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In this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a dynamic opposite learning strategy broadens the search area of the population, enhances global search ability, and improves population diversity. In the honey harvesting stage of the honey badger (development), differential mutation strategies are combined, selectively introducing local quantum search strategies that enhance local search capabilities and improve population optimization accuracy, or introducing dynamic Laplacian crossover operators that can improve convergence speed, while reducing the odds of the HBA sinking into local optima. Through comparative experiments with other algorithms on the CEC2017, CEC2020, and CEC2022 test sets, and three engineering examples, EHBA has been verified to have good solving performance. From the comparative analysis of convergence graphs, box plots, and algorithm performance tests, it can be seen that compared with the other eight algorithms, EHBA has better results, significantly improving its optimization ability and convergence speed, and has good application prospects in the field of optimization problems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most promising black-box numerical optimization methods. However, DE algorithms suffer from the problem of control parameter settings. Various adaptation methods have been proposed, with success history-based adaptation being the most popular. However, hand-crafted designs are known to suffer from human perception bias. In this study, our aim is to design automatically a parameter adaptation method for DE with the use of the hyper-heuristic approach. In particular, we consider the adaptation of scaling factor F, which is the most sensitive parameter of DE algorithms. In order to propose a flexible approach, a Taylor series expansion is used to represent the dependence between the success rate of the algorithm during its run and the scaling factor value. Moreover, two Taylor series are used for the mean of the random distribution for sampling F and its standard deviation. Unlike most studies, the Student's t distribution is applied, and the number of degrees of freedom is also tuned. As a tuning method, another DE algorithm is used. The experiments performed on a recently proposed L-NTADE algorithm and two benchmark sets, CEC 2017 and CEC 2022, show that there is a relatively simple adaptation technique with the scaling factor changing between 0.4 and 0.6, which enables us to achieve high performance in most scenarios. It is shown that the automatically designed heuristic can be efficiently approximated by two simple equations, without a loss of efficiency. [ABSTRACT FROM AUTHOR]

It is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current–voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect parameters have on the efficacy of the PV system with respect to current and energy results. The problem's characteristics make the handling of algorithms susceptible to local optima and resource-intensive processing. To effectively extract PV model parameter values, an improved hybrid Sparrow Search Algorithm (SSA) with Exponential Distribution Optimization (EDO) based on the Differential Evolution (DE) technique and the bound-constraint modification procedure, called ISSAEDO, is presented in this article. The hybrid strategy utilizes EDO to improve global exploration and SSA to effectively explore the solution space, while DE facilitates local search to improve parameter estimations. The proposed method is compared to standard optimization methods using solar PV system data to demonstrate its effectiveness and speed in obtaining PV model parameters such as the single diode model (SDM) and the double diode model (DDM). The results indicate that the hybrid technique is a viable instrument for enhancing solar PV system design and performance analysis because it can predict PV model parameters accurately. [ABSTRACT FROM AUTHOR]

One of the most significant financial benefits of a shared mobility mode such as ridesharing is cost savings. For this reason, a lot of studies focus on the maximization of cost savings in shared mobility systems. Cost savings provide an incentive for riders to adopt ridesharing. However, if cost savings are not properly allocated to riders or the financial benefit of cost savings is not sufficient to attract riders to use a ridesharing mode, riders will not accept a ridesharing mode even if the overall cost savings is significant. In a recent study, the concept of discount-guaranteed ridesharing has been proposed to provide an incentive for riders to accept ridesharing services through ensuring a minimal discount for drivers and passengers. In this study, an algorithm is proposed to improve the performance of the discount-guaranteed ridesharing systems. Our approach combines a success rate-based self-adaptation scheme with an evolutionary computation approach. We propose a new self-adaptive metaheuristic algorithm based on success rate and differential evolution for the Discount-Guaranteed Ridesharing Problem (DGRP). We illustrate effectiveness of the proposed algorithm by comparing the results obtained using our proposed algorithm with other competitive algorithms developed for this problem. Preliminary results indicate that the proposed algorithm outperforms other competitive algorithms in terms of performance and convergence rate. The results of this study are consistent with the empirical experience that two people working together are more likely to come to a correct decision than they would if working alone. [ABSTRACT FROM AUTHOR]

Oriented by the demands on high-speed and large-load picking up/handling in textile, electronics and other industries, a novel parallel manipulator (PM) achieving SCARA motion was designed. First, the mobility of the PM was obtained by dual space theory. Thereafter, the kinematic model was deduced in detail, and a dynamics model was developed using the Lagrangian formulation. The validity of the above models was verified by ADAMS software. Furthermore, the singularity existing in the workspace was analyzed. Subsequently, a homogeneous Jacobian matrix was constructed by the characteristic length factor, based on which dexterity index was defined. Aiming at the optimal performance in the singularity-free task workspace, a dimensional synthesis model incorporating kinematics and dynamics performance is proposed, which is efficiently tackled by differential evolution (DE) algorithm. The obtained result provides a sound foundation for the development of an engineering prototype. [ABSTRACT FROM AUTHOR]

The determination is presented of seven parameters of a DC motor's drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters' determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive's acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the La calculation. The deviations of all the other parameters' calculations were less than 2%. [ABSTRACT FROM AUTHOR]

The selection method of individuals is a crucial step in the mutation operator of differential evolution (DE). Typically, methods that select individuals with better fitness values are used to increase the exploitation ability of the algorithm. However, some researches have shown that incorporating distribution information of target space to measure diversity can improve the exploration ability of the algorithm. With this concept in mind, this paper presents an innovative approach called the adaptive stochastically ranking-based tournament selection method (ASR-TS). ASR-TS initially uses tournament selection and subsequently stochastically ranks selected individuals based on fitness and diversity measurements, leading to the determination of the tournament's winner. Furthermore, the stochastic ranking parameter is adaptively set based on the success rate of the previous generation to strike a balance between the exploitation and exploration abilities of the algorithm. The proposed ASR-TS method was tested on CEC 2013 benchmark functions in several original and improved DEs. To further validate the effectiveness of this method, the ASR-TS method was also tested on CEC 2022 benchmark functions as well as real-world problems. The experimental results demonstrate that the proposed ASR-TS method outperformed various other methods by a significant margin, which proves its efficiency and effectiveness in balancing exploration and exploitation. [ABSTRACT FROM AUTHOR]

In practical applications, the optimal solutions of multi-objective optimization are not unique. Some problems exist different Pareto Sets (PSs) in the decision space mapped to the same Pareto Front (PF) in the objective space, which are called multimodal multi-objective problems (MMOPs). To tackle this issue, this paper proposes a multimodal multi-objective optimization based on a local optimal neighborhood crowding distance differential evolution algorithm. First, an adaptive partitioning strategy in the initialization phase is proposed by using the characteristics of the heuristic stochastic search. That ensures the local optimal solution is quickly found among multiple PSs. Second, opposition-based learning is combined with differential mutation to generate vectors, which accelerate the convergence of the population to the optimal solution. Finally, a method for neighborhood crowding distances on different Pareto ranks is designed. The distance is computed by a weighted sum of Euclidean distances for the nearest neighbors. While reducing computational complexity, this strategy reflects realistic crowding degree. With these methods, balances the diversity performance of the decision and the objective space, while improving the search capability. Multiple PSs reveal the problem's potential characteristics and meet the needs of the decision-maker. The practical significance is verified by the application of actual distance minimization problem. According to experimental results, the proposed method can achieve a high level of comprehensive performance. [ABSTRACT FROM AUTHOR]

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller. [ABSTRACT FROM AUTHOR]

In this paper we investigate the parameter estimation of solar photovoltaic (PV) models using the self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The dynamic mutation divides the population into three groups according to fitness values and selects groups and their vectors with adaptive probabilities to create a mutant vector. The algorithm also encodes scaling factor and crossover rate values into target vectors to use in mutation and crossover operations and adjusts them with pheromones in the selection process. Experimental results show that the SDE-FMP algorithm can give the solutions with the lowest errors and is overall competitive with the compared methods regarding the mean errors. [ABSTRACT FROM AUTHOR]

This paper studies a common planning problem encountered in steelmaking production. The problem is to group different customer orders into a set of batches to accommodate the mass production mode of steelmaking furnaces. We formulate the problem as a novel mixed-integer programming model by considering the practical technological requirements. To solve the problem, we propose a cooperative coevolution framework in which an effective decomposition scheme based on graph partitioning is developed. The decomposition scheme first explores the problem structure by considering the production process rules and then exploits the batch information of the best-so-far solution to identify the potentially better decompositions. To solve each decomposed subcomponent, we propose a new differential evolution algorithm which incorporates a subpopulation-based classification mechanism and local search with an external archive strategy to balance the abilities of exploration and exploitation. Computational tests on a set of real production data as well as on a more diverse set of randomly generated problem instances show that our method is effective and efficient in practical application and outperforms other benchmark algorithms. [ABSTRACT FROM AUTHOR]

We propose and analyze a variant of the recently introduced kinetic-based optimization method that incorporates ideas like survival-of-the-fittest and mutation strategies well-known from genetic algorithms. Thus, we provide a first attempt to reach out from the class of consensus/kinetic-based algorithms towards genetic metaheuristics. Different generations of genetic algorithms are represented via two species identified with different labels, binary interactions are prescribed on the particle level and then we derive a mean-field approximation in order to analyze the method in terms of convergence. Numerical results underline the feasibility of the approach and show in particular that the genetic dynamics allows to improve the efficiency, of this class of global optimization methods in terms of computational cost. [ABSTRACT FROM AUTHOR]

In recent years, intrusion detection systems (IDSs) have increasingly come to be regarded as a significant method due to their potential to develop into a key component that is necessary for the safety of computer networks. This work focuses on the usage of extreme learning machines, which are also known as ELMs, with the purpose of spotting prospective intrusions and assaults. The proposed method combines the self-adaptive differential evolution method for optimising network input weights and hidden node biases and multi-node probabilistic approach with the extreme learning machine for deriving network output weights. This body of work presents an innovative method of learning that can be put into practice in order to determine whether or not an incursion has taken place in the system that is the focus of the investigation that is being carried out by this body of work. A hybrid extreme learning machine is used in the execution of this strategy. When there is one thousand times more traffic on a network, the ability of regular IDS systems to detect malicious network intrusions is lowered by a factor of one hundred. This is because there are less opportunities to detect the intrusions. This is due to the fact that there are less probabilities to identify potential dangers. This paper lays the groundwork for a novel methodology for identifying malicious network breaches. The findings of the simulation demonstrated that putting into practice the approach that was proposed resulted in an improvement in the accuracy of the scenario's classification while it was being investigated. The implementation of the method seems to have produced the desired results. [ABSTRACT FROM AUTHOR]

This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead. [ABSTRACT FROM AUTHOR]

The purpose of this research is to improve the dynamic-mechanical properties of the polypropylene filled by artichoke stem (AS) particles and wollastonite (W) in different weight fractions. The effect of weight ratios of fillers in polypropylene was mathematically modeled using the data obtained as a result of the experimental work. In the modeling phase, multiple nonlinear neuro-regression analysis was used. In this context, proposed linear and nonlinear models have been examined by performing R2 training, R²adjusted, R²testing, and boundedness check. The models that satisfy these four criteria were selected as the objective functions for the optimization phase. Finally, Modified Differential Evolution Algorithm was used to obtain maximum storage modulus and loss modulus by adjusting weight percent ratio of artichoke stem particle and wollastonite. The experimental results and the modeling optimization results showed that when the polypropylene-artichoke stem particle-wollastonite hybrid polymer composite was used instead of other non-hybrid polymer composite, the storage modulus and the loss modulus improved by approximately 40%. [ABSTRACT FROM AUTHOR]

The p-Hub allocation problem is a classic problem in location assignment, which aims to optimize the network by placing Hub devices and allocating each demand node to the corresponding Hub. A mutation Transit search (TS) algorithm with the introduction of the black hole swallowing strategy was proposed to solve the p-Hub allocation problem. Firstly, the mathematical model for the p-Hub allocation problem is established. Six mutation operators specifically designed for p-Hub allocation problem are introduced to enhance the algorithm's ability to escape local optima. Additionally, the black hole swallowing strategy was incorporated into TS algorithm so as to accelerate its convergence rate while ensuring sufficient search in the solution space. The improved TS algorithm was applied to optimize three p-Hub location allocation problems, and the simulation results are compared with those of the basic TS algorithm. Furthermore, the improved TS algorithm is compared with the Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA), Harmony Search Algorithm (HS), and Particle Swarm Optimization (PSO) to solve three of p-Hub allocation problems. Finally, the impact of the number of Hubs on the cost of three models was studied, and the simulation results validate the effectiveness of the improved TS algorithm. [ABSTRACT FROM AUTHOR]

Multilocus variable number tandem repeat analysis (MLVA) utilizes short DNA repeat polymorphism in genomes, which is termed variable number tandem repeat (VNTR), to differentiate closely related organisms. One research challenge is to find an optimal set of VNTR to distinguish different members accurately. An intuitive method is to use an exhaustive search method. However, this method is not an efficient way to find optimal solutions from a dataset comprising many attributes (loci) due to the curse of dimensionality. In this study, metaheuristic methods are proposed to find an optimal set of loci combination. Basic genetic algorithm (BGA) and modified genetic algorithm (MGA) were proposed in our previous work for this purpose. However, they require prior knowledge from an experienced user to specify the minimum number of loci for achieving good results. To impose no such expertise requirement for parameter setting, a GA with Duplicates (GAD), which allows the inclusion of duplicated loci in a chromosome (potential solution) during the search process, is developed. The study also investigates the search performance of a hybrid metaheuristic method, namely quantum-inspired differential evolution (QDE). Hunter-Gaston Discriminatory Index (HGDI) is used to indicate the discriminatory power of a loci combination. Two Mycobacterium tuberculosis MLVA datasets obtained from a public portal and a local laboratory respectively, are used. The results obtained by using exhaustive search and metaheuristic methods are first compared, followed by a performance comparison among BGA, MGA, GAD, and QDE by a statistical approach. The best-performing GA method (i.e., GAD) and QDE are selected for a performance comparison with several recent metaheuristic methods using both MLVA datasets by a statistical approach. The statistical results show that both GAD and QDE could achieve higher HGDI than the recent methods using a small but informative set of loci combination. [ABSTRACT FROM AUTHOR]

In recent decades, differential evolution (DE) has been employed to address a diverse range of nonlinear, nondifferentiable, and nonconvex optimization problems. In this study, we introduce an enhanced adaptive differential evolution algorithm to address the inversion problem associated with magnetic data. The primary objective of the inversion process is to minimize the discrepancy between observed data and predicted data derived from the inverted model. So, the contributions of this paper include the following two points. First, a new mechanism for generating crossover rate (CR) is proposed, which tends to reduce the CR values corresponding to vectors with better objective function values. Second, a new mutation strategy with direction information is proposed to expedite convergence. Additionally, modifications were made to the adjustment of the regularization factor to prevent it from becoming too minimal, thereby preserving its efficacy. The proposed algorithm is validated through synthetic models and a field example. Results from synthetic models demonstrate that our method is superior to and competitive with the original adaptive DE in both solution quality and convergence velocity. For the field example, the Inverted models align closely with the drill-well information. [ABSTRACT FROM AUTHOR]

The formation and evolution mechanisms of complex types and scales of marine–continental transitional shale pores are still indefinite, restricting the accurate evaluation of shale reservoir and the effective evaluation of coal-bearing marine–continental transitional shale gas resource quantity. Considering the Shanxi shale in Ordos basin of China as the research object, combining the FE-SEM images and petrophysical analysis, high-pressure mercury intrusion porosimetry, and CO2 and N2 adsorption–desorption experiments, the structure characteristics and differential evolution mechanisms of multiscale and multitype of coal-bearing shale pores were discussed. The results show that coal-bearing marine–continental transitional shales are rich in clay minerals and organic matters (OMs). Pores developed within organic matters, clay, and brittle minerals of coal-bearing shale have decreasing porosity values. OM pores are directly related to micro- and mesopores, with high specific surface areas, while the porosity of inorganic pores increases with the increasing pore diameter. The porosity of all pores shows a positive relationship with permeability, which changes periodically with the increase in maturity. Coal-bearing shale pores are mainly plate- and ink bottle-shaped, with multimodal pore size distributions. Controlled by both diagenesis and hydrocarbon generation, the evolution of coal-bearing shale pores could be mainly divided into four stages. Furthermore, the pore evolution model of coal-bearing marine–continental transitional shale was preliminarily constructed. This study would enhance the understanding of reservoir evolution of the coal-bearing shale and provide useful information for the assessment and evaluation of reservoir capacity. [ABSTRACT FROM AUTHOR]

Aiming at the problem of load dispatching under demand response, it is necessary to provide a response scheme to meet the interests of residents and improve the stability of power grid operation. This paper considered electricity price, incentive demand response mechanism and residential electricity demand, and established a multi-user load scheduling high-dimensional objective optimization model with the goal of minimizing electricity cost and community load variance. Combined with the characteristics of the model, a cooperative co-evolutionary differential evolution algorithm based on multi-strategy is proposed. It designed a hybrid coding and population initialization strategy based on the characteristics of residential electricity consumption to improve the quality of the solution. It introduced the idea of cooperative co-evolution to decompose the problem variables, and divided the population according to the high-dimensional target grouping and aggregation to avoid falling into local optimum. In the evolution of each sub-population, it adopted a double differential mode coordination strategy, and constructed knowledge transfer individuals to realize information interaction between populations. Finally, it retained the complete excellent solution to the external file through the population merging strategy combining greedy and random selection to improve the convergence and distribution of the Pareto optimal set. Simulation shows that the proposed method can reduce the electricity cost of community residents by about 18% and the variance of load fluctuation by more than 30%. As the number of residents increases As the number of residents increases, the convergence and diversity of the algorithm are more obvious than other algorithms in the same field. [ABSTRACT FROM AUTHOR]

The performance evaluation and classification of computer system is a key step in the development of computer high performance. In order to eliminate the misjudgment of performance levels due to uncertainty in the evaluation process, a theoretical model for computer performance evaluation using extended cloud theory to develop.Secondly, to take into account the clarity and ambiguity of the stratification boundary, then this paper proposes an optimal method for calculating cloud entropy that adaptively evaluates the entropy of an object, which improves the rationality and accuracy of the model. Finally, an improved vector measurement method（INFO） is also proposing for the nonlinear solution of model parameters in the presence. A differential evolution strategy is employing to perturb the vectors to generate candidate solutions to optimize the initial distribution uniformity of potential solutions. In the search phase, combined with the T-distribution strategy, the search range of the algorithm can be expanded according to the probability value to improve the global search capability of the algorithm. The results show that the proposed model is an effective method for computer performance evaluation. [ABSTRACT FROM AUTHOR]

Whether and how the spatial arrangement of a population influences adaptive evolution has puzzled evolutionary biologists. Theoretical models make conflicting predictions about the probability that a beneficial mutation will become fixed in a population for certain topologies like stars, in which "leaf" populations are connected through a central "hub." To date, these predictions have not been evaluated under realistic experimental conditions. Here, we test the prediction that topology can change the dynamics of fixation both in vitro and in silico by tracking the frequency of a beneficial mutant under positive selection as it spreads through networks of different topologies. Our results provide empirical support that meta-population topology can increase the likelihood that a beneficial mutation spreads, broaden the conditions under which this phenomenon is thought to occur, and points the way toward using network topology to amplify the effects of weakly favored mutations under directed evolution in industrial applications. Lay Summary: Most natural populations are spatially structured, meaning that they are geographically subdivided and connected by migration. Whether spatial structure impacts adaptive evolution has been less clear as different theoretical approaches to modeling the spread of beneficial mutations through spatially structured populations make contrasting predictions. Our work uses evolution experiments and numerical simulations to show that spatial structure can impact the pace of adaptive evolution. Certain topologies, specifically a four-patch star with bidirectional migration through a central hub to each of three "leaf" populations, can accelerate the rate at which a beneficial mutation spreads through a population relative to an unstructured, well-mixed population. The cause of this acceleration is a reduced probability that beneficial mutations are stochastically lost when rare because they get concentrated in the central hub and then disperse outwards to the leaves. Our results offer the first experimental support for models of adaptive evolution in space based on evolutionary graph theory, may help understand the spread of invasive species or pathogens, and could be used in industrial settings to selectively enrich desired traits or biomolecules of interest. [ABSTRACT FROM AUTHOR]

The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples. However, when the cluttering is nonlinear in a spatial-Doppler profile, it will cause an off-grid effect and reduce the sparse recovery performance. A meshless search using a meta-heuristic algorithm (MH) can completely eliminate the off-grid effect in theory. Therefore, genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and grey wolf optimization (GWO) methods are applied to SR-STAP for selecting exact clutter atoms in this paper. The simulation results show that MH-STAP can estimate the clutter subspace more accurately than the traditional algorithm; PSO-STAP and GWO-STAP showed better clutter suppression performance in four MH-STAP methods. To search for more accurate clutter atoms, PSO and GWO are combined to improve the method's capacity for global optimization. Meanwhile, the fitness function is improved by using prior knowledge of the clutter distribution. The simulation results show that the improved PSO-GWO-STAP algorithm provides excellent clutter suppression performance, which solves the off-grid problem better than does single MH-STAP. [ABSTRACT FROM AUTHOR]

There are several unsolved problems in federated learning, such as the security concerns and communication costs associated with it. Differential privacy (DP) offers effective privacy protection by introducing noise to parameters based on rigorous privacy definitions. However, excessive noise addition can potentially compromise the accuracy of the model. Another challenge in federated learning is the issue of high communication costs. Training large-scale federated models can be slow and expensive in terms of communication resources. To address this, various model pruning algorithms have been proposed. To address these challenges, this paper introduces a communication-efficient, privacy-preserving FL algorithm based on two-stage gradient pruning and differentiated differential privacy, named IsmDP-FL. The algorithm leverages a two-stage approach, incorporating gradient pruning and differentiated differential privacy. In the first stage, the trained model is subject to gradient pruning, followed by the addition of differential privacy to the important parameters selected after pruning. Non-important parameters are pruned by a certain ratio, and differentiated differential privacy is applied to the remaining parameters in each network layer. In the second stage, gradient pruning is performed during the upload to the server for aggregation, and the final result is returned to the client to complete the federated learning process. Extensive experiments demonstrate that the proposed method ensures a high communication efficiency, maintains the model privacy, and reduces the unnecessary use of the privacy budget. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggle. We can incorporate efficient strategies such as surrogate-assisted optimisation to address the challenges; however, implementing inter-process communication for surrogate-based model training is difficult. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. We first test the framework on a set of benchmark optimisation problems and then apply to a geoscientifc model that features landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computationally time while retaining optimisation solution accuracy through the use of surrogates in a parallel computing environment. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in better understanding of paleoclimate and geomorphology. [ABSTRACT FROM AUTHOR]

Multi-objective problems have gained much attention during the last decade. To balance the diversity and the convergence of the multi-objective differential evolution algorithm (MODE), an improved MODE is proposed based on the affinity propagation clustering (APC) and the non-dominated count approach in this paper. The proposed algorithm is referred to as AP-MODE, which improves the search efficiency by utilizing the affinity propagation approach to find out the population distribution structure for guiding search. In addition, mating restriction probability is used to select parent individuals for recombination from the neighborhoods or the whole population. Meanwhile, the mating restriction probability is updated according to the non-dominated count approach at each generation. This proposed algorithm is verified by comparing it with some state-of-the-art multi-objective evolutionary algorithms, and the simulation results on DTLZ test problems indicate that AP-MODE can efficiently achieve two goals of multi-objective optimization, i.e., the convergence to actual Pareto front and uniform spread of individuals along Pareto front. [ABSTRACT FROM AUTHOR]

In a competitive scenario, to effectively utilise the resources and battery power, the communication and control update has to be minimised to a great extent. In this work, an event-triggered coupled controller with linearly varying event threshold is proposed for setpoint tracking of USV, which optimally utilises the resources with the help of a simple interactive control loop in a classical control framework. The gains of coupled controller are optimised using differential evolution (DE) algorithm. This research analyses the effect of various classes of event thresholds on the number of triggering instants and proposes a linear event threshold (LET) for a reduced number of control triggers. In addition, the relative percentage difference in energy consumption of the USV for different cases of event-triggered control are compared with that of the USV without event-triggered control. The simulation results establish the superiority of the linear event-triggered control in reducing the control update significantly with satisfactory performance in setpoint tracking without compromising stability. [ABSTRACT FROM AUTHOR]

The development of large-scale sustainable energy has affected the security of electricity systems. Virtual power plant (VPP) realize multi-energy synergistic complementation and efficient operation mainly with electricity. The paper proposed a flexible resource dynamic aggregation regulation method of VPP to ensure more renewable energy generation, which enables the efficient operation of multiple energy sources with electricity as the main source through collaborative and complementary measures. Firstly, the operating mode of VPP under market mechanisms is proposed, and the operating mechanism for VPP's participation in the market is established. Secondly, the types of flexible resources are identified, a cooperative game relationship between multiple autonomous agents within the power grid and VPP is formed to support an intelligent control method for VPP considering the complementarity of multiple energy sources and carbon emissions. Finally, a novel inverse cotangent compound differential evolution (NICCDE) algorithm is proposed by combining the innovative composite differential evolution algorithm with the tangent function, which realizes the flexible synergistic utilization of multiple energy sources. and reduces the training cost of the algorithm. Case studies from the conducted case study demonstrate the significant cost reduction achieved by the proposed method in comparison to conventional approaches when applied to VPP. Moreover, the utilization of this method enhances the precision and comprehensiveness of the search outcomes, thereby augmenting the overall effectiveness of the VPP system. [ABSTRACT FROM AUTHOR]

Multi‐objective optimization of dimethyl oxalate (DMO) hydrogenation to produce ethylene glycol (EG) is performed for three problems with four different objectives: maximization of DMO conversion and minimization of energy cost for problem 1; maximization of productivity and minimization of energy cost for problem 2; maximization of productivity and minimization of side product for problem 3; minimization of bare module and energy cost for problem 4; maximization of productivity and minimization of bare module for problem 5, and maximization of productivity and minimization of bare module and energy cost for problem 6. Multi‐objective differential evolution with ranking‐based mutation operator is applied to tackle the optimization problem with ASPEN simulator as model‐based optimization for EG production. The plots of decision variables denote that the optimal solution is strongly dependent on temperature. [ABSTRACT FROM AUTHOR]

The focus of this study is a control system with a nonlocal state that is driven by a neutral stochastic integro‐differential evolution hemivariational inequality known as Hilfer fractional (HF). In order to demonstrate effective requirements for the existence of mild solutions to the relevant control system, we first make use of the features of the extended Clarke subdifferential and a fixed point theorem for multivalued functions. We next prove that there are optimal state‐control sets that are induced by HF neutral stochastic integro‐differential evolution hemivariational inequalities with a nonlocal condition by using limited Lagrange optimal structures. The distinctiveness of the control system's solutions is not taken into consideration while developing optimal control outcomes. At last, an example is employed to go over the major findings. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of a class of impulsive nonlinear evolution partial differential equations. We give new results about existence and multiplicity of global classical solutions. The method used is based on the use of fixed points for the sum of two operators. Our main results will be illustrated by an application to an impulsive Burgers equation. [ABSTRACT FROM AUTHOR]

Although the conventional differential evolution (DE) localization algorithm provides reasonable localization results, its improvement in localization performance is limited. Considering the distance information between the tags being an effective way to improve localization performance, this paper proposes a novel cooperative DE-based localization algorithm by presenting the localization process and the fitness function, which contains the distance information between the tags. Specifically, our method first utilizes the conventional DE algorithm to provide the initial values of the tags. Then, according to the initial values, the distance information between the tags is fully utilized to improve the localization performance. The simulation results demonstrate our algorithm's effectiveness in improving the localization performance and simultaneously realizing the localization of all tags. [ABSTRACT FROM AUTHOR]

This study uses different feature selection methods in the field of business failure prediction and tests the capability of Genetic Programming (GP) as an appropriate classifier in this field. The prediction models categorize the insolvency/non-insolvency of a firm one year in advance from a large set of financial ratios. Different selection strategies based on two evolutionary algorithms were used to reduce the dimensionality of the financial features considered. The first method considers the combination between the global search provided by an evolutionary algorithm (differential evolution) with a simple classifier, together with the possible use of classical filters in a first step of feature selection. Secondly, genetic programming is used as a feature selector. In addition, these selection approaches will be compared when GP is used exclusively as a classifier. The results show that, when using GP as a classifier method, the proposed selection method with GP stands out from the rest. Moreover, the use of GP as a classifier improves the results with respect to other classifier methods. This shows an added value to the use of GP in this field, in addition to the interpretability of GP prediction models. [ABSTRACT FROM AUTHOR]

In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are discussed. On the other hand, methods based on protein conformational energy minimization and with different search strategies are analyzed. In this latter case, our methods based on a memetic combination between differential evolution and the fragment replacement technique are included, incorporating also the possibility of niching in the evolutionary search. Different proteins have been used to analyze the pros and cons in both approaches, proposing possibilities of integration of both alternatives. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithms tend to be limited to local optimization when solving complex optimization problems. Different iteration schemes lead to different convergence speeds. In this paper, we mainly use the dynamic mutation parameter FS to improve the DE algorithm. Based on two ideas, a total of seven DE schemes are proposed to optimize the DE algorithm. We test the performance of the improved DE scheme on 56 test functions. Experiments show that the improved DE algorithm is better than the baseline DE algorithm in terms of accuracy, convergence and8 convergence speed. [ABSTRACT FROM AUTHOR]

A differential evolution (DE) algorithm with superior-inferior mutation scheme (SIDE) is proposed to solve global optimization problems over continuous space. Firstly, a superior-inferior mutation scheme is introduced based on the superior individual and inferior individual. Secondly, the dynamic adjustment strategy of exploration factor for balancing superior individual and inferior individual is proposed, which indicates how much weight to place on local exploitation and global exploration. They are integrated to DE in different milestones of optimization to balance exploration and exploitation of the search space and can alleviate the premature convergence. In order to verify the performance of SIDE, a set of numerical experiments on 32 benchmark functions are executed for performance comparison with 8 advanced DE variants and 4 non-DE-based algorithms for 30, 50 and 100 variables. The experimental results indicate that SIDE is much better than compared DE algorithms in terms of optimization quality. Furthermore, SIDE has the best adaptability to high dimensional problems. [ABSTRACT FROM AUTHOR]

Under the retailing environment, shelf space-based competition between items with different brands has drawn much attention from practitioners and researchers, the decision for low-temperature stored items on limited shelf spaces are compounding this challenge. This paper discusses the shelf display space partition assignment decision for low-temperature dairy items with two-phase demand in remaining shelf-life to maximize the profit of the shelf. Two-phase demand is assumed for the first time based on the knowledge that consumers' perception on the quality of the dairy item is unequal to its true shelf-life. Moreover, considering shelf partitions in different heights carry different sale gaining potentials, three profit weights are given to the partitions. Herein, a new research model named as SPSPA of shelf profit space partition assignment for the low-temperature stored dairy items is constructed. To effectively solve the proposed SPSPA, a hybrid firefly particle swarm optimization (HFPSO) is developed combing the superiority of particle swarm optimization (PSO) in coarse search and firefly algorithm (FA) in fine search. Numerical experiments have been performed on small- and large-scale cases to verify the performance of the proposed HFPSO algorithm by comparing with genetic algorithm, differential evolution (DE), PSO, and FA based on which management insights are obtained for the shelf space assignment decision. [ABSTRACT FROM AUTHOR]

Data hiding along with the security plays a vital role in the wireless network. In recent years, object detection, Feature Extraction (FE) and correlation based technique are developed to predict the pixels that is suitable for embedding. However, traditional data hiding method still suffered in the selection of less distortion pixels. To overcome these shortcomings, this method proposes (i) Moving Object (MO) detection, (ii) FE, (iii) data hiding, and (iv) data encryption. Initially, the MO in the video are detected using optical flow and tracking the weight of MO. Next, data hiding features are extracted using Optimization Fruit fly Algorithm (OFA) with Differential Evolution (DE) and Opposition Based Learning (OBL) algorithm. Then, a modified data hiding techniques based on correlation, regression and pixel segmentation with Prediction Error (PE) techniques are used for improving the performance. Finally, the hidden data is encrypted using Binary Tree Structure (BTS) to provide additional layer of security. Experimental result shows the proposed method is superior to other state-of-the-art methods in terms of Peak-Signal-to-Noise Ratio (PSNR), embedding capacity and security analysis. [ABSTRACT FROM AUTHOR]

These days image processing technology has evolved a lot. Digital media can be distorted easily due to the availability of editing tools. So, watermarking can be used to protect digital media. This article proposes the Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based effective and optimal robust image watermarking technique using the differential evolution (DE) algorithm, which is invariant against rotation, scaling, and translation (RST). The watermark embedding process uses a 3-level DWT and SVD. Here, speeded-up robust features (SURF) are also used to extract feature points and match features. The RST-attacked image can be restored using these features by applying the M-estimator SAmple Consensus(M-SAC) algorithm. DE algorithm is used to estimate the optimized value of the scaling factor. The optimized scaling factor is assisted in acquiring the highest possible robustness with enhanced imperceptibility. After experimentation, results illustrate the proposed technique is invariant to the RST, and signal processing attacks. [ABSTRACT FROM AUTHOR]

In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively. [ABSTRACT FROM AUTHOR]

This paper presents the development of a web-based course timetabling system based on an enhanced genetic algorithm. The enhanced method utilizes a heuristic mutation which concentrates on mutating the infeasible genes to improve the algorithms' exploration and exploitation capability. The method was implemented using a free and open-source application and can be accessed online. Based on the actual datasets from Caraga State University, the enhanced method optimized the use of classroom resources by using a smaller number of rooms. The generated timetable is more efficient as it satisfies not just hard constraints, which are conflicting schedules, but also soft constraints. [ABSTRACT FROM AUTHOR]

The unequal area facility layout problem (UAFLP) is a problem in industrial engineering that aims to minimize material handling costs between a group of unequal-shaped facilities. In this study, Differential Evolution (DE) with continuous variables and Particle Swarm Optimization (PSO) are employed to solve the UAFLP. This study also compares the performance of the DE method (never used to solve UAFLP) and the PSO method (frequently used to solve facility layout problems). The experimental results demonstrate that PSO is effective in solving small-sized datasets, while DE works well in solving datasets with larger areas. [ABSTRACT FROM AUTHOR]

The combined use of LiDAR (Light Detection And Ranging) scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics. This information can be used in many ways in forest mapping, scenario analyses, and forest management planning. This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation (kNN stands for “k nearest neighbors”). In this method, data are imputed to a certain location from k field-measured sample plots, which are the most similar to the location in terms of LiDAR metrics and topographic variables. Weighted multidimensional Euclidean distance was used as the similarity measure. The study tested two different methods to optimize the distance measure. The first method optimized, in the first step, the set of LiDAR and topographic variables used in the measure, as well as the transformations of these variables. The weights of the selected variables were optimized in the second step. The other method optimized the variable set as well as their transformations and weights in one single step. The two-step method that first finds the variables and their transformations and subsequently optimizes their weights resulted in the best imputation results. In the study area, the use of three to five nearest neighbors was recommended. Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation. The optimal distance measure always included both LiDAR metrics and topographic variables. The study showed that the optimal similarity measure may be different for different regions. Therefore, it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors. [ABSTRACT FROM AUTHOR]

Dynamic differential annealed optimization (DDAO) is a recently developed physics-based metaheuristic technique that mimics the classical simulated annealing mechanism. However, DDAO has limited search abilities, especially when solving complicated and complex problems. A unique variation of DDAO, dubbed as mDDAO, is developed in this study, in which opposition-based learning technique and a novel updating equation are combined with DDAO. mDDAO is tested on 10 different functions from CEC2020 and compared with the original DDAO and nine other algorithms. The proposed mDDAO algorithm performance is evaluated using 10 numerical constrained functions from the recently released CEC 2020 benchmark suite, which includes a variety of dimensionally challenging optimisation tasks. Furthermore, to measure its viability, mDDAO is employed to solve feature selection problems using fourteen UCI datasets and a real-life Lymphoma diagnosis problem. Results prove that mDDAO has a superior performance and consistently outperforms counterparts across benchmarks, achieving fitness improvements ranging from 1% to 99.99%. In feature selection, mDDAO excels by reducing feature count by 23% to 79% compared to other methods, enhancing computational efficiency and maintaining classification accuracy. Moreover, in lymphoma diagnosis, mDDAO demonstrates up to 54% higher average fitness, 18% accuracy improvement, and 86% faster computation times. [ABSTRACT FROM AUTHOR]

Unstable processes are challenging to control because they have one or more positive poles that produce unrestrained dynamic activity. Controlling such unstable plants becomes more challenging with the occurrence of the delay. This article presents a novel direct synthesis based sliding mode controller design for unstable second order plus dead-time processes. A sliding surface with three parameters has been considered. The continuous control law, which is responsible for maintaining the system mode to the desired sliding surface mode, has been obtained using the direct synthesis approach. The discontinuous control law parameters have been obtained using the differential evolution optimization technique. A desired reference model is considered for the direct synthesis method, and an objective function is constituted in terms of performance measure (integral absolute error) and control effort measure (total variation of controller output) for the optimization approach. Illustrative examples show the superiority of the proposed controller design method over recently reported literature, especially in terms of load rejection. The proposed controller approach is further extended to control the temperature of a nonlinear chemical reactor. Furthermore, the robustness of the proposed controller is also investigated for plant parametric uncertainty. [ABSTRACT FROM AUTHOR]

Purpose: A control scheme is presented to attenuate the vibration of the large flexible space structures (LFSS), which includes the optimization of actuator positions and the design of the robust vibration control algorithm. In contrast to the existing literature, the optimization criterion does not depend on the controller parameters, and robust adaptive dynamic programming (RADP) can effectively suppress the vibration of the LFSS under mixed continuous disturbance and model uncertainties. Method: First, the optimization criteria of optimal placements of the actuator, which can maximize the actuating efficiency, decoupling input voltage and modal contribution factor, are proposed. On this basis, the adaptive parameter differential evolution (APDE) algorithm is employed to speed up the optimization process. Subsequently, the RADP is designed to enhance the control effect and robustness of the system under external disturbances and model uncertainties. Finally, the RADP-based vibration control performance under optimal and random placement layouts is compared. Results and Conclusion: The effectiveness of the proposed optimization criterion, APDE method and RADP-based vibration control algorithm is verified by simulation. The proposed optimization criteria save the actuator energy consumption and enhance the adaptability of the optimization. The optimal actuator positions can be swiftly identified by the APDE algorithm. In addition, the RADP-based vibration control algorithm improves the control effect, reduces decay time and enhances the robustness of the LFSS under mixed continuous excitation and model uncertainties. [ABSTRACT FROM AUTHOR]

Due to the unreasonable connectivity of wireless sensor networks, there are errors in calculating the distance between the location-unaware nodes and the location-aware nodes. Therefore, a novel method combining improved particle swarm optimization and improved differential evolution (IPSO-IDE) is proposed. Firstly, the learning factor of the particle swarm optimization method is modified, and the disturbance term is added to prevent premature, thus strengthening its exploration ability. Then, the adaptive mutation and crossover mechanism are applied to the differential evolution method so that each chromosome can mutate and cross adaptively with individual fitness and average fitness, to improve its exploitation ability. Finally, the improved particle swarm optimization (IPSO) and differential evolution method (IDE) are run in parallel in the node positioning model. After fixed number of iterations, the optimal individual information of the two methods is shared to obtain the IPSO-IDE method. Therefore, IPSO-IDE has the advantages of both. The results show that the IPSO-IDE algorithm not only has some advantages in time complexity, but also improves the positioning accuracy by 27.2%, 8.7%, 4.5% and 5.7% respectively, compared with particle swarm optimization with a variable neighborhood algorithm, modified particle swarm optimization algorithm, hybrid particle swarm optimization and differential evolution algorithm and artificial bee colony algorithm. [ABSTRACT FROM AUTHOR]

The cracking behavior of lignite during drying–wetting cycles impacts the efficiency of coal mining, underground coal gasification, and coalbed methane development. Lignite is known to have high water content, and it exhibits strong hydrophilic properties, resulting in a weak and rigid gel structure caused by water interactions with coal matrix molecules. Even at ambient temperatures, dehydration causes violent shrinkage of the lignite matrix and numerous fractures. Due to the shallow burial depth of lignite, it is often mined in open pits, and it often undergoes cycles of dehydration and rewetting in the open-air environment, resulting in significant changes to its physical structure and mechanical properties. Thus, a detailed characterization of this evolution process is necessary. In this study, lignite samples with different compositional properties were collected from the Shengli Coalfield in the Erlian Basin, and their physical structure and mechanical properties were characterized using nondestructive testing methods such as nuclear magnetic resonance, environmental scanning electron microscopy, X-ray computed tomography, and atomic force microscopy. The results provide insights into the impact of material composition on the syneresis behavior and destruction process of lignite. Upon removal of water from the coal matrix, rapid shrinkage occurs, resulting in the simultaneous formation of numerous fractures. Significant differences were observed in the contraction characteristics and fracture propagation patterns among the various lignite lithotypes. Xylite lignite exhibited the highest degree of contraction following dehydration, with the macropore reduction rate being the most significant. Matrix lignite showed a lower degree of syneresis compared to xylite lignite, while fusain-rich lignite demonstrated the weakest syneresis ability. The homogeneous xylite lignite shrinks as a whole after dehydration, and its fractures are long and straight, with good orientation. Fractures formed in the detrital humic groundmass of matrix lignite are short, convoluted, and poorly oriented. Fusain-rich lignite fractures are thin, and straight, with the best orientation. The expansion of fractures can cause geological disasters such as land subsidence, collapse, and landslides. Fusain hinders fractures' expansion and acts as a "skeleton" to support the lignite structure. The presence of fusain is advantageous to maintaining the steadiness of the lignite. The differential evolution of the physical structure of lignite during the drying–wetting cycles, which aids to avert and mitigate disasters linked to dehydration cracking in different lignite-related engineering geological fields, is discussed in this paper. [ABSTRACT FROM AUTHOR]

In this article, a new hybrid improved differential evolution and Nelder-Mead (IDE-NM) is introduced for optimizing the multi-objective machining process during the turning operation under three modes of lubrication conditions. The fitness functions are the tangential cutting force, the surface roughness, and the cutting power. Five mixed design variables are considered in the optimization procedure including the cutting speed, feed rate, and depth of cut, mode of lubrication, and the type of cutting material. The mathematical expressions of the three objectives are created based on experimental results and modeled using the artificial neural network (ANN). In the first step, the proposed method is examined by solving seven mechanical engineering design problems. The comparison results illustrate that the IDE-NM algorithm outperforms other state-of-the-art optimization methods considered in the literature. Moreover, for the turning operation problem, the results of IDE-NM are compared with those of four recent metaheuristics. The results show that the proposed method outperforms the four compared algorithms in terms of robustness, high success rate, and can provide effective solutions. [ABSTRACT FROM AUTHOR]