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The integrated energy system (IES) with hydrogen storage has become one of the most important developments in multi-energy coupling field, where the severe conflict of interests between different entities leads to great challenges to the economy and low-carbon operation. A multi-objective robust dynamic pricing and operation strategy optimization method based on the Stackelberg game is proposed for the hydrogen-containing energy storage (HES) IES. Firstly, the HES-IES trading framework is established based on the introduction of an integrated energy operator (IEO) and a load aggregator (LA). Secondly, a multi-objective robust Stackelberg game model is developed with the IEO as the leader and the LA as the follower, considering the minimization of operating costs and carbon emissions of the IEO and the minimization of integrated energy costs of the LA as the objectives. Finally, the compromise planning and the max-min fuzzy are addressed to solve the multi-objective model, which adopts the adaptive differential evolution (ADE) algorithm. In addition, the robust optimization (RO) with adjustable coefficients is employed to tackle uncertainties of source and load. The results show that this method can effectively balance the operating costs and carbon emissions of the system, improve the benefit of the IEO, reduce the costs of the LA, and avoid the uncertainty risk. Compared with traditional algorithms, the ADE algorithm has significant advantages in the number of iterations and solving time. In summary, the multi-objective robust dynamic pricing and operation strategy optimization proposed in this paper could effectively achieve the benefit balance between the IEO and the LA, also further improve the economy and robustness of the system and reduce carbon emissions. [Display omitted] • A multi-objective robust dynamic pricing and operation strategy optimization method based on Stackelberg game is proposed. • The compromise planning, max-min fuzzy method and adaptive differential evolution algorithm are adopted to solve the model. • The robust optimization with adjustable coefficients is employed to deal with the source-load uncertainty. • The economy and robustness of the system are improved, and carbon emissions are reduced. [ABSTRACT FROM AUTHOR]

This work focuses on minimum-time low-thrust orbit transfers from a prescribed low Earth orbit to a specified low lunar orbit. The well-established indirect formulation of minimum-time orbit transfers is extended to a multibody dynamical framework, with initial and final orbits around two distinct primaries. To do this, different representations, useful for describing orbit dynamics, are introduced, i.e., modified equinoctial elements (MEE) and Cartesian coordinates (CC). Use of two sets of MEE, relative to either Earth or Moon, allows simple writing of the boundary conditions about the two celestial bodies, but requires the formulation of a multiple-arc trajectory optimization problem, including two legs: (a) geocentric leg and (b) selenocentric leg. In the numerical solution process, the transition between the two MEE representations uses CC, which play the role of convenient intermediate, matching variables. The multiple-arc formulation at hand leads to identifying a set of intermediate necessary conditions for optimality, at the transition between the two legs. This research proves that a closed-form solution to these intermediate conditions exists, leveraging implicit costate transformation. As a result, the parameter set for an indirect algorithm retains the reduced size of the typical set associated with a single-arc optimization problem. The indirect heuristic technique, based on the joint use of the necessary conditions and a heuristic algorithm (i.e., differential evolution in this study) is proposed as the numerical solution method, together with the definition of a layered fitness function, aimed at facilitating convergence. The minimum-time trajectory of interest is sought in a high-fidelity dynamical framework, with the use of planetary ephemeris and the inclusion of the simultaneous gravitational action of Sun, Earth, and Moon, along the entire transfer path. The numerical results unequivocally prove that the approach developed in this research is effective for determining minimum-time low-thrust Earth–Moon orbit transfers. • Earth–Moon minimum-time low-thrust orbit transfers are investigated. • Problem is formulated as a multiple-arc optimal control problem. • Costate transformations are used, related to different state representations. • Indirect heuristic algorithm with stratified objectives is employed. • Numerical results prove effectiveness of the optimization methodology. [ABSTRACT FROM AUTHOR]

Supercritical water gasification (SCWG) has been utilized for producing hydrogen from organic wastes, which is associated with sustainable development. Nevertheless, identifying the optimal SCWG conditions and appropriate catalysts for diverse waste materials to yield hydrogen-rich syngas is consistently a laborious and costly endeavor. The current research presents a comprehensive framework that combines machine learning models (Decision Tree, Ensembled Learning Tree, Support Vector Machine, and Gradient Boost Regression) with Differential Evolution Optimization (DEO) to predict and optimize hydrogen production from Supercritical Water Gasification (SCWG) using 24 different biomass characteristics and process parameters.The findings indicate that ELA-DEO emerges as the preferred method for forecasting hydrogen yield (R2 test = 0.95, RMSE Test = 0.091), demonstrating its effectiveness in handling intricate variable-target relationships. Conversely, Support Vector Machine (SVM) displayed weaker performance with an R2 Test of 0.73 and RMSE Test of 0.116. In the SHAP feature importance analysis, temperature, catalyst amount, catalyst type, hydrogen and oxygen were highlighted as important parameters affecting the process. In addition, by fine-tuning the machine learning hyperparameters, the DEO optimization method was used to maximize the production of H 2. The optimized ELA-DEO model was used to identify the ideal features and conditions applicable to our experimental setup. Based on these findings, a recommended biomass table was finally formulated, which was validated by the ELA-DEO model. According to our laboratory results, Dunaliella salina produced 12 mmol of hydrogen with a 35% selectivity. Black liquor with 3% (wt) wood has the capacity to produce 17.21 mmol of hydrogen. Also, when crude glycerol was reacted with RuCl 3 , 18.7 mmol of hydrogen were generated. However, Dunaliella salina produced a 64% mole fraction of total gas output, which matched the maximum value predicted by our ELA-DEO models. Schematic diagram of the machine learning based optimization for H 2 -rich syngas production. [Display omitted] • An innovative ensemble forecasting method evolved to estimate SCWG (Supercritical water gasification) performance. • The DEO (Differential Evolution Optimization) methodology optimizes H2 yields using SHAP (SHapley Additive exPlanations) value and feature importance. • Offer desired biomass to maximize hydrogen yield, which adopts the temperature till 430C and pressure 22–24 MPa. • PDP (Partial Dependence Plot) facilitates comparative study to determine the best value for a feature. [ABSTRACT FROM AUTHOR]

Marketing has proven to be an important asset to every business, and the growth of digital platforms has made it even more crucial, which makes the use of tools to measure and guide advertising strategies almost mandatory. In this context, Marketing Mix Modeling (MMM) has been seen as a powerful tool for backing up this process. However, the sole use of MMM does not help with complex budget allocation decisions, and some of the most used solutions for this modeling lacks on effective or intuitive solutions for this kind of problem. That said, the subject of this article is the proposal of a budget-allocation method consisting of a two-stage differential evolution optimization algorithm that considers the carryover and saturation effects for media channels in the calculations. The model was evaluated on a counterfactual scenario, aiming at the improvement of the Return on Ad Spend over a real scenario of an open database and showed positive results with a significant hypothetical increase in the financial result of the given scenario, proving to be a viable tool for this use. [ABSTRACT FROM AUTHOR]

In real terrain and dynamic obstacle scenarios, the complexity of the 3D UAV path planning problem greatly increases. Thus, to procure the optimal flight path for UAVs in such scenarios, an augmented Artificial Gorilla Troops Optimizer, denoted as OQMGTO, is proposed. The proposed OQMGTO algorithm introduces three strategies: combination mutation, quadratic interpolation, and random opposition-based learning, aiming to enhance the ability to timely escape from local optimal path areas and rapidly converge to the global optimal path. Given the flight distance, smoothness, terrain collision, and other five realistic factors of UAVs, specific constraint conditions are proposed to address complex scenarios, aiming to construct a path planning model. By optimizing this model, OQMGTO algorithm solves the path planning problem in complex scenarios. The extensive validation of OQMGTO algorithm on CEC2017 test suite enhances its credibility as a powerful optimization tool. Comparison experiments are conducted in simulated terrain scenarios, including six multi-obstacle terrain scenarios and three dynamic obstacle scenarios. The experimental findings validate OOMGTO algorithm can assist UAV in searching for excellent flight paths, featuring high safety and reliability characteristics, which confirms the superiority of OOMGTO algorithm for path planning in simulated terrain scenarios. Furthermore, in four flight missions carried out in real terrains, OQMGTO algorithm demonstrates superior search performance, planning smooth trajectories without mountain collision. • A novel algorithm (OQMGTO) is proposed for 3D UAV path planning. • OQMGTO adopts combined mutation to overcome susceptibility to local optima. • OQMGTO uses a quadratic interpolation to enhance the exploitation capabilities. • OQMGTO achieves better optimization performance on CEC2017 test suite. • OQMGTO exhibits the superiority of path planning in simulated and real terrains. [ABSTRACT FROM AUTHOR]

One of the most challenging robotic manipulator designs is finding an appropriate balance between the shaking force and shaking moment because this reduces vibrations. Several approaches have been introduced in the last decades; nevertheless, some assumptions must be established to make such a balance. In this paper, a dynamic balancing approach is proposed. The main novelty is the no dependence on specific trajectories to be executed by the manipulator, which allows finding a design with a similar tradeoff in the balancing under robot configuration changes. Also, the proposal incorporates mass distribution and link shape in a single design procedure. The proposal is stated as a constrained nonlinear optimization problem and applied to a hybrid serial-parallel robotic manipulator. The use of different bio-inspired algorithms and one gradient one in the solution of the balancing problem reveals that differential evolution finds the most suitable design. Besides, comparative simulation results of the obtained design with other design approaches show that the obtained design presents the most suitable tradeoff between the shaking force and the shaking moment when the manipulator executes tasks with different operating velocities. [ABSTRACT FROM AUTHOR]

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic algorithm that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity and an imbalance between exploration and exploitation. In this paper, an improved honey badger algorithm called ODEHBA is proposed to improve the performance of basic HBA. Firstly, an improved orthogonal opposition-based learning technique is employed to assist population in escaping local optimum. Secondly, differential evolution is utilized to ensure the enrichment of population diversity and to enhance convergence speed. Finally, the exploration capability of ODEHBA is boosted by an equilibrium pool strategy. To validate the efficacy of proposed ODEHBA, it is compared with 13 well-known metaheuristic algorithms on CEC2022 benchmark test sets. Friedman test and Wilcoxon rank-sum test are utilized to assess the performance of ODEHBA. Furthermore, three engineering design problems and Internet of Vehicles (IoV) routing problem are applied to validate the capability of ODEHBA. The simulation results demonstrate that ODEHBA excels in solving complex numerical problems, engineering design, and IoV routing problems. This holds significant practical implications for cost reduction and improved resource utilization. [ABSTRACT FROM AUTHOR]

Multi-objective optimization algorithms are widely employed in the optimization of Dividing-Wall Column (DWC). However, due to the inherent complexity of DWC design problems, the choice of different optimization algorithms significantly influences the results. This study aims to investigate the applicability of various multi-objective optimization algorithms in the design of ternary DWCs. Three multi-objective algorithms including multi-objective genetic algorithm (MOGA), multi-objective differential evolution with tabu list algorithm (MODE-TL), and multi-objective particle swarm algorithm (MOPSO) are used to solve the optimization problem of six configurations of dividing-wall columns including direct dividing-wall column, indirect dividing-wall column, Petlyuk dividing-wall column, and three configurations of liquid-only transfer dividing-wall column (LTS), and the optimization results are compared and analyzed. The results show that MOPSO has the worst performance and is relatively less applicable to dividing-wall columns. The MOGA has great global exploration ability but is sensitive to structure complicity. The MODE-TL algorithm can make balance between exploration and exploitation and is less sensitive to structure complicity and the number of variables. Finally, according to the comprehensive performance of the three algorithms, we conclude that MODE-TL has the highest applicability in ternary dividing-wall column optimization. [Display omitted] • Six alternative ternary DWCs, including liquid-only transfer DWCs, are designed and optimized. • The performance of three improved optimization algorithms in six configurations is compared and the reasons are analyzed. • The most applicable multi-objective algorithm for ternary DWCs' optimal design is discussed. [ABSTRACT FROM AUTHOR]

The travelling salesman problem (TSP) and its variants have been studied extensively due to its wide range of real-world applications, yet there are challenges in providing efficient algorithms to deal with some of its variants. The multiple travelling salesman problem (MTSP), is the generalization of TSP, which aims to determine m − routes for ' m ' salesmen to cover a set of n − cities exactly once where each route starts and ends at a depot such that the total distance is least. In this, the number of cities in each route of the optimal solution may be distributed disproportionately. This paper presents, a bi-objective MTSP (BMTSP) with the load balancing constraint, where the first objective is to minimize the total travel distance and the second objective minimizes the total time. A metaheuristic based genetic algorithm with tournament selection (GATS) is designed by integrating with mixed strategies, such as flip, swap and scramble in mutation operation to obtain efficient Pareto solution for BMTSP. The computational experiments are carried out on different data sets, which are derived from the TSPLIB. The performance of GATS is compared with different genetic approaches and simulation results show that the proposed GATS obtained improved solutions on some of the benchmark instances. [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 ventilation resistance coefficients of a mine is vital to ventilation system safety management, diagnosis, and intelligentization. Airflow typically serves as the basis for inverting ventilation resistance coefficients. However, the issue of non-uniqueness in conventional nonlinear optimization methods affects the accuracy of inversion. Therefore, this study introduces a novel optimization approach based on deep reinforcement learning (DRL) to invert resistance coefficients. In this methodology, inversion is regarded as a Markov decision process, with the ventilation network solving model (VNSM) embedded within the DRL environment. We design an agent utilizing deep neural networks, which dynamically adjusts the resistance coefficient state variables by interacting with the VNSM to enhance the consistency between theoretical and measured airflow. The consistency corresponds to the agent's reward. The proximal policy optimization is employed to optimize the agent's policy. In field experiment, the MAE between the airflow calculated and the measured airflow is 0.354, with MSE of 0.287, RMSE of 0.536, and MRE of 0.013. Compared with the standard genetic algorithm, differential evolution algorithm, and evolution strategy algorithm, the DRL method shows lower MRE, MAE, RMSE, and MSE values by 23.5%, 15.3%, 14.1%, and 26.4%, respectively. Additionally, DRL method exhibits smaller sensitivity differences for different roadways than others algorithm. [ABSTRACT FROM AUTHOR]

Modification with efficient, stable and low-cost co-catalyst has been considered as a promising way to upgrade photocatalytic property of Mn x Cd 1−x S. Herein, Ni(HCO 3) 2 nanosheets were loaded on polyhedral Mn 0.2 Cd 0.8 S (MCS) surface through an in-situ hydrothermal method. As-prepared MCS/Ni(HCO 3) 2 composites exhibited the boosted H 2 evolution activities. The optimal H 2 evolution rate (17.7 mmol g−1 h−1) with the calculated AQE value (17.5%) at 420 nm was achieved over the MCS/Ni(HCO 3) 2 composite with 10 mol% loading amount of Ni(HCO 3) 2 , which reached 4.0 times of MCS alone. The boosted activity could be ascribed to highly efficient interfacial charge separation and abundant surface active site derived from the structural and functional advantages of MCS/Ni(HCO 3) 2 composite. This work gives a fresh perspective on the design and application of transition metal bicarbonate as co-catalysts towards H 2 evolution. [Display omitted] A novel Mn 0.2 Cd 0.8 S/Ni(HCO 3) 2 composite was fabricated and exhibited the enhanced H 2 evolution activity due to excellent charge separation efficiency and abundant surface active site. • Mn 0.2 Cd 0.8 S/Ni(HCO 3) 2 composite was in-situ constructed. • Interfacial contact between two component accelerated charge separation. • Ni(HCO 3) 2 co-catalysts provided abundant active reaction sites. • Co-catalyst modification was responsible for the enhanced H 2 evolution activity. [ABSTRACT FROM AUTHOR]

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets. [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]

This paper proposes a novel efficient inspired algorithm based on snow avalanches in nature which is named the Snow Avalanches Algorithm (SAA), for solving the benchmark and engineering optimization problems and determining the global solution. The proposed algorithm is modeled using four phases including avalanche due to mountain slope, human factors, weather in the region as well as normal conditions and it has only one control parameter. The advantages of this algorithm are low control parameters, simple structure and also easy implementation. The effectiveness of the SAA algorithm is examined on 23 classic benchmark test functions. Then, the effectiveness of the SAA to achieve accurate results in different aspects is examined and proved on engineering problems including six different cases. The superiority of the SAA to solve the classic benchmark test functions is compared with spotted hyena optimization (SHO), particle swarm optimization (PSO), Aquila optimizer (AO), differential evolution (DE), bat algorithm (BA), dwarf mongoose optimization (DMO), genetic algorithm (GA), artificial bee colony (ABC), and ant colony optimization (ACO). The simulation results provide evidence for the well-organized and efficient performance of the SAA in solving a great diversity of engineering problems. The results demonstrated that the SAA can be more effective than other algorithms to solve the test functions in terms of optimization accuracy and convergence rate. Moreover, the results proved that the SAA obtained more competitive results than the previous methods to solve constrained engineering optimization problems, especially hybrid energy system design as well as economic load dispatch problems. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is critically important for optimising the performance of Intrusion Detection Systems (IDSs) used in Wireless Sensor Networks (WSNs). However, selecting an optimal number of relevant features from massive IDS data sets has been an ongoing FS research problem. Many approaches use Machine Learning (ML), such as nature-inspired population-based and Differential Evolution (DE)-based algorithms for FS. However, the main drawback of DE is that it has a premature convergence issue, which leads to false convergence. In this paper, a novel mutation strategy is proposed to alleviate premature convergence and to develop a modified DE-based FS algorithm called DE with maturity extension (DE-ME). The novel mutation is achieved by changing the mutation factor in the DE\rnd\1 based on K-Nearest Neighbour (KNN) as the most efficient implementation without adding further complexity to the design process. The proposed DE-ME algorithm improves the FS operation with significant performance improvements in terms of overcoming premature convergence and increasing accuracy for selection. The DE-ME algorithm was used to select the most effective features from 41 features in the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset. The accuracy of DE-ME is high at 99.66 %, its False Positive Rate (FPR) is low at 0.464 %, and the true positive rate (TPR) achieved is 99.80 %. In addition, the number of features selected by DE-ME is 6 out of 41 features in the dataset. [ABSTRACT FROM AUTHOR]

In this study, we solve fractional order PDEs with free parameters that regulate wave motion in a low-pass electrical transmission line equation (LPET) using the unified technique. To convert the governing equation into an ordinary differential equation (ODE), we have used a simple linear fractional transformation. The unified technique can therefore be used to find various single and extracting wave solutions to the equation of attention. Using Maple-18 software, all of the established solutions are validated and verified. By selecting appropriate values for the fractional order and free parameters, the two dimensional (2D) and three dimensional (3D) profiles of some of the assimilation solutions demonstrating kink, singular kink, cross kink, bright-dark kink, lump type kink, anti-kink, dark-anti kink, singular periodic, periodic, singular soliton solutions are displayed. The effect of free parameters and fractionality on the dynamical properties of the precise and exploited solutions are graphically illustrated and discussed in detail. We have also compared our results to those found in the literature review. Wherever possible, the judgment demonstrates no significant differences between our realized and published solutions. The results of this investigation show that the adopting technique is both productive and powerful in extracting wave solutions to nonlinear evolution problems that arise in the areas of applied mathematics, mathematical physics, and engineering. [ABSTRACT FROM AUTHOR]

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]

Daily increases in electricity prices accompany daily increases in energy consumption and use. An effective load-balancing scheduling system is necessary for the lowest cost of use and the lowest cost. Despite these devices having a significant capacity for power consumption, they must find a means to balance the load at a low price. Even if lowering the voltage is challenging, it is possible to do it at the lowest cost. Hybrid Whale Differential Evolution (HWDE) is a new optimization method that combines the well-known approaches of the Whale Optimization Algorithm (WOA) and Enhanced Differential Evolution (EDE). By balancing the required Real-Time Price (RTP) and Critical Peak Price (CPP) loads, WOA and EDE capabilities can save costs and ensure the device receives sufficient voltage. The three most recent performance indicators are kWh per charge, energy usage, and the maximum-average ratio. Existing models are evaluated according to their expenses (in rupees), energy consumption, cost per kilowatt-hour, and total cost. All simulation results indicate that HWDE is the optimal solution in every circumstance. In MATLAB simulations, HWDE consistently outperforms its rivals. [ABSTRACT FROM AUTHOR]

The space-based gravitational wave observatory mission consists of three drag-free controlled spacecraft, which form an equilateral triangle configuration. Due to the injection error, the configuration will deviate from the originally designed one, and the injection configuration cannot keep stability for a long term, which will affect the performance of the gravitational wave detection. In this study, we propose a method for reconfiguration using multiple-revolution Lambert solutions. In addition, assuming that the real-time data of spacecraft's motion states can be obtained with high accuracy and the propulsion system can ensure the magnitude and accuracy of maneuvers, and taking the cost of reconfiguration and configuration stability requirements into account, a novel method of redesigning configuration based on differential evolution algorithm is proposed. Finally, the numerical simulations show that the redesigned configuration not only can reduce the maneuver cost significantly, but also can meet the requirements of long-term stability requirements. [ABSTRACT FROM AUTHOR]

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 this work, a new adaptive numerical method is proposed for solving nonlinear, singular, and stiff initial value problems often encountered in real life. Starting with a fixed step size, the new method's performance can be significantly enhanced by introducing an adaptive step-size approach. The qualitative properties of the proposed method have been investigated to determine the efficiency and reliability of the method. The proposed method is of fifth-order accuracy, zero stable, L -stable, and consistent. In addition, the proposed method is convergent, and its stability properties are also shown through its Order Stars. Finally, numerical experiments are conducted to illustrate the performance of the method. The results obtained show that the proposed method compares favourably with existing methods. [ABSTRACT FROM AUTHOR]

To maximize the economic benefits of geothermal energy production, it is essential to optimize geothermal reservoir management strategies, in which geologic uncertainty should be considered. In this work, we propose a closed-loop optimization framework, based on deep learning surrogates, for the well control optimization of geothermal reservoirs. In this framework, we construct a hybrid convolution–recurrent neural network surrogate, which combines the convolution neural network (CNN) and long short-term memory (LSTM) recurrent network. The convolution structure can extract spatial information of reservoir property fields and the recurrent structure can approximate sequence-to-sequence mapping. The trained model can predict time-varying production responses (rate, temperature, etc.) for cases with different permeability fields and well control sequences. In this closed-loop optimization framework, production optimization, based on the differential evolution (DE) algorithm, and data assimilation, based on the iterative ensemble smoother (IES), are performed alternately to achieve a real-time well control optimization and to estimate reservoir properties (e.g. permeability) as the production proceeds. In addition, the averaged objective function over the ensemble of geologic parameter estimates is adopted to consider geologic uncertainty in the optimization process. Geothermal reservoir production cases are examined to evaluate the performance of the proposed closed-loop optimization framework. Our results show that the proposed framework can achieve efficient and effective real-time optimization and data assimilation in the geothermal reservoir production process. • A hybrid convolution–recurrent neural network is constructed as a surrogate for geothermal production problems. • A closed-loop optimization framework is proposed for geothermal energy production. • Data assimilation and well control optimization can be achieved simultaneously for the geothermal reservoir. • The deep learning surrogate can speed up the optimization and data assimilation processes. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing has become an essential technology for businesses and individuals alike. Task scheduling is a critical aspect of cloud computing that affects the performance and efficiency of cloud infrastructure. During this pandemic where most of the healthcare services like COVID-19 sampling, vaccination process, patient management and other services are dependent on cloud infrastructure. These services come with huge clients and server load in a small instance of time. These task loads can only be managed at cloud infrastructure where an efficient resource management algorithm plays an important role. The optimal utilization of cloud infrastructure and optimization algorithms plays a vital role. The cloud resources rely on the allocation policy of the tasks on cloud resources. Simple static, dynamic, and meta -heuristic techniques provide a solution but not the optimal solution. In such a scenario machine learning and evolutionary algorithms are only the solution. In this work, a hybrid model based on meta -heuristic technique and neural network is proposed. The presented neural network inspired differential evolution hybrid technique provides an optimal assignment of the tasks on cloud infrastructure. The performance of the DE-ANN hybrid approach is performed using performance metrics, average start time(ms), average finish time(ms), average execution time(ms), total completion time(ms), simulation time(ms), and average resource utilization respectively. The proposed DE-ANN approach is validated against BB-BC, and Genetic approaches. It outperforms the existing meta -heuristic techniques i.e. Genetic approach, and Big-Bang Big-Crunch. The performance is evaluated using two configuration scenarios using 5 virtual machines and 10 virtual machines with varying tasks from 1000 to 4500. Experimental results show that the DE-ANN technique significantly improves task scheduling performance compared to other traditional techniques. The technique achieves an average improvement of 19.15% in total completion time(ms), 32.23% in average finish time(ms), 51.95% in average execution time(ms), and 33.24% in average resource utilization respectively. The DE-ANN technique is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures. [ABSTRACT FROM AUTHOR]

With the rise and development of computer networks, network security has become a major issue, and intrusion detection has emerged as a crucial aspect of network security. However, traditional intrusion detection methods suffer from issues like slow detection rates and low accuracy. This necessitates the exploration and development of optimized algorithms for improving intrusion detection efficiency and accuracy. Recently, the multi-objective differential evolution algorithm has shown promising results in the field of intrusion detection. This paper explored the application of the multi-objective differential evolution algorithm in computer network intrusion detection systems. The experimental results on the KDDCUP'99 dataset demonstrated that the algorithm delivers high levels of precision and recall, outperforming other intrusion detection methods. Therefore, combining the multi-objective differential evolution algorithm with traditional intrusion detection methods can lead to significant improvements in intrusion detection efficiency and accuracy. Further optimization of this algorithm can be required to meet complex intrusion detection requirements. The multi-objective differential evolution algorithm presents exciting prospects, which can help enhance the efficiency and accuracy of intrusion detection, and provide strong technical support for ensuring network security and preventing cyber-attacks. [ABSTRACT FROM AUTHOR]

In the 2016 International Astronautical Congress, SpaceX's Mars immigration plan was first formally proposed alongside a fully-reusable transportation infrastructure. In the 12th edition of the China Trajectory Optimization Competition (CTOC-12) held in 2022, a Mars transportation trajectory design problem using reusable transporters from a parking distant retrograde orbit (DRO) in the Earth–Moon system was released. It is expected to transport as many immigrants as possible using a maximum of 50 transporters within a total of 20 years. The BIT-DFH-BJUT team reported a solution that can deliver 9080 immigrants to Mars, which ranked first in the competition. In this paper, the methods and results from the winning team are presented, primarily including an overall analysis, underlying round-trip trajectory design, and top-level scheduling. Specifically, a round-trip is divided into four phases, leaving the DRO to man-boarding at the perigee, Earth–Mars interplanetary transfer, Mars's return to the Earth, and return to the DRO. An overall optimization framework is constructed by synthesizing techniques such as data set creation and patching, differential evolution, nonlinear programming, greedy algorithm, and mixed-integer programming. Finally, we outline the final solution of our team and compare the results with those from the top five teams. This competition demonstrates that a large-scale Mars immigration plan is possible by using reusable transporters from the Earth–Moon system. • Trajectory designing and optimization problem for large-scale Mars immigration is established. • The practical designing and optimization methods are presented, including underlying round-trip trajectory design, and top-level scheduling. • The solution that can deliver 9080 immigrants to Mars is demonstrated. [ABSTRACT FROM AUTHOR]

Deep neural network-based object detectors are vulnerable to adversarial examples. Among existing works to fool object detectors, the camouflage-based method is more often adopted due to its adaptation to multi-view scenarios and non-planar objects. However, most of them can still be easily observed by human eyes, which limits their application in the real world. To fool human eyes and object detectors simultaneously, we propose a differential evolution based dual adversarial camouflage method. Specifically, we try to obtain the camouflage texture by the two-stage training, which can be wrapped over the surface of the object. In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene background, making human eyes difficult to distinguish. In the second stage, we design three loss functions to optimize the local texture, which is selected from the global texture, making object detectors ineffective. In addition, we introduce the differential evolution algorithm to search for the near-optimal areas of the object to attack, improving the adversarial performance under certain attack area limitations. Experimental results show that our proposed method can obtain a good trade-off between fooling human eyes and object detectors under multiple specific scenes and objects. [ABSTRACT FROM AUTHOR]

• An approximate optimization model for perturbed orbit rendezvous is obtained via a quasi-optimal thrust strategy. • A fast process to judge the low-thrust accessibility and obtain the parameters of thrust is proposed to reduce the dimensionality of the optimization model and improve the efficiency. • A fast analytical correction process is proposed to modify the solution by the numerical errors between the predicted orbit and target orbit. Trajectory optimization of low-thrust perturbed orbit rendezvous is a crucial technology for space missions in low Earth orbits, which is difficult to solve due to its initial value sensitivity, especially when the transfer trajectory has many revolutions. This paper investigated the time-fixed perturbed orbit rendezvous between low-eccentricity orbits and proposed a priori quasi-optimal thrust strategy to simplify the problem into a parametric optimization problem, which significantly reduces the complexity. The optimal trajectory is divided into three stages including transfer to a certain intermediate orbit, thrust-off drifting and transfer from intermediate orbit to the target orbit. In the two transfer stages, the spacecraft is assumed to use a parametric law of thrust. Then, the optimization model can be then obtained using very few unknowns. Finally, a differential evolution algorithm is adopted to solve the simplified optimization model and an analytical correction process is proposed to eliminate the numerical errors. Simulation results and comparisons with previous methods proved this new method's efficiency and high precision for low-eccentricity orbits. The method can be well applied to premilitary analysis and high-precision trajectory optimization of missions such as in-orbit service and active debris removal in low Earth orbits. [ABSTRACT FROM AUTHOR]

• An energy efficiency perspective to improve the performance sustainability in building retrofitting projects is offered. • A novel maintenance plan optimization is developed to find proper maintenance intensities and instants simultaneously. • The optimal maintenance control is transformed into a parametric optimization by impulsive and switched system modelling. • An improved DE algorithm is employed as a numerical solver to deal with the non-analytic components in the sub-objectives. The maintenance time schedule optimization into the investment budget decision for building energy retrofitting and maintenance planning is incorporated. The aggregate performance and population dynamics of the totality of retrofitted items is described by an impulsive switched system model. The items are switched to different working states by maintenance actions with impulsive effects. Considering the retrofitting project with deterioration. The objective of optimal maintenance control is to maximize the overall energy savings and financial payback. The optimal maintenance control problem is showed to be feasible and bounded. The maintenance time schedule and intensities are simultaneously optimized by an improved differential evolution (DE) algorithm. Compared with the fixed maintenance time schedule, the proposed approach is validated by simulation results, specifically, 3.36% of energy saving and 23.09% of internal rate of return (IRR) can be improved. [ABSTRACT FROM AUTHOR]

A pseudo random number generator (PRNG) is built with a chaotic map without fixed points implemented using fixed-point arithmetic. The generated binary sequences by the PRNG pass all the TestU01 statistical tests to prove the randomness of the generated binary sequences. Three multimodal benchmark functions are optimized with the heuristic Differential Evolution (DE) using the proposed PRNG, and these are compared with the DE using the standard random function available in the C programming language. If the shuffling technique is added to the generated chaotic random sequences, both approaches give the same statistically solutions. A hardware design in FPGA of the PRNG also is shown. • A PRNG is designed with a chaotic map without fixed points. • The PRNG is tested with the heuristic differential evolution. • A hardware design in FPGA is shown and compared with the state of the art designs. [ABSTRACT FROM AUTHOR]

The cold chain logistics (CCL) distribution have been studied by considering customer satisfaction, we propose a multi-objective CCL model with the goals of minimal carbon transaction cost, minimal network cost and maximal customer satisfaction. According to the characteristics of the model, an improved Multi-Objective algorithm was designed, which integrates the dynamic crowding distance and differential mutation operator into non-dominated sorting genetic algorithm II (DDNSGA-II). The DDNSGA-II enhances the diversity of the initial population, the local search ability and search accuracy. Finally, the experimental data show that the proposed approaches effectively increase the customer satisfaction, reduce total distribution costs, and promote energy conservation and emission reduction. [ABSTRACT FROM AUTHOR]

In this study we propose an agent-based SEIR model by taking agents' heterogeneous mobilities into account. By using the actual human flow data between each district of Tokyo, we create behavior scenarios for every agent in the model. After calibrating the risk of infection in each region using DE (differential evolution), we were able to reproduce the realistic number of positive cases not only for the entire Tokyo metropolitan area but also for each of the 23 districts, with an NRMSD of less than 0.05. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust differential game scheme to solve the collision avoidance problem for networked multi-agent systems (MASs), subject to linear dynamics, external disturbances and limited observation capabilities. Compared with the existing differential game approaches only considering obstacle avoidance objectives, we explicitly incorporate the trajectory optimization target by penalizing the deviation from reference trajectories, based on the artificial potential field (APF) concept. It is proved that the strategies of each agent defined by individual optimization problems will converge to a local robust Nash equilibrium (R-NE), which further, with a fixed strong connection topology, will converge to the global R-NE. Additionally, to cope with the limited observation for MASs, local robust feedback control strategies are constructed based on the best approximate cost function and distributed robust Hamilton–Jacobi–Isaacs (DR-HJI) equations, which does not require global information of agents as in the traditional Riccati equation form. The feedback gains of the control strategies are found via the ant colony optimization (ACO) algorithm with a non-dominant sorting structure with convergence guarantees. Finally, simulation results are provided to verify the efficacy and robustness of the novel scheme. The agents arrived at the targeted position collision-free with a reduced arrival time, and reached the targeted positions under disturbance. • The proposed scheme can optimize trajectories with the artificial potential field. • Local control strategies are constructed to cope with the limited observation. • The feedback gains of control strategies are found via the heuristic algorithm. • The robustness of control strategies is considered by optimizing the worst-case cost. [ABSTRACT FROM AUTHOR]

This paper proposes the application of control by state feedback using the linear quadratic regulator (LQR) optimized by metaheuristics to damp low-frequency electromechanical oscillations in electrical power systems. The current sensitivity model was used to represent the single machine infinite bus (SMIB) system in the time domain. The weighting matrices of the LQR were adjusted using four different algorithms: (i) the genetic algorithm, (ii) the differential evolution algorithm, (iii) the particle swarm optimization algorithm, and (iv) the gray wolf optimization (GWO) algorithm. In the cases considered, disturbances were applied to the electrical power system and, then, performances comparisons associated with each metaheuristic were statistically analyzed, in which the number of iterations, error, and time to achieve convergence of each algorithm were compared. From the results, it was possible to conclude that the algorithms were efficient in adjusting the weighting matrices of the LQR, providing additional damping to the poles of interest of the system. It was also possible to conclude that the GWO algorithm presented the best performance, accrediting it as a powerful tool in the study of small-signal stability for the analyzed case. • Design of an LQR for small-signal stability analysis of power systems. • Implementation metaheuristics for adjusting the parameters of the LQR. • Comparison of the performance of evolutionary and swarm-based algorithms. [ABSTRACT FROM AUTHOR]

New investigations were carried out to demonstrate the ability of Trichoderma viride dead biomass for the bioremoval of Cr(VI) from aqueous solutions. The gathered experimental data were initially modelled considering both adsorption and reduction kinetic models, followed by equilibrium models. In the next step, Design of Experiments method (DoE) and a Differential Evolution (DE) algorithm were applied for determining the optimal Response Surface Methodology and Artificial Neural Network (ANN) models which are able to correlate the process efficiency with the experimental conditions. In addition, DE (in combination with the previously determined ANN) was used to obtain the optimal working conditions for maximum process efficiency. These optimization procedures allow setting various process parameters and working conditions which ensure the maximum bioremoval efficiency. DoE highlighted that all the study parameters have statistically significant individual influence on Cr(VI) bioremoval efficiency and, for example, at pH 2 and a temperature of 50 °C, 100% of Cr(VI) is bioremoved if the biosorbent dosage is 10 g/L, considering a minimum contact time of 2 h and given a maximum initial concentration of 122.5 mg/L. According to the ANN model, a contact time set at 24 h yields optimal values for Cr(VI) bioremoval by dead biomass considering pH 1.00, 3 g/L biosorbent dosage, 23 mg/L metallic ion concentration and a temperature of 50 °C. pHpzc, FT-IR and SEM-EDX analyses were included for biomass characterization and to underline the bioremoval mechanism of Cr(VI). Our results are providing a solid basis for the development of technologies for detoxification of contaminated effluents. [Display omitted] • Dead biomass of the fungal strain Trichoderma viride was applied for the bioremoval of Cr(VI) from aqueous solutions. • Both adsorption and reduction kinetic models, followed by equilibrium models were used in an initial step. • Design of Experiments method (DoE) and a Differential Evolution (DE) algorithm were applied. • DoE highlights the statistically significant individual influence of study parameters on Cr(VI) bioremoval efficiency. • The optimal neural network was determined from multiple iterations of a DE algorithm. [ABSTRACT FROM AUTHOR]

The approach outlined in this paper takes advantage of the existence of stationary states and similarity solutions to reduce a one-dimensional population balance model with particle volume as the internal dimension from a partial integro-differential equation to a single ordinary differential equation for the evolution of the mean size. The mechanisms of accretional growth, collisional growth and breakage, either individually or in all possible combinations, are considered. Power-law rate kernels are employed and a self-similar fragment distribution is assumed for the breakage term. When expressed in terms of the evolution of the scaled number-mean particle volume, the model parameters consist of kernel orders and the characteristic times associated with each mechanism. Equations for the scaled moments of the stationary states and similarity solutions are exhibited for all possible mechanism combinations as are equations for the trajectories of scaled number-mean particle volume. Stability of the stationary states and similarity solutions is addressed in an appendix. It should be clear that when this level of simplification is possible, the subject of population balance modeling becomes considerably easier to teach while its use becomes much easier to promote in industry. The use of this simplified approach for extraction of model parameters from experiment is demonstrated in an example, followed by a brief discussion of using the results for scale up. To close out the paper, the same approach, but now in two internal dimensions (particle volume and surface area), is used to demonstrate the capability of the model to unify a set of operating conditions for carbon black manufacture across multiple grades and two different types of synthesis reactors. This is done by extracting the characteristic coalescence time at multiple temperatures and correlating the results in an Arrhenius plot. The model matches known values of primary particle size, and predicts a key indicator of the product's fractal aggregate structure, namely the number of primary particles per aggregate. The model results are then pushed to yield insight into the nature of the possible collision and coalescence mechanisms. [Display omitted] • Similarity solutions/stationary states enable reduction of 1-D models to a single ODE for mean size evolution. • Distribution shape is expressed in the form of scaled moments that are constant under these conditions. • Certain of these combine with the rate kernels to identify characteristic timescales. • These simplifications enable extraction of rate kernel orders and timescales from data. • These 1-D model simplifications are extended to a 2-D collision-coalescence example. [ABSTRACT FROM AUTHOR]

• A time-varying external archive differential evolution can obtain multiple roots for system of nonlinear equations. • A judgment criterion dividing individuals into the ordinary, local and global optimal subpopulations is proposed. • A new difference mutation vector is used to ensure the diversity of the population. • The generality, computation efficiency, and applicability of the proposed method are verified. In this work, a time-varying external archive differential evolution algorithm is proposed, which can obtain the multiple roots for system of nonlinear equations for the forward displacement of parallel mechanisms. In time-varying external archive differential evolution three improvements are presented. At first, a time-varying subpopulation strategy is proposed to consider both global and local searching capabilities. Then, an individual judgment criterion is proposed, which employs the weighted basis vector mutation strategy for ordinary individuals and a hybrid mutation operator for locally optimal individuals. Finally, the external archive strategy of neighborhood-based crowding differential evolution is improved to enhance the domain searching ability of the algorithm. To evaluate the performance of time-varying external archive differential evolution, twenty benchmark functions are selected as the test suite. Moreover, the solutions for forward displacements of four strongly coupled parallel mechanisms with different degrees of freedom are derived to test the generality of this method. From the acquired experimental results, it is demonstrated that fairly-competitive results can be provided compared to other methods, in both benchmark functions and forward displacement solutions of parallel mechanisms. [ABSTRACT FROM AUTHOR]

• Differentially private mechanisms are modeled as Markov chains or Markov decision processes. • Differentially private properties are specified in the extended branching time temporal logic dpCTL⁎. • In order to verify differential privacy, the corresponding model checking problems are investigated. • Efficient model checking algorithms are implemented and case studies for differential privacy are performed. With the explosion of digital data collected from social apps, privacy protection regulations have been issued by almost all countries. Differential privacy is proposed as a successful technique to make use of these data, without leaking personal private data at the same time. In this paper, we investigate logical reasoning for differential privacy properties and propose model checking algorithms. We introduce the branching time temporal logic dpCTL* to specify differentially private properties. Several mechanisms in differential privacy are formalized as Markov chains or Markov decision processes. In our framework, we show that subtle privacy conditions are specified by dpCTL*. In order to verify privacy properties automatically, model checking problems are investigated. We develop a model checking algorithm for Markov chains. Model checking dpCTL* properties on Markov decision processes however is shown to be undecidable. Therefore, we propose a sound algorithm to verify these properties in Markov decision processes. We implement our model checking algorithm into a tool, based on the PRISM language, as well as show the experimental results and a case study of stream processing in differential privacy under the essential fairness assumptions. [ABSTRACT FROM AUTHOR]

Nowadays, chemical processes are projected to obtain their best performance in energy and water consumption, pollutant emissions and total annual costs, while still meeting quality of products and good operational performance. These goals are accomplished through adequate optimization of the fitness function by manipulating the operational variables (decision variables) of the process. However, a successful optimization process depends completely on the constraint handling established in the modeling of the process. The weighted summation of constraint violations (weighting function technique, WF) is one of the most common approaches for handling constraints in optimization problems. Nevertheless, in spite of this technique yielding good results, in this work we show a novel self-adaptive constraint handling technique (SA) based on a self-adaptation dynamic threshold and self-adaptation (weight) factors. This technique deals with real and discrete variables and converts equality constraints into inequality constraints through a dynamic threshold. Both penalization techniques (WF and SA) were, respectively, coupled to a Differential Evolution (DE) algorithm to optimize some benchmark functions and chemical engineering optimization problems. In addition, the rigorous model of a distillation train was optimized in Aspen One for the first time with a self-adaptive constraint handling technique in chemical engineering. Although both penalization techniques were coupled to the same DE algorithm and both cases were run under the same conditions, the results show that the dynamic self-adaptive constraint handling technique coupled to DE (DE-SA) achieves considerably better best-solutions than the best-solutions obtained by the weighting function technique coupled to DE (DE-WF). In addition, DE-SA led to substantial reductions of numerical effort in relation to DE-WF. These conclusions are supported by statistical analysis of the results of 30 runs of the optimization process for each constraint handling technique, for a distillation train. [Display omitted] • Differential Evolution was coupled with a Dynamic self-Adaptive constraint handling. • This optimization strategy tackled several kinds of constrained optimization problems. • Dynamic constraint showed better performance than weighted constraint. • Dynamic constraint led to obtain the best optimal values of the fitness function. • Dynamic constraint led to use less computational effort than weighted constraint. [ABSTRACT FROM AUTHOR]

This article solves the scheduling problem in vegetable processing and packaging facility, where multiple types of vegetable consignments need to be packed using dissimilar parallel machines. The problem considers the real-life complexities, such as arrival and discharge time of consignments and setup time for the pre-processing requirement between consecutive consignments. The objective is to identify optimal schedules with minimum total cost and minimum makespan by satisfying the associated constraints. This study proposes an optimization framework incorporating metaheuristic techniques and heuristic mechanism to determine optimal solution for the scheduling problem. The heuristic mechanism assists metaheuristic techniques in reducing constraint violations by rescheduling vegetable consignments assigned to the machine. The efficacy of the proposed framework is analyzed by solving a case study using multiple single and multi-objective metaheuristic techniques. All metaheuristic techniques are able to determine a better schedule for optimizing total cost and makespan using the proposed framework than their naive metaheuristic technique on solving the single objective model. Differential Evolution with Multi-Population Based Ensemble of Mutation Strategies provided a better solution than other techniques for the single objective model of both objectives. Non-dominated Sorting Teaching Learning Based Optimization, with heuristic mechanism, provided strictly dominating solutions compared to the other techniques. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new application of WDE for estimation of nonlinear Hammerstein-Wiener systems. • Designed WDE is implemented for measured input, internal and output data viably. • The sundry scenarios of HWM establish robustness, stability and convergence of WDE. • Statistical observations prove efficacy for controlled autoregressive scenarios. Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability. The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters. The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials. Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme. Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios. [ABSTRACT FROM AUTHOR]

In recent aerospace missions, space logistics have proven essential in storing, delivering and returning crew and materials between terrestrial facilities and space stations. Unlike classical commercial logistics, space logistics operations are cost-prohibitive and mission-driven, and its replenishment cycle for essential materials is relatively long. Therefore, the complete utilisation of spacecraft payload is of utmost importance. The theory of the inventory packing problem is extended in this study to build autonomous agents that interact with one another within a space logistics decision support system to reinforce the replenishment decision, chunk loading optimisation, and quality inspection. With the long replenishment cycle time, an agent embedded with interval type-2 fuzzy logic is explored to support chaotic time-series demand forecasting to derive re-order quantities in the desired period. Afterwards, the second agent solves the space chunk loading problem using the differential evolution algorithm to utilise payloads and capacities, particularly cylindrical chunks fully. The third agent measures actual item dimensions and quality to deploy the three-dimensional object scanning devices. Feedback is provided to the second agent to derive optimal chunk-loading instructions. Thanks to the autonomous interactions among the above agents, mission-critical decisions for space logistics are supported to achieve operational excellence. [ABSTRACT FROM AUTHOR]

One of the industries with the fastest growth rates worldwide, and notably in Thailand, is medical tourism. With connections to Cambodia and Laos, Ubon Ratchathani is located in lower northeastern Thailand, close to Vietnam and Myanmar. Therefore, there is a significant chance that this region will welcome medical travelers. High-quality medical facilities are available in Ubon Ratchathani to fulfill the needs of medical tourists. A visitor's decision to travel to Ubon Ratchathani for medical treatment is influenced by factors other than the high-level medical facilities, such as lodging, accessibility to public transportation, and tourist attractions. The public transportation services in Ubon Ratchathani, especially the public bus system, are poorly designed and may let down visitors. The purpose of this study is to develop a smart public bus route design that will meet tourists' demands. The concept of open innovation will be utilized to develop the model. We surveyed 400 visitors to Ubon Ratchathani. The tourists' opinions and views of public transportation will be made public and used as an input parameter when designing bus routes. The bus route can then be constructed using the differential evolution algorithm (DE). A web-based smart public transportation system was built. In order to construct an efficient smart public bus system (SPBS), open innovation was used in the development phase. According to the computational results, the new routes using DE lead to a 5.97% reduction in travel distance when compared to the output of the more well-known genetic method. More than 98.5% of visitors are satisfied with the new routes, and once they start running, 99.5% of all respondents plan to use public transit. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a popular optimization algorithm with easy implementation and fast convergence rate. For evolutionary algorithms such as DE, the initialization process of solution members is crucial because the distribution of initial population can govern the overall quality of final solution obtained in terms of accuracy and convergence speed. This study leverages the strengths of both chaotic maps and oppositional-based learning strategy to design a new DE variant with modified initialization scheme, namely chaotic oppositional DE (CODE) in order to generate the initial population with good quality of mean fitness and diversity of the solutions. The effectiveness of CODE variants incorporated with seven different chaotic maps are investigated using CEC 2014 benchmark functions and the chaotic circle oppositional DE (CCODE) is revealed as the best performing CODE variants. The optimization performance of CCODE is further compared with other existing optimization algorithms in terms of solution accuracy and convergence speed. Extensive simulation studies prove that the proposed algorithm is able to outperform its peers by achieving better trade-off between two contradicting requirements of fast convergence speed and population diversity preservation. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problem (CMOP) considers the convergence, diversity and feasibility of the population in the optimization process, so it is challenging to find desirable solutions of CMOP. Existing evolutionary multi-objective optimization algorithms have good performance on unconstrained multi-objective optimization problems, but have difficulties in solving CMOPs in discrete feasible regions. Aiming at this issue, this paper proposes a novel multi-level population hybrid search evolution algorithm (MLHSEA). First, a new multi-level hybrid search strategy (i.e. MHSS) is designed in the algorithm, which divides the population into three-level subpopulations based on Pareto ranks, constraint violation degree values, and feasible thresholds. Each subpopulation has its own unique evolution strategy to maximize the evolutionary potential of each subpopulation, which is beneficial to make some feasible solutions break through the discrete feasible region and reach the Pareto frontier. Then, a new population fusion degree strategy (i.e. PFDS) is proposed to timely perform population fusion and information exchange according to the population fusion degree (PFD) of each sub-population, thus improving the searchability of the target space. Finally, a novel detection reset strategy (i.e. DRS) is proposed for the lowest inferior subpopulation. This strategy can make inferior subpopulations avoid unnecessary evolutionary iterations and improve population diversity. Based on constrained test suites with four different characteristics, the experimental results show that the proposed MLHSEA outperforms other state-of-the-art constrained multi-objective optimization algorithms in performance. [ABSTRACT FROM AUTHOR]

We present a new mixed finite element method for a class of parabolic equations with p -Laplacian and nonlinear memory. The applicability, stability and convergence of the method are studied. First, the problem is written in a mixed formulation as a system of one parabolic equation and a Volterra equation. Then, the system is discretized in the space variable using the finite element method with Lagrangian basis of degree r ≥ 1. Finally, the Cranck-Nicolson method with the trapezoidal quadrature is applied to discretize the time variable. For each method, we establish existence, uniqueness and regularity of the solutions. The convergence order is found to be dependent on the parameter p on the p -Laplacian in the sense that it decreases as p increases. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks include a substantial number of nodes with limited battery power that is used for gathering and sending the data to the Base station. Here most energy is consumed in data transfer. Hence a foremost problem is the maximization of network lifetime through minimization of energy consumption in the nodes. To resolve this issue, the clustering method is involved to achieve energy-efficient data transmission. A cluster head is elected which uses an energy distribution mechanism to conserve the remaining power, thereby extending network lifetime. Various present methodologies are employed but every algorithm had notable constraints individually. In this paper, a hybrid Sparrow Search Algorithm with Differential Evolution algorithm is intended to solve the energy efficiency issue by cluster head selection in Wireless Sensor Networks. The proposed algorithm uses the high-level search efficiency of the Sparrow Search Algorithm and the lively potential of Differential Evolution that enhances the lifetime of nodes. The performance of this hybrid model seems to be exploiting the count of alive nodes and dead nodes, throughput, and residual energy. The proposed Improved Sparrow search algorithm using the Differential evolution model for choosing the best possible cluster head shows a development in residual power and throughput than other compared algorithms. [ABSTRACT FROM AUTHOR]

Most studies in re-circulating cooling water system(RCWS) synthesis principally focused on configurations and economic optimizations but ignored the water and air evaluation in the cooling tower. This study proposes a comprehensive RCWS synthesis method based on stage-wise structure and the Poppe method to optimize the economic cost and determine the cooler configurations while simultaneously evaluating the water and air parameters. The closed-loop issues manipulation and differential evolution algorithm are applied for calculating the iteration process for unsaturated and supersaturated air conditions. The validation and case study are presented to confer the discrepancies between the proposed and previous models. Then different types of matching, comparison of different tower inlet limit temperatures, and makeup temperatures are proposed to explain the discrepancy of tower calculation methods and stage numbers. The validation and discussion show that the TAC calculations for the four cases are reduced by about 4% and the outlet air temperature of the Poppe method is about 2 °C lower than the Merkel method, with a supersaturated state. This study proposes a comprehensive approach for RCWS accurate synthesis problems. [ABSTRACT FROM AUTHOR]

Nonlinear equation systems (NESs) are common in practical applications, and solving them is an important task in numerical computation. Evolutionary algorithms (EAs) for handling NESs have received considerable attention. EAs generate a large amount of data, which reflect the evolutionary behavior. However, there are still deficiencies in the mining and use of these data. Inspired by the ability of humans to acquire knowledge from past historical experience, this paper proposes a knowledge-learning-and-transfer-aided hybrid niching-based differential evolution (HNDE/KLT). HNDE/KLT aims to acquire knowledge from historical experiences and use them to guide the evolution. The HNDE/KLT include two main features: i) artificial neural networks are embedded in EAs, so the algorithm can learn from successful historical evolutionary information and obtain the relationship between the current individual's position and the optimal evolutionary direction; ii) a knowledge-transfer-based technique is designed to perform information exchange between different subpopulations, enhancing exploitation efficiency. Experimental results show that the HNDE/KLT can better solve NESs, indicating that learning plus transfer can make problem solving more intelligent. Additionally, we employed our algorithm to solve two real-world problems, and obtained good results. [ABSTRACT FROM AUTHOR]