin-wall component with seven different angles (10°, 20°, 30°, 45°, 60°, 75°, and 90°) were tested. Results indicate that the APCM has a more significant optimization effect at smaller angles. For angles above 60°, relatively small height deviations can be achieved without parameter control. For the 15-layer thin-wall component, the APCM control reduced the height deviation at the 10° angle from 7.72 to 2.09 mm. Therefore, the proposed APCM is ideal for corner paths in the DED-arc process of NAB alloy. [ABSTRACT FROM AUTHOR]

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) are general-purpose optimisers that come with several parameters like the sizes of parent and offspring populations or the mutation rate. It is well known that the performance of EAs may depend drastically on these parameters. Recent theoretical studies have shown that self-adjusting parameter control mechanisms that tune parameters during the algorithm run can provably outperform the best static parameters in EAs on discrete problems. However, the majority of these studies concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs. We study one of the best-known parameter control mechanisms, the one-fifth success rule, to control the offspring population size λ in the non-elitist (1 , λ) EA. It is known that the (1 , λ) EA has a sharp threshold with respect to the choice of λ where the expected runtime on the benchmark function OneMax changes from polynomial to exponential time. Hence, it is not clear whether parameter control mechanisms are able to find and maintain suitable values of λ . For OneMax we show that the answer crucially depends on the success rate s (i. e. a one- (s + 1) -th success rule). We prove that, if the success rate is appropriately small, the self-adjusting (1 , λ) EA optimises OneMax in O(n) expected generations and O (n log n) expected evaluations, the best possible runtime for any unary unbiased black-box algorithm. A small success rate is crucial: we also show that if the success rate is too large, the algorithm has an exponential runtime on OneMax and other functions with similar characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The opposition-based differential evolution (ODE) cannot adaptively adjust the number of individuals partake opposition-based learning, which makes it difficult to solve complex optimization problems. In this manuscript, we present an innovative approach for the treatment of variable population ODE (SASODE) by leveraging on adaptive parameters. The core idea of SASODE is to assign a jumping rate to each individual in the population, which is the key parameter that determines whether an individual enters a subpopulation or not. The initial rate assignment relies on the empirical mean of a normal distribution. During the iterative process, the mean is adjusted adaptively by taking into account the historical information of the individuals retained from the preceding generation. At the same time, the variation of this mean directly lead to changing the jumping rate of individuals and thus to adjusting the subpopulation size. In addition, the constant c and the Lehmer mean together maintain a balance between exploration and exploitation of SASODE. Experimental results show that the algorithm ranks first in the Wilcoxon test on 61 benchmarks and three optimization problems in three dimensions. Then, we confirm that SASODE can achieve an accuracy of 96% or even higher on the feature selection problem. Therefore, SASODE outperforms the other state-of-the-art algorithms compared in terms of convergence rate and accuracy. [ABSTRACT FROM AUTHOR]

In response to the growing concern about environmental sustainability and natural resource depletion, car manufacturers are looking for ways to make their vehicles more efficient. One approach is to use turbocharging technology, which compresses the air entering the engine cylinder for a higher power density per unit of displacement volume. Although turbocharging can reduce carbon emissions by improving engine performance, it has several disadvantages, including reduced reliability and service life, increased maintenance frequency and cost, and reliance on high quality fuels and lubricants. This study investigates the possible continuous monitoring of the running time of the turbocharger (TCR) rotor shaft and the influence of various input parameters, such as oil pressure and oil temperature, on TCR wear. We hypothesize that the continuous monitoring of these variables could prevent breakdowns and optimize turbocharger operation. We created a test bench consisting of an internal combustion engine (ICE) combined with a turbocharging system which includes an independent lubrication and braking mechanism. Our experiments demonstrated a positive correlation between oil inlet temperature and wear sensitivity over time. We also revealed a close relationship between oil inlet pressure, thermal properties, initial rotor speed, and wear duration. These results indicate that continuous qualitative and quantitative monitoring of wear progression can predict impending failures and increase turbocharger efficiency. The evaluation of the TCR sensitivity to wear time can assess both the quality of the turbocharger assembly at the manufacturing stage and the adequacy of its preliminary adaptation in real conditions.

Differential evolution (DE) is a heuristic global search algorithm based on population. It has exhibited great adaptability in solving continuous-domain problems, but sometimes suffered from insufficient local search ability and being trapped in local optimum when dealing with complicated optimization problems. To solve these problems, an improved differential evolution algorithm with population diversity mechanism based on covariance matrix (CM-DE) is proposed. First, a new parameter adaptation strategy is used to adapt the control parameters, in which the scale factor F is updated according to the improved wavelet basis function in the early stage and Cauchy distribution in the later stage and the crossover rate C R is generated according to normal distribution. The diversity of population and convergence speed are improved by employing the method above. Second, the perturbation strategy is incorporated into crossover operator to enhance the search ability of DE. Finally, the covariance matrix of the population is constructed, where the variance in the covariance matrix is used as indicator to measure the similarity between individuals in the population in order to prevent the algorithm from falling into local optimum resulted by low population diversity. The CM-DE is compared with the state-of-art DE variants including LSHADE (Tanabe and Fukunaga, 2014), jSO [1] , LPalmDE [2] , PaDE [3] and LSHADE-cnEpSin [4] under 88 test functions from CEC2013 [5] , CEC2014 [6] and CEC2017 (Wu et al., 2017) test suites. From the experiment results, it is obvious that among 30 benchmark functions from CEC2017 on 50D optimization, the CM-DE algorithm has 22, 20, 24, 23, 28 better performances comparing with LSHADE, jSO, LPalmDE, PaDE, and LSHADE-cnEpsin. For CEC2017 on 30D optimization, the proposed algorithm secures better performance on 19 out of 30 benchmark functions in terms of convergence speed. In addition, a real-world application is also used to verify the feasibility of the proposed algorithm. The experiment results validate the highly competitive performance in terms of solution accuracy and convergence speed. • A new parameter adaptation mechanism is proposed to adjust the scale factor F and crossover rate CR. The experiment results show that the new parameter adaptation mechanism improves the exploration ability of the proposed algorithm. • In order to prevent prematureness, a perturbation strategy is incorporated into the crossover strategy, which firstly constructs a new crossover operation between the mutant vector and target vector based on the t-distribution probability density function; secondly, the information of the outstanding individuals is used to guide the search direction. • By calculating the covariance matrix of the population, the variance in the covariance matrix is used to determine the diversity of individuals in the current population. In the iterative process, a counter is set to count the number of variances in each dimension that is less than the set condition. When the counter meets the predefined threshold, a simple competition mechanism is used to increase the diversity of the population by perturbing the t-distribution or the Cauchy distribution for all stagnant individuals. [ABSTRACT FROM AUTHOR]

Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their training processes are usually highly time-consuming and they are not able to benefit from parallel or distributed platforms; (2) they are usually sensitive to their hyperparameters, which means that the quality of the final results is heavily dependent on their values; (3) and limited benchmarks have been used to assess their generality. This paper addresses these issues by proposing a methodology for training out-of-the-box parameter control policies for mono-objective non-niching evolutionary and swarm-based algorithms using distributed reinforcement learning with population-based training. The proposed methodology is suitable to be used in any mono-objective optimization problem and for any mono-objective and non-niching Evolutionary and swarm-based algorithm. The results in this paper achieved through extensive experiments show that the proposed method satisfactorily improves all the aforementioned issues, overcoming constant, random and human-designed policies in several different scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority. • A wavelet basis function based generation of scale factor F is proposed. • A hybrid trial vector generation strategy with perturbation and t-distribution is advanced. • A fitness-deviation-based parameter control is proposed. • A novel diversity indicator is proposed and a restart scheme can be launched if necessary. • To avoid over-fitting, a larger test suite is employed in algorithm validation. [ABSTRACT FROM AUTHOR]

In response to the growing concern about environmental sustainability and natural resource depletion, car manufacturers are looking for ways to make their vehicles more efficient. One approach is to use turbocharging technology, which compresses the air entering the engine cylinder for a higher power density per unit of displacement volume. Although turbocharging can reduce carbon emissions by improving engine performance, it has several disadvantages, including reduced reliability and service life, increased maintenance frequency and cost, and reliance on high quality fuels and lubricants. This study investigates the possible continuous monitoring of the running time of the turbocharger (TCR) rotor shaft and the influence of various input parameters, such as oil pressure and oil temperature, on TCR wear. We hypothesize that the continuous monitoring of these variables could prevent breakdowns and optimize turbocharger operation. We created a test bench consisting of an internal combustion engine (ICE) combined with a turbocharging system which includes an independent lubrication and braking mechanism. Our experiments demonstrated a positive correlation between oil inlet temperature and wear sensitivity over time. We also revealed a close relationship between oil inlet pressure, thermal properties, initial rotor speed, and wear duration. These results indicate that continuous qualitative and quantitative monitoring of wear progression can predict impending failures and increase turbocharger efficiency. The evaluation of the TCR sensitivity to wear time can assess both the quality of the turbocharger assembly at the manufacturing stage and the adequacy of its preliminary adaptation in real conditions. [ABSTRACT FROM AUTHOR]

Aiming at the problems of easy introduction of chemical element residues, micro scratches and low material removal rate in contact chemical mechanical polishing and non-contact plasma polishing processes of single-crystal silicon, a non-contact green polishing method of plasma cavitation stripping for single-crystal silicon is proposed. In the water-based working fluid medium with pulse voltage more than 100 V, the high impedance state isolation vapor layer breaks down due to the small curvature convergence of electron flux and induces an oxygen plasma channel. The convex position of the micro-region on the surface of the anode single crystal Si generates an SiO2 loose film due to the enhancement of the anode chemical reaction by the oxygen plasma. During the pulse intermission period, the plasma channel collapses due to the cold shock liquefaction of the water-based working fluid medium near the wall surface, and cavitation micro-jet impact force is formed at the same time to strip the loose film on a nano scale. After treating the sample for a certain time, the surface roughness of the sample can reach 1.54 nm, and no new chemical elements will be introduced into the surface of the sample. It provides a green non-contact method for plane/non-plane ultra-precision machining of brittle and hard materials.

Differential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adaptive differential evolution algorithm with multi-mutation schemes and weighted control parameter setting (MWADE), to further strengthen its search capability. In MWADE, a multi-schemes mutation strategy is first proposed to properly exploit or explore the promising information of each individual. Herein, the whole population is dynamically grouped into three subpopulations according to their fitness values and search performance, and three different mutant operators with various search characteristics are respectively adopted for each subpopulation. Meanwhile, in order to ensure the exploration of algorithm at the later evolutionary stage, a weight-controlled parameter setting is proposed to suitably assign scale factors for different differential vectors. Moreover, a random opposition mechanism with greedy selection is introduced to avoid trapping in local optima or stagnation, and an adaptive population size reduction scheme is devised to further promote the search effectiveness of algorithm. Finally, to illustrate the performance of MWADE, thirteen typical algorithms are adopted and compared with MWADE on 30 functions from IEEE CEC 2017 test suite with different dimensions, and the effectiveness of its proposed components are also investigated. Numerical results indicate that the proposed algorithm has a better search performance.

In high dimension, the bifurcation theory of periodic orbits of nonlinear dynamics systems are difficult to establish in general. In this paper, by performing the curvilinear coordinate frame and constructing a Poincaré map, we obtain some sufficient conditions of the bifurcation of periodic solutions of some 2n-dimensional systems for the unperturbed system in two cases: one is a decoupled n-degree-of-freedom nonlinear Hamiltonian system and the other has an isolated invariant torus. We use a new method and study new types of systems compared with the existing results. As an application we study the bifurcation and number of periodic solutions of an ice covered suspension system. Under a certain parametrical condition, the number of periodic solutions of this system can be 2 or 1 with the variation of parameter p 2 . [ABSTRACT FROM AUTHOR]

In this paper, taking the generalized synchronization problem of discrete chaotic systems as a starting point, a generalized synchronization method incorporating error-feedback coefficients into the controller based on the generalized chaos synchronization theory and stability theorem for nonlinear systems is proposed. Two discrete chaotic systems with different dimensions are constructed in this paper, the dynamics of the proposed systems are analyzed, and finally, the phase diagrams, Lyapunov exponent diagrams, and bifurcation diagrams of these are shown and described. The experimental results show that the design of the adaptive generalized synchronization system is achievable in cases in which the error-feedback coefficient satisfies certain conditions. Finally, a chaotic hiding image encryption transmission system based on a generalized synchronization approach is proposed, in which an error-feedback coefficient is introduced into the controller. [ABSTRACT FROM AUTHOR]

Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm’s efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm’s superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

There are few reports on the nondestructive adjustment of the oscillation amplitude of the chaotic sequence in the discrete map. To study the lossless regulation of the oscillation amplitude of chaotic sequences, this article proposes a new simple two-dimensional (2D) hyperchaotic map with trigonometric functions. It not only exhibits the offset boosting bifurcation and offset boosting coexistence attractors, but also shows the offset boosting of two state variables with respect to arbitrary parameters in the 2D map. The simulation results of bifurcation diagram, maximum Lyapunov exponent and attractor phase diagram show that the map can produce complex dynamical behaviors. In addition, the introduction of new control parameters into the 2D hyperchaotic map can also make the hyperchaotic map exhibit rich multi-stable phenomena. At the same time, the covariation of the initial state and control parameters can result in arbitrary switching and coexistence of attractors in the phase plane. The 2D hyperchaotic map was tested and verified by hardware experiment platform. Moreover, we design a pseudo-random number generator (PRNG) to test the hyperchaotic map. The results show that the pseudo-random numbers generated by the hyperchaotic map have high randomness. [ABSTRACT FROM AUTHOR]

This study proposes a general multiple parameter control framework by leveraging the ability of a reinforcement learning system to learn empirical knowledge for evolutionary computation. We design a feedback evaluation mechanism to define the rewards offered to agents, using which they can learn to choose appropriate parameters in formulated action sets. Moreover, a learning strategy is proposed to utilize the parameter selection‐related knowledge that is gained during training episodes. Three famous evolutionary computation (EC) methods (i.e., particle swarm optimization, artificial bee colony, and differential evolution) are selected as the baseline algorithms and applied to the proposed framework. The aforementioned redesigned algorithms are tested on 15 common benchmark functions, as well as the CEC2017 benchmarks. In addition, the robustness of the algorithms is demonstrated through parameter sensitivity analysis. The results of the comparative analysis reveal that the three improved algorithms exhibit a faster overall convergence and higher accuracy than their state‐of‐the‐art variants. It is also confirmed that our proposed framework has the capability to improve the performance of EC approach. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms for a global optimization problem have unbound strategy parameters that affect solution accuracy and algorithm efficiency. The task of determining the optimal values of unbound parameters is called the parameter setting problem and can be solved by static parameter setting methods (performed before the algorithm run) and dynamic parameter control methods (performed during the run). The paper introduces a novel hierarchical parameter setting method for the class of population-based metaheuristic optimization algorithms. A distinctive feature of this method is the use of a hierarchical algorithm model. The lower level represents a sequential algorithm from this class, and the upper level represents an algorithm with the parallel island model. Parameter setting is performed by the hierarchical method, which combines parameter tuning for the sequential algorithm and adaptive parameter control for the parallel algorithm. Parameter control is based on vector fitness criteria which consist of a convergence rate and a solution value. An approach for estimating the convergence rate for a multistep optimization method is proposed. Experimental results for CEC benchmark problems are presented and discussed. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of existing methods of controlling the efficiency of multipart radio engineering systems and their individual components. The authors consider the situations when traditional methods do not allow controlling the efficiency of the system in operational mode. The study substantiates the practicability and possibility of estimating the efficiency of microwave channels for receiving telemetry information from artificial space objects according to indirect criteria. The principle of forming a list of indirect control criteria is demonstrated on the example of the functional diagram of the radio receiver system of the RT-32 C, X, K radio telescope. The study formulates the principles of creating a system designed to control the parameters of the information channel of such a microwave radio receiver system by controlling indirect parameters which correspond to the chosen criteria and the list of the basic parameters. A list of indirect parameters affecting the performance of the entire system by controlling the characteristics of its main parameters has been created. The paper carefully considers the problems that arise when equipping the radio receiver system with built-in tools for controlling and managing the indirect parameters. A system of nominal equations is designed for estimating the state of the basic parameters of the components of the radio receiver system. Each of the indirect parameters is codified in digital form. An example of a block diagram of a distributed control and management system for complex radio devices is presented. The authors determine acceptable deviations for the indirect parameters relative to the nominal values of the direct parameters for controlling the state of both individual devices and the whole multipart system. It is proposed to implement a control and management system of a complex system by using a specialized controller-based circuit board built into each functional device of the radio receiver system. One possible version of such board, its design and all functional units are considered in detail. The proposed methods of controlling and managing the state of a multipart radio-technical system when receiving telemetric information directly in the working mode has been successfully approved during a series of radio astronomical studies on RT-32 C, X, K radio telescope at the Space Researches and Communications Center of the State Space Agency of Ukraine.

Cuckoo search (CS) algorithm is an efficient search technique for addressing numerical optimization problems. However, for the basic CS, the step size and mutation factor are sensitive to the optimization problems being solved. In view of this consideration, a new version namely the parameter control based CS (PCCS) algorithm is presented to strengthen the search accuracy and robustness. In this variant, the step size and mutation factor are dynamically updated according to the elite information stored in the historical archives at each generation, so as to realize the reasonable setting of these control parameters. For performance evaluation, numerical experiments are conducted on 25 benchmark functions from two different test suites. Moreover, the application in neural network optimization is also considered to further investigate the effectiveness. Experimental results indicate that the proposed PCCS algorithm is a promising and competitive method in terms of solution quality and convergence rate. [ABSTRACT FROM AUTHOR]

Comparative estimation of energy parameters of power amplifiers of single-band radio transmitters using automatic mode adjustment using a deterministic two-frequency test signal instead of a random single-band signal modulated by speech is investigated in the work. Relationships are found that allow judging the power consumption of the terminal stage of the power amplifier with automatic mode adjustment under various types of modulation based on the results of measurements obtained during tests. The ratios between the power consumption of the output stage when amplifying the random speech signal and amplifying the deterministic two-frequency test signal are obtained both without taking into account losses in the controlled power supply and taking into account such losses. Method is proposed for calculation of energy gain and efficiency factor (efficiency) when applying automatic control of supply voltage of output cascades of shortwave transmitters intended for modulation with speech signals. The loss in the regulated power supply has been estimated. The advantage of power amplifier circuits with automatic mode adjustment is justified.

In this paper, taking the generalized synchronization problem of discrete chaotic systems as a starting point, a generalized synchronization method incorporating error-feedback coefficients into the controller based on the generalized chaos synchronization theory and stability theorem for nonlinear systems is proposed. Two discrete chaotic systems with different dimensions are constructed in this paper, the dynamics of the proposed systems are analyzed, and finally, the phase diagrams, Lyapunov exponent diagrams, and bifurcation diagrams of these are shown and described. The experimental results show that the design of the adaptive generalized synchronization system is achievable in cases in which the error-feedback coefficient satisfies certain conditions. Finally, a chaotic hiding image encryption transmission system based on a generalized synchronization approach is proposed, in which an error-feedback coefficient is introduced into the controller.

In recent decades, bio-inspired numerical algorithms have emerged as an alternative for optimizing the structural design of trusses. The differential evolution algorithm (DEA) has demonstrated both good performance and ease of implementation. However, unlocking the full potential of DEA to address engineering problems poses a significant challenge, necessitating a strategic and informed definition of each component of the algorithm. This research systematically evaluates the influence of defining DEA components on improving the reliability of truss optimization. The algorithm structure of DEA was configured for five aspects: (I) the mutation operator, (II) inclusion of multi-modal techniques, (III) inclusion of parameter control techniques, (IV) definition of the initial population, and (V) local search heuristics. A comprehensive evaluation is conducted to assess the performance of 23 DEA configurations in optimizing eight planar and spatial trusses, varying in size from 10 to 163 elements. Assessment is based on key criteria such as optimal weight, robustness, and computational cost, providing a thorough basis for comparison. The results showed that no tested DEA configuration is the best for all trusses. Instead, the study revealed the presence of recommendable configurations, each tailored to the specific complexities and scales inherent in various truss structures. The integration of multimodal and local search techniques proves particularly advantageous for larger trusses, amplifying the algorithm's exploratory capabilities to effectively navigate and uncover optimal regions. In contrast, using parameter control technique was more effective in optimizing smaller trusses, capitalizing on the rapid exploration of potential optimal areas in a smaller search space. There was a mutation operator that produced the best results for large trusses and good results for smaller structures. This operator uses the target vector as the base vector and guides its movement from a best-based difference vector, achieving a balance between the exploratory stage and user-defined constraints that promote the exploitation of potentially optimal areas. No discernible impact was observed when the initial population heuristic proposed was used. Finally, this study underscores the feasibility of DEA configurations for optimizing trusses of varying complexities, as proved by a comparison with results from the literature. • Mutation and multimodal techniques impact the DEA performance on truss optimization. • Custom insights refine the optimal design of trusses at varying levels of complexity. • DEA configurations yield improved truss designs as confirmed by literature results. • Paper links DEA concepts to truss optimization, aiding understanding. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is one of the most popular and powerful branches of evolutionary algorithm family. However, even many state-of-the-art DE-based variants still exist weakness such as improper parameter adaptation and population stagnation during the later stage of evolution. To mitigate these deficiencies, differential evolution with fitness-difference based parameter control and hypervolume diversity indicator (FDHD-DE) is proposed in this paper. Firstly, a semi-adaptive adaptation scheme for control parameters is proposed, in which the generation of scale factor and crossover rate is modified by dividing into two stages, thus enhancing the efficiency of parameter adaptation. Secondly, a novel fitness-based weighting strategy is proposed to improve the performance of existing success history-based adaptation by employing a novel approach of utilizing fitness information. Finally, a hypervolume-based diversity indicator and corresponding dimension exchange strategy are proposed to alleviate the problem of population stagnation. The performance of FDHD-DE is verified on the 88 benchmark functions from Congress on Evolutionary Computation (CEC) 2013, CEC 2014, and CEC 2017 test suites on 10D, 30D and 50D and a real-world application. The experiment results are compared with several state-of-art DE variants, and the results show that FDHD-DE has better performance, both in terms of solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks. [ABSTRACT FROM AUTHOR]