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This paper takes a reaction-diffusion predator-prey system with ratio-dependent Holling III functional response function and Leslie-Gower term into consideration. First of all, the system model is proposed on the basis of basic biological assumptions and previous work, and the existence conditions of the equilibrium point of the system are discussed. Secondly, under the assumption of the existence of equilibrium point, the Turing instability necessary conditions induced by diffusion are investigated. Thirdly, optimal control theory is derived, and the adjoint system and the first-order optimization condition are established. Fourthly, parameter identification based on optimal control is studied, and the technique is extended to network structure. Finally, extensive numerical simulations, including Turing pattern, Normalized Population High Distribution Area (NPHDA) diagram and parameter identification, are carried out to illustrate and validate the analytical results. For the system dynamics phenomena, the results from different perspectives effectively demonstrate that the theoretical findings, numerical simulations and natural reality are identical. In terms of the parameter identification of continuous model and network model, the efficiency and accuracy of various algorithms are fully tested. • This paper proposes and investigates index normalized population height distribution area. • The optimal control problem of reaction-diffusion predator-prey system is formally derived. • The parameter identification research is extended to population network structure space. [ABSTRACT FROM AUTHOR]

• An efficient mid-range approximation method for large-scale problems is proposed. • The improved trust-region strategy has a flexible and controllable performance. • A metamodel assembly technique is developed to alleviate the efficiency issues. • Optimization problems of 512 and 232 design variables are considered respectively. • The method successfully obtains the optimum at a reasonable computational cost. This paper presents an efficient solution for high-fidelity large-scale aerodynamic shape optimization problems based on several developments in the mid-range approximation method within a trust region optimization framework. The mid-range approximation method is an iterative optimization technique that utilizes mid-range approximations to replace the physical experiments or simulations during the optimization based on the selected trust region strategy. It could transform the original optimization problem into a sequence of approximate sub-optimization problems. In this work, an improved trust region strategy is proposed to contain more optimization states with a flexible and controllable performance to suit different types of problems. A metamodel assembly technique and its gradient-enhanced version are developed to further relax the requirements of computational costs in the mid-range approximation method. Its performance is discussed through a detailed comparison of metamodel performance using a mathematical benchmark case named Vanderplaats scalable beam. The wing only of the Common Research Model is offered to the proposed method for aerodynamic shape optimization. The optimization has 1 design objective, 232 design variables, and 135 design constraints. With all constraints satisfied, the optimized configuration has a 4.85 % improvement in wing drag performance. The shock region is greatly reduced and the wing pressure distribution is smooth and nearly parallel. These results show that the proposed method could achieve the design goal successfully within a reasonable computational cost for large-scale problems. [ABSTRACT FROM AUTHOR]

• The negative stiffness inertial amplifier tuned mass dampers is introduced. • The new closed-form expressions for optimal design parameters of NSIA-TMDs. • The H 2 and H ∞ optimization based closed-form expressions for optimal design parameters. • NSIA-TMDs shows 50% better vibration reduction capacity than conventional TMD. The negative stiffness inertial amplifier tuned mass dampers (NSIA-TMD) are introduced in this paper. Another two novel tuned mass dampers such as negative stiffness tuned mass damper (NS-TMD) and inertial amplifier tuned mass damper (IA-TMD) are mathematically developed from the negative stiffness inertial amplifier tuned mass dampers (NSIA-TMD) and the static masses of these three novel dampers are retained constant. The exact closed-form expressions for optimized system parameters for these novel dampers are obtained using H 2 and H ∞ optimization techniques. The dynamic responses of the SDOF systems controlled by H 2 and H ∞ optimized novel tuned mass dampers subjected to base excitations are obtained analytically. The dynamic response reduction capacities of the novel tuned mass dampers are compared with the dynamic response reduction capacities of traditional tuned mass dampers (TMD). Therefore, the dynamic response reduction capacities of H 2 optimized NS-TMD, NSIA-TMD, and IA-TMD are significantly 45.51 % , 43.47 % , 41.08 % superior to the H 2 optimized traditional tuned mass dampers. Furthermore, the dynamic response reduction capacities of H ∞ optimized NS-TMD, NSIA-TMD, and IA-TMD are significantly 3.31 % , 8.98 % , 13.79 % superior to the H ∞ optimized traditional tuned mass dampers. The nonlinear negative stiffness inertial amplifier tuned mass dampers (NNSIA-TMD) are also introduced in this paper. As a result, the dynamic response reduction capacities of H 2 optimized nonlinear negative stiffness tuned mass damper (NNS-TMD), NNSIA-TMD, and nonlinear inertial amplifier tuned mass damper (NIA-TMD) are significantly 24.54 % , 21.92 % , 19.12 % superior to the H 2 optimized traditional tuned mass dampers. Furthermore, the dynamic response reduction capacities of H ∞ optimized NNS-TMD, NNSIA-TMD, and NIA-TMD are significantly 3.01 % , 9.04 % , 15.08 % superior to the H ∞ optimized traditional tuned mass dampers. The outcomes of this research are mathematically accurate and relevant to practical design applications. [ABSTRACT FROM AUTHOR]

To improve convergence speed of deep neural network trained by gradient descent methods (GDMs), this paper proposed a novel fractional order gradient descent optimizer (FOAdam) based on Caputo operator and Adam algorithm, and analyzed the impact of fractional order on convergence performance using optimization theory and simulation. Furthermore, to address the trade-off between convergence speed and precision under fixed-order conditions, a fractional order scheduler (FOS) was designed based on the Connections Cloud Model (CCM). Numerical experiments conducted on MNIST and CIFAR-10 datasets showed that FOS-FOAdam outperformed other mainstream GDMs in terms of convergence speed and precision. Finally, FOS-FOAdam was applied in recognizing and classifying image datasets of cuttings and cores at the logging site, demonstrating good prospects for practical engineering applications. [ABSTRACT FROM AUTHOR]

• Determining the urban critical infrastructures in the event of an earthquake. • Designing Location-Routing-Inventory decisions in humanitarian relief chain. • Simulation of interaction of urban infrastructure in earthquake conditions. • Using a cooperative game to examine the manner of cooperation of disaster relief supply chain players. • Application of the model to a real-world disaster relief logistic case study. This paper describes a new simulation based mathematical model for locating distribution centres, vehicle routing, and inventory problems under earthquake conditions. In this paper, the proposed network includes affected areas, suppliers, distribution centres, and hospitals. Additionally, the basic infrastructures of the city, which are very fragile at the time of the earthquake, are identified. Then, the demand of each relief commodity is calculated depending on the different earthquake scenarios using simulation. The estimated demand is incorporated into the mathematical model as an uncertain parameter. The proposed methodology is designed as a two-stage model so that in the first stage the location and inventory of distribution centres are addressed. Thereafter, in the second stage the routing decisions are taken for the distribution of relief commodities from the distribution centres and suppliers to the affected areas. Due to the NP-hardness of the second stage model, this model is solved using multi-objective stochastic fractal search. This algorithm is one of the population-based and stochastic optimization techniques and inspired by the natural phenomenon of fractal growth. It should be noted that in the second stage, a cooperative game theory of coalition type is considered, which resulted in synergies that minimize the relief golden time. The kind of cooperation is the use of potential of co-operators' vehicle. In this stage, the possibility for the players to cooperate by sharing people and commodities transportation requests is considered. Also, to validate the model, a real case study is provided for a possible earthquake in Tehran. Finally, the comparison between the simulation results and the values obtained from the real system evaluates the performance of the implemented model. Considering normality and a 95% confidence interval, it can be concluded that the proposed model provides a precise representation of the real system's performance. [ABSTRACT FROM AUTHOR]

• In-silico framework for the simultaneous optimal control and design of shape-morphing composite magnetic sheets. • The externally applied magnetic flux density and the remnant magnetisation of hard particles are simultaneously optimized. • The existence of optimal solutions for both the underlying state equation and the control problem is proved. • Explicit expressions of the continuous gradients are obtained by using the formal Lagrangian method. • An alternative to intuition based approaches to obtain complex magnetically induced configurations is provided. This paper presents a novel in-silico framework for the simultaneous optimal control and design of complex magnetic responsive polymer composite materials. State-of-the-art optimisation techniques are used in conjunction with the latest developments in the numerical solution of hard-magnetic soft materials undergoing large (potentially extreme) deformations, in order to address the challenging task of designing shape-morphing two-dimensional composite magnetic sheets. This paper introduces the following key novelties: (i) an optimisation-driven method for the simultaneous optimal control and design of the externally applied magnetic flux density as well as the remnant magnetisation of hard particles within the elastomer matrix, (ii) the well-posedness character of the optimisation problem is established by proving existence of solutions for both the underlying state equation and the control problem itself, (iii) a gradient-based optimisation algorithm is proposed for the numerical approximation of the problem, where explicit expressions of the continuous gradients are obtained by using the formal Lagrangian method. Furthermore, a series of numerical examples are presented in order to demonstrate the capability of the proposal as an alternative to intuition or experimentally-based approaches, representing an optimisation-driven method that facilitates the design of smart materials yielding complex magnetically induced shape morphing configurations. [ABSTRACT FROM AUTHOR]

• A robust method is proposed to alleviate the effect of traffic data with outliers. • A comprehensive traffic flow prediction indicator system is established in this paper. • We hybridize polynomial and Gaussian kernel to build nonlinear version of our method. • Parameters of the proposed method are optimized by Fruit Fly Optimization Algorithm. A novel least squares twin support vector regression method is proposed based on the robust L 1 -norm distance to alleviate the negative effect of traffic data with outliers. Although there is some known work for the short-term traffic flow prediction problems, their efficacy depends heavily on the collected traffic data, which are often affected by various external factors (e.g. weather, traffic jam or accident), leading to errors and missing data. This makes it difficult to pick an effective method that accurately predicts the traffic state. As a contribution of this paper, an iterative algorithm is designed to solve the non-smooth L 1 -norm terms of our method; its convergence also proved. Further, a comprehensive traffic flow indicator system based on speed, traffic flow, occupancy and ample degree is utilized in this paper. We also extend the proposed method to a nonlinear version by hybridizing the polynomial kernel and radial basis function kernel, where the weight coefficient of hybrid kernel is determined by the change tendency of traffic data. To promote the prediction performance, the parameters of our nonlinear method are optimized by adaptive fruit fly optimization algorithm. Extensive experiments on real traffic data are performed to evaluate our model. The results indicate that the newly constructed model yields better prediction performance and robustness than other models in various experimental settings. [ABSTRACT FROM AUTHOR]

• A novel full nonlinear mathematical model of asymmetric hydro turbine governing system established. • The multi-stability characteristics of as well as its generation mechanism are revealed. • The effect mechanism of water diversion system topology on the system stability is revealed. • The recommended setting values of system parameters under different topologies are provided. This paper aims to study the stability and nonlinear dynamics of hydro-turbine governing system with asymmetric water diversion system, i.e. asymmetric hydro turbine governing system, by using Hopf bifurcation theory. Firstly, the full nonlinear mathematical model of asymmetric hydro turbine governing system is established by all system components and nonlinear head loss. This model contains two units with different capacities which share a common pipeline. Based on the nonlinear mathematical model, the multi-stability characteristics of asymmetric hydro turbine governing system under load disturbance is studied by using stable domain and verified by numerical simulation. Moreover, the multi-time scale oscillation is revealed and its relationship to system multi-stability is investigated. Furthermore, the effect of system parameters and topological parameters on system stability is analyzed. Results indicate that: two stable domains are emerged under load disturbance, the overall stability of asymmetric hydro turbine governing system is determined by the intersection area of the two stable domains. In addition, the system parameters and topological parameters both have obvious effect on system stability, which can be significantly improved by reasonable tuning of system parameters and optimization of water diversion system layout. [ABSTRACT FROM AUTHOR]

• A variable order fractional accumulation generating operator is proposed. • A variable order derivative is introduced into a grey model. • The effect of the new model is better than that of similar competitive models. • The new model can be used to predict problems with variable order characteristics. The use of constant order differential equations to describe the evolution of complex systems is often unable to describe some of the changing characteristics of the systems accurately. Variable order fractional derivatives provide us with new tools to solve such problems. In this paper, the accumulation and derivative orders of the classic grey model are expanded from constants to functions, and a variable order fractional grey model is established to describe the evolution process of complex systems. Firstly, this paper defines the variable order fractional accumulation generation sequence. On the basis of this sequence, a variable order fractional derivative grey model is established, the parameters of the model are estimated using the least square method, and the quantum particle swarm optimization algorithm is used to solve the order of fractional derivative and accumulation. Sadik transform and Laplace transform are adopted to obtain the analytical solution of the new model. Lastly, the effectiveness of the new model is verified through four cases. Compared with other models, the variable order fractional model can describe the development process of complex systems more accurately. [ABSTRACT FROM AUTHOR]

• The studied problem in this paper is completely novel. • Generalized shifted Legendre polynomials (GSLP) are introduced. • GSLP is the novel and powerful computational tool for solving fractional optimal control problems. • We derive a new fractional operational matrix in the Caputo sense for the GSLP. • Applying the optimization technique to obtain the best possible approximate solution. This paper proposes an optimization method for solving a general form of nonlinear fractional optimal control problems (NFOCP) governed by nonlinear fractional dynamical systems and mixed conditions. A model for cancer treatment based on the NFOCP is developed, by including the population of immune cells, tumor cells, normal cells, fat cells, chemotherapeutic and immunotherapeutic drug concentrations. The aim behind the NFOCP is to find the optimal doses of chemotherapeutic and immunotherapeutic drugs which minimize the difference between the numbers of tumor and normal cells. The proposed approach includes inserting weight constants for the tumor and normal cells in the cost functional so that the normal cell population is large minimizing, therefore, the tumor burden. The method approximates the solutions of the NFOCP using the generalized shifted Legendre polynomials (GSLP). Operational matrices of integer and fractional derivatives are derived for the GSLP. The Gaussian quadrature rule and the Lagrange multipliers method reduce the NFOCP to a system of algebraic equations. Additionally, the error analysis of the algorithm is considered. Numerical examples demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

• The visual difference between noise and image structure is described and used for denoising. • A dual-constrained Retinex decomposition model is proposed for low-light image enhancement and removing imaging noise. • A patch-aware low-rank prior is proposed for denoising, which can measure noise intensity for each image patch. For low-light image enhancement, Retinex-based models have been acknowledged as a representative technique, yet they frequently amplify noise hidden in dark image. There have been some attempts to noise reduction. Because they assume a uniform level of noise, the background seems blurry. In this paper, a dual constraint is developed to perform Retinex decomposition and de-noising. First, a patch-aware low-rank model (PALR) is proposed to reject noise. In contrast to the global noise intensity assumption, PALR can measure noise intensity for each image patch, and flexibly control the regularization extent to achieve a trade-off between noise removal and details preservation. Second, noise looks like image background texture instead of image structure. Relative total variation (RTV) is used to simulate the visual difference and works well in enlarging image gradients for highlighting details. By imposing PALR constraint on clean scene image, enforcing visual difference constraint on the illumination and reflectance, a dual-constrained Retinex decomposition model (DCRD) is proposed, which can remove noise during decomposition. DCRD can be solved by an alternating optimization algorithm. Experiments on commonly tested low-light image datasets demonstrate the competing performance of our proposed model in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A combined probability prediction system based on quantile prediction is proposed. • The quantile prediction component based on deep learning is constructed. • An innovation improved multi-objective optimization algorithm is proposed. • Opposition learning and elite level strategy are introduced in optimization algorithm. Wind speed prediction accuracy is critical for grid connection safety and intelligent wind farm management. However, most wind speed prediction studies mainly focus on the deterministic prediction, and are rarely discussed in wind speed uncertain prediction. Therefore, this paper proposes a wind speed combined probability prediction system that integrates data denoising technology and creatively introduces the concept of quantile into the deep learning model to construct the wind speed quantile prediction component. To ensemble the prediction components effectively, a novel multi-objective marine predator combination strategy is developed that circumvents the limitations of the traditional multi-objective optimization algorithm. The experimental results based on two wind speed datasets show that the proposed system can improve wind speed prediction accuracy, build a more appropriate wind speed prediction interval, efficiently measure and minimize the uncertainty of the forecast process. [ABSTRACT FROM AUTHOR]

• An interval sequence prediction method for power generation is proposed. • The new matrixed grey model can be applied to different interval sequences. • Parameters are estimated by combining Particle swarm optimization algorithm and Ordinary Least Square method. • The new model has higher prediction accuracy compared with four competing models. • Intervals of different types of power generation in China are predicted. Development trends of power generation sequences have nonlinearity and uncertainty. The nonlinearity can be reflected by the nonlinear grey Bernoulli model. The uncertainty can be reflected by the interval sequence. A matrixed nonlinear grey Bernoulli model based on interval sequences is proposed in this paper. In order to make the model directly applicable to interval sequence, the traditional grey Bernoulli model is linearized by power function transformation, then the parameters are matrixized, and finally the recursive prediction formula is obtained by Cramer's law. Particle swarm optimization algorithm is used to estimate the nonlinear parameter. By adjusting the parameter, the new model can be used to predict exponential, saturated and parabolic interval sequences. The new model is used to predict the intervals of China's thermal power, hydro power, nuclear power, wind power, national power generation and power generation in east China. The prediction results show that the new model has higher prediction accuracy than the competing models. Finally, the power generation of four modes and national power generation from 2022 to 2025 are predicted and analyzed. [ABSTRACT FROM AUTHOR]

• A novel discrete multivariable grey prediction model considering the spatial proximity effect is proposed. • The complete spatial distance index is designed to quantify the intensity of the spatial proximity effect. • The spatial proximity effect term is constructed and incorporated into the grey prediction framework. • The proposed method extends the application field of the grey forecasting technology. • The novel forecasting method significantly outperforms a range of benchmark models. The temporal and spatial characteristics of the collected data typically play an essential role in time series forecasting. However, traditional grey models pay more attention to temporal information, meanwhile disregarding the presence of spatial features critical to producing accurate forecasts. Moreover, the grey theory has few attempts to overcome this problem. Given this situation, this paper proposes a novel multivariable grey prediction model considering the spatial proximity effect for time series forecasting. More specifically, the complete spatial distance index is defined initially based on the geographical and economic distance to quantify the intensity of the spatial proximity impact. On this basis, the spatial proximity effect term is constructed and incorporated into the conventional discrete multivariable grey model, establishing the novel model. Subsequently, the particle swarm optimization algorithm determines the optimal weight coefficient in the developed index. For illustration and verification purposes, the proposed model performs experiments predicting Jiangsu's economic outputs compared to five prevalent benchmarks. Furthermore, three statistical metrics, the Diebold-Mariano test, the Monte Carlo experiments, and the probability density analysis are employed to validate the model's efficacy and robustness. Empirical results demonstrate that the proposed method outperforms all five competitors with strong robustness and broad generalization. Therefore, this new model is used for forecasting Jiangsu's future economic outputs from 2021 to 2025. [ABSTRACT FROM AUTHOR]

• A new advanced whale optimization algorithm for global optimization is proposed. • Armed force program and strategic adjustment are integrated with the original method. • CEC 2014 functions are tested to verify the effectiveness of the proposed optimizer. • The proposed optimizer is compared with others in solving engineering problems. In this paper, we propose a new whale army optimization algorithm with a view to solving multifarious optimization problems. The key novelty of our approach is to modify the original whale optimization algorithm to make it effective to solve the complicated, large-scale and constrained optimization problems. Our modifications mainly embody two aspects: the beneficial strategic adjustment to set key parameters and to help establish base principles in the original optimizer and the introduction of armed force program which classifies the search whales into different categories to achieve efficient cooperation. We evaluate the performance of the proposed algorithm, using three simple benchmark test functions over thirty CEC-2014 real-parameter numerical optimization problems and three constraint engineering design problems. The test results indicate that this algorithm can provide a faster local convergence rate, a higher convergence accuracy, and a lower computational complexity in comparison to traditional whale optimization algorithms and other sophisticated state of the art whale optimizers. Performance wise, it also surpasses many advanced methods for large-scaled complex functions. Furthermore, in this paper we propose a variant of whale army optimization algorithm to specifically address and solve optimizing constrained problems with a high degree of precision. [ABSTRACT FROM AUTHOR]

• This paper proposed a novel wind speed forecasting algorithm. • A new dataset division mechanism based on the moving window is used in this paper. • In order to improve the accuracy and stability simultaneously, the multi-objective particle swarm optimization is applied. • We compared the proposed MOPSO-Combined method with the combined model that published before in the paper. Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before. [ABSTRACT FROM AUTHOR]

• A novel nonlinear impulsive time-delay system in fed-batch process is proposed. • The robust parameter identification problem is presented to determine the optimal parameters. • A parallel particle swarm optimization algorithm is developed to solve the identification problem. In this paper, taking the feeding process as an impulsive form and considering the time-delay in fermentation process, we propose a nonlinear impulsive time-delay system to formulate the fed-batch culture in microbial 1,3-propanediol production. For this system, we discuss some important properties of the solution. To determine the optimal time-delay and kinetic parameters in the system, we then present a robust parameter identification model subject to continuous state inequality constraints. By the constraint transcription technique and a penalty method, we transform the robust parameter identification problem to a sequence of optimization problems with only box constraints. On this basis, we propose a parallel Particle Swarm Optimization algorithm to solve the resulting problem. Finally, the numerical results show that the impulsive time-delay system can well describe the fed-batch process and the developed algorithm is highly effective. [ABSTRACT FROM AUTHOR]

• A multi-objective optimization method using game algorithm for parallel manipulators is proposed. • A new stiffness index considering the coupling effect of the non-diagonal elements is proposed. • A new index which defines the difference between the total and principal diagonal stiffness indices is proposed. • A four-dimensional intuitive image of slice distribution of the index is presented. In this paper, a multi-objective optimization game algorithm (MOOGA) for parallel manipulators (PMs) is proposed. The proposed algorithm considers the volume of the regular cylindrical workspace, motion/force transmission performance, and stiffness performance as objective functions. First, the distributions of the objective functions in the complete parameter space are calculated and sorted by importance. Second, game weighting factors and lower bound values are assigned to different objective functions according to the engineering requirements. Finally, after multiple rounds of gaming according to the weighting factors and lower bound values, the objective functions reach an optimal balance point and obtain a balance intersection subspace. In addition, a new comprehensive stiffness index (CSI) is proposed, that takes the coupling of the non-diagonal elements into consideration. This index decouples the linear and angular stiffness and has definite physical dimensions as well as a clear physical meaning. A Lagrangian function is used to obtain the maximum and minimum stiffnesses at a given position along with their corresponding directions. To compare the difference between the CSI and the principal diagonal stiffness index (PDSI), a divergence index κ is proposed. 2UPR–RPU and 2UPR–2RPU PMs are employed as examples to implement the proposed algorithm, where U, P and R denote a universal joint, prismatic pair and revolute pair, respectively. The corresponding slice distributions of the local CSI and κ in the regular cylindrical workspace are presented. Additionally, the distributions of the extreme linear stiffness indices and their corresponding directions are presented. The results show that the CSI is decreased by 99% relative to the PDSI. The numerical results demonstrate the effectiveness of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Highlights • A new Nash optimization method based on alternate elitist information exchange is presented. • Nash solutions between symmetric and alternate elitist information exchange is equivalent. • The Nash solution is nothing to do with the way of information exchange. • The Nash solution is only concerned with the information that is exchanged. • The methodology developed can be easily extended to MDO on HPC environments. Abstract Traditionally, it is considered that Nash equilibrium is based on the symmetric elitist information exchange. This paper presents a new Nash optimization method based on alternate elitist information exchange, it makes the information exchange and decision-making more convenient. Moreover, the subsequent decision-making can get the reward from the decision of the previous one, so the new method is more flexible in numerical implementation than that of the traditional symmetric elitist information exchanged algorithm. The equivalence of the solution of the new algorithm with the Nash optimization problem is proved in the paper. Then it is combined with evolutionary algorithms and/or adjoint methods to solve not only unconstrained but also constrained multi-objective optimization problems in aerodynamics. Numerical results show the potential efficiency and flexibility of new algorithms in solving complex multi-objective/disciplinary optimization problems in aeronautics. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a two-stage damage detection method for truss structures. • First stage focused on the damage localization and in the second stage, the damage severities are evaluated. • To localize the suspected damaged elements, using the concept of residual force vector, a new index is proposed. • To evaluate the damage severities, the damage detection problem formulated as an optimization problem and solved by GA. Abstract Use of optimization search algorithms is recognized as an efficient method for solving structural damage identification problems. Although these algorithms demonstrated their robustness to identify the location and extent of multiple damages in structural systems, they impose so much computational efforts to the damage assessing process that it reduces their attraction. In this paper by utilizing the concept of residual force vector, an efficient approach based on a Truss Element Damage Index (TEDI) is defined to assist in a fast and reliable prediction of damaged elements. Based on the proposed technique, the first step focuses on location detection of most probable damaged members. The healthy members will then be eliminated from the total list of variables. This can reduce the computational effort significantly. In the second step to identify damaged locations and severities, the Genetic Algorithm is employed to search for the optimum solution in the new search space resulted from the first step. Three test examples are considered to investigate the efficiency of proposed method for damage identification. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes the means to model phenomena exhibiting a possibly skewed and multimodal response. • The approach is based on calculating variables having a finite range and fixed values for the first four moments. • This paper provides the means to estimate the above variables and to quantify the corresponding sampling error. • The versatility of the method is illustrated by modeling the dynamics of an aeroelastic structure subject to flutter. Abstract This paper proposes a family of random variables for uncertainty modeling. The variables of interest have a bounded support set, and prescribed values for the first four moments. We present the feasibility conditions for the existence of any of such variables, and propose a class of variables that conforms to such constraints. This class is called staircase because the density of its members is a piecewise constant function. Convex optimization is used to calculate their distributions according to several optimality criteria, including maximal entropy and maximal log-likelihood. The flexibility and efficiency of staircases enable modeling phenomena having a possibly skewed and/or multimodal response at a low computational cost. Furthermore, we provide a means to account for the uncertainty in the distribution caused by estimating staircases from data. These ideas are illustrated by generating empirical staircase predictor models. We consider the case in which the predictor matches the sample moments exactly (a setting applicable to large datasets), as well as the case in which the predictor accounts for the sampling error in such moments (a setting applicable to sparse datasets). A predictor model for the dynamics of an aeroelastic airfoil subject to flutter instability is used as an example. The resulting predictor not only describes the system's response accurately, but also enables carrying out a risk analysis for safe flight. [ABSTRACT FROM AUTHOR]

• A level-set based robust topology optimization method considering bounded field parameters is proposed. • A variable time step strategy based on the RBF gradient function is developed to realize the separation of time and space. • The bounded field parameters are characterized through the dimension reduction method and dimension-wise method. In this paper, a robust topology optimization method considering bounded field parameters with uncertainties based on the variable time step parametric level-set method is proposed. Firstly, a variable time step strategy for level set function evolution based on the gradient of the level set function that uses radial basis function interpolation to realize the separation of time and space is developed, which can achieve better design results during topology optimization. Beyond that, the dimension reduction method and dimension-wise method based on the polynomials are used for characterization and quantification of bounded field parameters with uncertainties. Finally, the sensitivity of the robust optimization model is derived based on the shape derivative principle, which provides the basis for the implementation of gradient based optimization algorithm. Three examples illustrate the effectiveness, necessity and influence of important parameters of the robust topology optimization method considering bounded field parameters with uncertainties based on the variable time step parametric level-set method from different aspects. [ABSTRACT FROM AUTHOR]

• Cost and tool life of a hardened steel turning of are properly characterized as typical poisson random variables. • Objective functions are simultaneously optimized coupled with their respective variances. • The proposed method reduces the dimension of the multi-objective optimization problem. • Proposed confidence ellipse for Pareto points supports a decision-making based on variability and mean shift. • A multivariate robust setup for steel turning process is obtained according to the fuzzy decision-maker. This paper presents a multi-objective optimization algorithm that combines Normal Boundary Intersection method with response surface models of equimax rotated factor scores in order to simultaneously optimize multiples sets of means and variances of manufacturing processes characteristics. The algorithm uses equimax factor rotation to separate means and variances in individual and uncorrelated functions and afterwards combines them in a mean squared error function. These functions are then optimized using Normal Boundary Intersection method generating a Pareto frontier. The optimal solutions found are then filtered according to a 95% non-overlapping confidence ellipses for the predicted values of the responses and posteriorly they are assessed by a Fuzzy decision-maker index established between the volume of each confidence ellipsoid and the Mahalanobis distance between each Pareto point and its individual optima for a given weight. In order to illustrate the practical implementation of this approach, two cases involving the multi-objective optimization of the hardened steel turning process were considered: (a) the AISI 52100 hardened steel turning with CC6050 mixed ceramic inserts and (b) the AISI H13 hardened steel turning with CC 670 mixed ceramic tools. For both cases, the best setup for cutting speed (V), feed rate (f) and depth of cut (d) were adjusted to find the minimal process cost (K p) and the maximal tool life (T), both responses with minimal variance. The suitable results achieved in these case studies indicate that the proposal may be useful for similar manufacturing processes. [ABSTRACT FROM AUTHOR]

• A novel hybrid system is proposed with deterministic prediction and interval prediction. • Outlier detection and correction method are used to reduce the complexity of the series. • Data decomposition strategy is applied to decompose the original series to improve the prediction accuracy. • A multi-objective optimization algorithm is selected to determine the original weights and biases of the forecasting model. • Applied interval prediction to quantify the range of concentration change of air pollutants caused by uncertainties. A novel system regarding deterministic and interval predictions of pollutant concentration is constructed in this study, which can not only obtain higher prediction accuracy in deterministic prediction and also provide effective interval prediction of air pollutant concentration. In the deterministic prediction stage, the improved extreme learning machine combines outlier detection and correction algorithm, data decomposition strategy, and a multi-objective optimization algorithm to form a hybrid model for predicting pollutant concentration. Moreover, the applicability of the optimization algorithm was verified from theoretical and experimental analysis. In the interval prediction stage, three distributions are compared to mine, the traits of deterministic prediction errors are analyzed, and interval prediction is designed to quantify the uncertainties associated with pollutant concentration. To investigate the prediction performance of the proposed system, comparison experiments have been executed using the PM 2.5 concentration series from three cities. The results indicate that the system proposed in this paper outperforms comparison models in forecasting accuracy and has advantages for pollution prediction. [ABSTRACT FROM AUTHOR]

This paper proposes a production and differential pricing decision model in a two-echelon supply chain that involves a demand from two or more market segments. In this framework, the retailer is allowed to set different prices during the planning horizon. While integrated production-marketing management has been a key research issue in supply chain management for a long time, little attention has been given to set prices and marketing expenditures in integrated multi-site (parallel) manufacturing systems and multiple demand classes. Generally, the presence of multiple demand classes induced by different market segments may impose demand leakage and then change production plan and ordering policies throughout the supply chain system. To tackle this problem, this paper develops a novel approach in order to provide an optimal aggregate production and marketing plan by interconnecting the sales channels of the retailer and demand. A non-linear model is established to determine optimal price differentiation, marketing expenditures and production plans of manufacturing sites in a multi-period, multi-product and multi-sale channels production planning problem by maximizing total profit of the supply chain. To handle the model and obtain solutions, we propose an efficient analytical model based upon convex hulls. Finally, we apply the proposed procedure to a clothing company in order to show usefulness and significance of the model and solution method. [ABSTRACT FROM AUTHOR]

• Sampled-data control and state quantization are considered simultaneously for the interconnected system. • The derived stability conditions only depend on the parameters of subsystem to reduce the computational complexity. • A grid search based algorithm is proposed to decrease the transmission quantity of the sampled-data. In this paper, we investigate the stability analysis for interconnected system, which is composed of multiple subsystems with sampled-data control and state quantization. The controlled interconnected system is modeled as a time-delay system with time-varying delays. By employing the information of sampling instants, a discontinuous Lyapunov functional is constructed. Using this functional, we derive tractable sufficient conditions to ensure the well-posedness and asymptotic stability of the considered system by taking the interconnected structure into consideration. These conditions depend only on the sampling interval, quantizer parameters and matrices parameters of individual subsystem, making them attractive in computation verification for large scale interconnected system. Furthermore, a grid search based optimization algorithm is proposed to reduce the total number of sampled data for the whole system. The effectiveness and applicability of the main results are shown by two examples. [ABSTRACT FROM AUTHOR]

• A novel concept of optimal space-filling identifiable design is raised for exploring complex black-box models. • Iterative algorithms are designed based on track substitution to achieve optimal space-filling properties. • Symmetrical global sensitivity indices are estimated accurately based on model outputs. • Extensive theoretical and numerical results demonstrate the effectiveness of the proposed designs. In this paper, a novel concept of optimal space-filling identifiable design is proposed in the framework of symmetrical global sensitivity analysis for exploring complex black-box models. The initial identifiable design is first generated algorithmically. Then based on two commonly used measures of space filling, the ϕ q and L 2 -discrepancy criterions, two optimal space-filling identifiable designs are proposed. The corresponding optimization algorithms are also given, in which adjacent identifiable designs are produced sequentially by using track substitution until the space-filling property has been optimized. By using the resulting optimal space-filling identifiable design, symmetrical global sensitivity indices can be directly estimated based on model outputs with high precision. Extensive theoretical and numerical results demonstrate the optimality and effectiveness of the proposed designs, as well as the superiority over the existing designs in the literature. Technical details are provided in the Appendix. [ABSTRACT FROM AUTHOR]

• A novel non-intrusive sensitivity-based nonlinear finite element model updating method is proposed. • The nonlinear response sensitivity is computed using real and imaginary perturbation synchronously. • The nonlinear finite element model is updated using time history responses. • A cantilever beam with steel slice is conducted to illustrate the experimental application of the proposed method. This paper presents a novel non-intrusive sensitivity-based nonlinear finite element model updating method in which the local nonlinearity of structure is considered. The sensitivity analysis is conducted to determine the sensitivity of a dynamic response with respect to different nonlinear parameters using the real and imaginary perturbation method in one single calculation. The use of the imaginary perturbation is capable of second-order accuracy in imaginary perturbation sensitivity analysis of the nonlinear finite element model updating procedure. The nonlinear parameters in the finite element model are estimated by using the improved sensitivity-based optimization algorithm. A nonlinear multi-degree-of-freedom oscillator and a cantilever beam with multiple nonlinear supports are studied to verify the accuracy of the proposed method. The effects of measurement noise and initial parameters on the performance of the presented approach are further investigated. Then, the proposed approach is verified by an experimental test of a cantilever beam with a steel slice. The updated nonlinear finite element model is further evaluated by the structure subjected to new excitations. Results show that the proposed method can effectively update the nonlinear finite element model even in the presence of contaminated measurement data and different initial parameters in the finite element model. [ABSTRACT FROM AUTHOR]

Determination of the creep model parameters is a challenging task particularly when a non-linear elastic visco-plastic (EVP) model is adopted, mainly due to the limited test duration as well as the assumption of the reference time. Therefore, this paper presents an innovative numerical solution to find the EVP model parameters applying the trustregion reflective least square optimization algorithm. The developed approach involves several available laboratory consolidation test results in the optimization procedure with the adopted commencing time to creep as a unit of time. In this paper, the laboratory results of Ottawa clay were employed to demonstrate the limitation of the recent method to obtain model parameters. Furthermore, the developed method is verified against Skå-Edeby clay in the laboratory conditions. The EVP model parameters are obtained by applying the developed method to the available laboratory consolidation results of clay samples. The analysis results of vertical strains and excess pore water pressures demonstrate that the developed method can be a feasible tool to estimate the settlement properties of clays. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regression and particle swarm optimization algorithm. For the proposed method, the global performance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objective functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process regression and particle swarm optimization algorithm was proposed for multi-objective optimization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint; R a revolute joint; U a universal joint). The comparison from the back propagation neural network, multivariate regression, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimization algorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization , the performance indices were significantly improved. • A mapping modeling between objective functions and design parameters based on Gaussian process regression is proposed. • A multi-objective optimization design method combined with the mapping model and particle swarm algorithm is proposed. • The proposed method can significantly reduce the computational cost for the high computation-intensive design problems. • A study on the comparison of the robustness and accuracy among the three mapping models is carried out. • The three mapping models are multivariate regression, BP neural network, and Gaussian process regression models. [ABSTRACT FROM AUTHOR]

• An improved box-plot is used to modify the outliers in the original carbon price data. • The optimal parameter structure of the individual model is determined by a novel comprehensive evaluation criterion. • The adaptability between the optimum forecasting model and the predicted subseries is ensured by an original matching strategy. • A new hybrid optimization algorithm is employed to optimize the ensemble models parameters. Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets. [ABSTRACT FROM AUTHOR]

Although many methods have been proposed in the literature for rigid-body guidance synthesis none of them establishes the position of the instant center (IC) and the centrode as a design requirement. In many cases, practical designs of mechanisms need to accurately establish the position of the IC and the centrode. Due to the lack of methods tackling this problem directly, designers try to approach it indirectly. In other words, they establish the position of the desired rigid-body to obtain the desired position of the IC. However, small errors in the position of the rigid-body may lead to significant errors in the position of the IC and a trial-and-error process is necessary to achieve accuracy. The main difficulty in implementing the centrode in a synthesis approach is how to formulate the IC conditions in an optimization method. This paper presents a synthesis approach based on optimization in which the location of the centrode is included in the objective function. The formulation developed in the paper uses exact differentiation to obtain the descent direction and can be applied to any type of planar mechanism. Multi-objective optimization is also possible, including different design criteria in the target function. Several examples are provided to demonstrate the robustness, accuracy and efficiency of the new approach. [ABSTRACT FROM AUTHOR]

• Four time-delay weights structural types are designed by the grey incidence model. • The novel multivariable grey prediction model considering the time-delay periods and time-delay weights is proposed. • The accumulating impact of influencing factors on CO 2 emissions of China's transport sector is measured. CO 2 emissions from the transportation sector occupy an increasingly important proportion in China's carbon dioxide emissions. Measuring the accumulative impact of factors on carbon emissions over time is of great significance for formulating corresponding policies. This paper aims to propose a novel time-delay multivariate grey model to measure the CO 2 emissions' accumulating impact of China's transportation sector. Firstly, the grey incidence model is used to identify time lags between the input and output variables, and also analyze the structure type of time-delay weights. Then, an accumulative time-delay multivariate grey prediction model is developed. In this model, a Gaussian formula is used to discretize the convolution integral of the time response function, and the particle swarm optimization algorithm is employed to determine the optimal weight coefficients. Finally, a case for CO 2 emissions prediction is adopted to test the effectiveness and practicality of this model compared with the alternative models. The results show that the proposed model outperforms other six competing models in accordance with two measuring indices, and suggestions on emissions mitigation are proposed based on the prediction results. [ABSTRACT FROM AUTHOR]

This study develops an improved moth-flame optimization algorithm, which is a recently proposed optimizer based on moth behavior in nature. It has achieved favorable results in medical science, educational evaluation, and other fields. However, the convergence rate of the original moth-flame optimization algorithm is too fast in the running process, and it is prone to fall into local optimum, which leads to the failure to produce the high-quality optimal result. Accordingly, this paper proposes a reinforced technique for the moth-flame optimization algorithm. Firstly, the simulated annealing strategy is introduced into the moth-flame optimization algorithm to boost the advantage of the algorithm in the local exploitation process. Then, the idea of the quantum rotation gate is integrated to enhance the global exploration ability of the algorithm and ameliorate the diversity of the moth. These two steps maintain the relationship between exploitation and exploration as well as strengthen the performance of the algorithm in both phases. After that, the method is compared with ten well-regarded and ten alternative algorithms on benchmark functions to verify the effectiveness of the approach. Also, the Wilcoxon signed rank and Friedman assessment were performed to verify the significance of the proposed method against other counterparts. The simulation results reveal that the two introduced strategies significantly improve the exploration and exploitation capacity of moth-flame optimization algorithm. Finally, the algorithm is utilized to feature selection and two engineering problems, including pressure vessel design and multiple disk clutch brake problems. In these practical applications, the novel algorithm also achieves particularly notable results, which also illustrates that the algorithm is qualified is an effective auxiliary appliance in solving complex optimization problems. [ABSTRACT FROM AUTHOR]

Maintenance research catches growing attention for its increasing importance in reality. Spare parts and intermediate buffer are two important factors related to maintenance activity, and both of them may greatly influence the system cost. Although some researchers have considered maintenance and buffer inventory, or maintenance and spare parts inventory together, the joint optimization of the three has not been considered before. This paper is focused on the interaction between maintenance, buffer inventory, and spare parts inventory, to achieve the minimization of the long-term expected cost rate for a production system. The investigated production system of the paper consists of two serial machines, an intermediate buffer, and a spare part inventory. The practical example for this type of system can be an automated work center or an automobile general assembly. The cost model that represents the long-term expected cost rate is developed by mathematical analysis. Four control variables corresponding to decisions made on maintenance, buffer, and spare parts are included. Then by using the genetic algorithm method, the cost model is optimized. The proposed method is applied to different simulation cases to show its efficiency and necessity. Additionally, the related optimization results indicate that the joint optimization can effectively alleviate the influence caused by the change of buffer accumulation speed. [ABSTRACT FROM AUTHOR]

This paper deals with the ( p , N )-policy M/G/1 queue with an unreliable server and single vacation. Immediately after all of the customers in the system are served, the server takes single vacation. As soon as N customers are accumulated in the queue, the server is activated for services with probability p or deactivated with probability (1 − p ). When the server returns from vacation and the system size exceeds N , the server begins serving the waiting customers. If the number of customers waiting in the queue is less than N when the server returns from vacation, he waits in the system until the system size reaches or exceeds N . It is assumed that the server is subject to break down according to a Poisson process and the repair time obeys a general distribution. This paper derived the system size distribution for the system described above at a stationary point of time. Various system characteristics were also developed. We then constructed a total expected cost function per unit time and applied the Tabu search method to find the minimum cost. Some numerical results are also given for illustrative purposes. [ABSTRACT FROM AUTHOR]

• A linearly dynamic random heuristic crossover is presented to get stronger local search ability. • An exponentially dynamic random differential mutation strategy is used to strengthen the global search ability. • A global-best Gaussian mutation operator is adopted to balance exploitation and exploration better. • A random opposition learning strategy is used to avoid getting stuck in local optima. • The proposed algorithm is verified on the classical functions, CEC-2013 and CEC-2017, and two practical problems. The Biogeography-Based Optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel Biogeography-Based Optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and K -means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms. [ABSTRACT FROM AUTHOR]

• Interval static analysis on cracked beams with uncertain size/position of cracks. • Uncertainty in size/position of cracks is handled by proper generalized functions. • Monotonicity of response is tested with respect to all uncertain parameters. • Bounds of monotonic response are obtained by sensitivity-based method. • Bounds of non-monotonic response are computed by global optimization technique. This paper deals with beams under static loads, in presence of multiple cracks with uncertain parameters. The crack is modelled as a linearly-elastic rotational spring and, following a non-probabilistic approach, both stiffness and position of the spring are taken as uncertain-but-bounded parameters. A novel approach is proposed to compute the bounds of the response. The key idea is a preliminary monotonicity test, which evaluates sensitivity functions of the beam response with respect to the separate variation of every uncertain parameter within the pertinent interval. Next, two alternative procedures calculate lower and upper bounds of the response. If the response is monotonic with respect to all the uncertain parameters, the bounds are calculated by a straightforward sensitivity-based method making use of the sensitivity functions built in the monotonicity test. In contrast, if the response is not monotonic with respect to even one parameter only, the bounds are evaluated via a global optimization technique. The presented approach applies for every response function and the implementation takes advantage of closed analytical forms for all response variables and related sensitivity functions. Numerical results prove efficiency and robustness of the approach, which provides very accurate bounds even for large uncertainties, avoiding the computational effort required by the vertex method and Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

• A novel Grey Bernoulli model is proposed. • The final optimal nonlinear parameter is determined by particle swarm optimization algorithm. • The applicability and efficiency of the new model is proven with three case studies. • The developed model is used to forecast the natural gas consumption in five large countries. This paper investigates the natural gas consumption of the United States, Germany, the United Kingdom, China, and Japan by a new Grey Bernoulli model. Analytical formulations of the time response function, restored values, and linear parameters estimation are derived. Further, the nonlinear parameter is determined by the Particle Swarm Optimization algorithm based on the linearized form of the model. Three numerical cases are considered to verify the effectiveness of the model. Finally, with observations from 2005 to 2017 claimed by British Petroleum Statistical Review of World Energy 2018 , this new model is built to compute the natural gas consumption of the selected countries from 2018 to 2022. The numerical results show that the natural gas consumption will be increasing in the coming years. [ABSTRACT FROM AUTHOR]

• An innovative algorithm called CMFPA is proposed. • CMFPA is successfully tested well-known functions and CEC2017' functions. • CMFPA is used to solve engineering optimization problems in real life. • Experiments show that CMFPA is efficient and effective. The flower pollination algorithm (FPA) is a relatively new swarm optimization algorithm that inspired by the pollination phenomenon of natural phanerogam. Since its proposed, it has received widespread attention and been applied in various engineering fields. However, the FPA still has certain drawbacks, such as inadequate optimization precision and poor convergence. In this paper, an innovative flower pollination algorithm based on cloud mutation is proposed (CMFPA), which adds information of all dimensions in the global optimization stage and uses the designed cloud mutation method to redistribute the population center. To verify the performance of the CMFPA in solving continuous optimization problems, we test twenty-four well-known functions, composition functions of CEC2013 and all benchmark functions of CEC2017. The results demonstrate that the CMFPA has better performance compared with other state-of-the-art algorithms. In addition, the CMFPA is implemented for five constrained optimization problems in practical engineering, and the performance is compared with state-of-the-art algorithms to further prove the effectiveness and efficiency of the CMFPA. [ABSTRACT FROM AUTHOR]

• An optimization method for coal-fired power plant desulfurization systems is proposed. • The optimization method can effectively reduce the desulfurization cost. • An enhanced FCM clustering method is developed. Desulfurization systems in coal-fired power stations often suffer the problem of high operating costs caused by a rule-of-thumb control strategy, which implies great potential for optimization of the operation. Due to the complex desulfurization mechanism, frequently fluctuating unit load, and severe disturbance, it is challenging to determine the optimal operating parameters based on the traditional mechanistic models, and the operating parameters are closely related to the operational efficiency of the flue gas desulfurization system. In this paper, an operation strategy optimization method for the desulfurization process is proposed based on a data mining framework, which is able to determine online the optimal operating parameter settings from a large amount of historical data. First, Principal Component Analysis (PCA) is used to reduce data redundancy by mapping the data into a new vector space. Based on the new vector space, an enhanced fuzzy C-means clustering (Enhanced-FCM) is developed to cluster the historical data into groups sharing similar characteristics. Taking sulfur dioxide emission concentration as a constraint condition, the system is optimized with economic benefits and desulfurization efficiency as the objective function. When performing optimization, the group that current operating conditions belong to is determined first, then the operating parameters of the best performance are searched within the group and provided as the optimization results. The method is validated and tested based on the data from a wet flue gas desulfurization (WFGD) system of a 1000 MWe supercritical coal-fired power plant in China. The results indicate that the proposed operation strategy can appropriately obtain operating parameter settings at different conditions, and effectively reduce the desulfurization cost under the constraint of meeting emission requirements. [ABSTRACT FROM AUTHOR]

• The proposed framework can effectively enhance the surrogate model accuracy. • The proposed framework can extend the comprehensive performance of Pareto solutions. • Pareto solutions obtained by the new algorithm have greater diversity and stability. • Results obtained by three-objective optimization model provides broader information. • New algorithm is more applicable than the nondominated sorting genetic algorithm II. This paper proposes an online surrogate model-assisted multiobjective optimization framework to identify optimal remediation strategies for groundwater contaminated with dense non-aqueous phase liquids. The optimization involves three objectives: minimizing the remediation cost and duration and maximizing the contamination removal rate. The proposed framework adopts a multiobjective feasibility-enhanced particle swarm optimization algorithm to solve the optimization model and uses an online surrogate model as a substitute for the time-consuming multiphase flow model for calculating contamination removal rates during the optimization process. The resulting approach allows decision makers to find a balance among the remediation cost, remediation duration and contamination removal rate for remediating contaminated groundwater. The new algorithm is compared with the nondominated sorting genetic algorithm II, which is an extensively applied and well-known algorithm. The results show that the Pareto solutions obtained by the new algorithm have greater diversity and stability than those obtained by the nondominated sorting genetic algorithm II, indicating that the new algorithm is more applicable than the nondominated sorting genetic algorithm II for optimizing remediation strategies for contaminated groundwater. Additionally, the surrogate model and Pareto optimal set obtained by the proposed framework are compared with those of the offline surrogate model-assisted multiobjective optimization framework. The results indicate that the surrogate model accuracy and Pareto front achieved by the proposed framework outperform those of the offline surrogate model-assisted optimization framework. Thus, we conclude that the proposed framework can effectively enhance the surrogate model accuracy and further extend the comprehensive performance of Pareto solutions. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines whether it is possible to utilise optimisation techniques to enhance the efficiency of the experiment-based development of a new product. Conventionally, a laboratory-based experimental investigation requires to check all combinations of all independent parameters describing the product; hence the optimum levels of the tested parameters is obtained. As a result there may be many thousands of samples produced to optimise these parameters; this is a time consuming, costly and laborious process. Instead, it is suggested that an empirical model is established and validated using the results of the laboratory investigation making it suitable for optimisation. In this paper the empirical model describes the compressive strength of a new masonry unit (bitublock) in terms of two parameters that are the proportion of the coarse aggregate and the compaction pressure. The expression for the compressive strength obtained from all experimental data was optimised showing a similar result to that from an experimental investigation. A much reduced set of experimental data was then used to establish further empirical models. The predictions from these models were compared with the output from the full set of experimental data to assess the accuracy of these empirical models and arrive at recommendations for the reduction of an experimental programme for future studies. From the experimental investigation alone, the optimal compressive strength (35.82MPa) of the new unit was obtained from a mix containing 30% coarse aggregate compacted at 24MPa. Empirical modelling using only half of the original experimental data predicted that a coarse aggregate content of 31.95% and a compaction pressure of 21.73MPa would provide an optimum compressive strength of 35.95MPa. This shows that the use of optimisation techniques can improve the efficiency and economy of laboratory investigations and also has a potential to provide economies during future scaling-up and manufacturing processes, e.g. in this case, by reducing compaction pressures. [Copyright &y& Elsevier]

Abstract: In this paper, the authors show an original methodology for optimization of tool geometry used in the milling of spherical surfaces. This methodology is based on thorough theoretical research, which highlights the kinematic characteristics of spherical surface generation by milling. These characteristics lead to the conclusion that the constructive angles of tools used in spherical surface milling must have the same values on both cutting edges. The paper also presents a practical design methodology of these tools, both for exterior spherical milling, and for interior spherical milling. The theoretical research was validated by manufacturing physical tools used in processing spherical joints from 330MW turbines, as well as in processing of sphere taps used in the petrochemical industry. [Copyright &y& Elsevier]

Abstract: Recently, Chiu et al. (2012) [1] present an alternative optimization procedure to derive the optimal replenishment lot size for an economic manufacturing quantity (EMQ) model with rework and multiple shipments. This inventory model was proposed by Chiu et al. (2011) [2]. Both papers do not consider the determining of the number of shipments. This paper determines both the optimal replenishment lot size and the optimal number of shipments jointly. The solution of this paper is better than the solutions of Chiu et al. [1,2]. [Copyright &y& Elsevier]

Abstract: Group decision making is an active area of research within multiple attribute decision making. This paper assumes that all the decision makers (DMs) are not equally qualified to contribute equitably to the decision process. The aim of this paper is to develop an approach to determine weights of DMs, in which the decision information on alternatives with respect to attributes, provided by each DM, is represented in the form of interval data. We define the average of all individual decisions as the positive ideal decision (PID), and the maximum separation from PID as the negative ideal decision, which are characterized by a matrix, respectively. The weight of each DM is determined according to the Euclidean distances between the individual decision and ideal decisions. By using the obtained weights of DMs, all individual decisions are aggregated into a collective decision. Then the alternatives is ranked based on the collective decision. Meanwhile, this paper also gives a humanized decision method by using an optimistic coefficient, which is used in adjusting the relative importance between profit and risk. Finally, we give an example to illustrate the developed approach. [Copyright &y& Elsevier]

Abstract: Since the observed values of security returns in real-world problems are sometimes imprecise or vague, an increasing effort in research is devoted to study the properties of risk measures in fuzzy portfolio optimization problems. In this paper, a new risk measure is suggested to gauge the risk resulted from fuzzy uncertainty. For this purpose, the absolute deviation and absolute semi-deviation are first defined for fuzzy variable by nonlinear fuzzy integrals. To compute effectively the absolute semi-deviations of single fuzzy variable as well as its functions, this paper discusses the methods of computing the absolute semi-deviation by classical Lebesgue–Stieltjes (L–S) integral. After that, several useful absolute deviation and absolute semi-deviation formulas are established for common triangular, trapezoidal and normal fuzzy variables. Applying the absolute semi-deviation as a new risk measure in portfolio optimization, three classes of fuzzy portfolio optimization models are developed by combining the absolute semi-deviation with expected value operator and credibility measure. Based on the analytical representation of absolute semi-deviations, the established fuzzy portfolio selection models can be turned into their equivalent piecewise linear or fractional programming problems. Since the absolute semi-deviation is a piecewise fractional function and pseudo-convex on the feasible subregions of deterministic programming models, we take advantage of the structural characteristics to design a domain decomposition method to separate a deterministic programming problem into three convex subproblems, which can be solved by conventional solution methods or general-purpose software. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness of the solution method. [Copyright &y& Elsevier]

Abstract: Most of the methodologies published in literature on wastewater minimisation for batch processes are based on short term scheduling techniques. When these methods are applied to longer time horizons, the computational time becomes intractable, hence the focus of this paper. This paper presents a methodology for simultaneous optimisation of production schedule and wastewater minimisation in a multipurpose batch facility. The key feature of the presented methodology is the adaption of cyclic scheduling concepts to wastewater minimisation. The methodology is developed based on continuous-time formulation and the state sequence network (SSN) representation. The methodology is successfully applied to two common literature examples and an industrial case study to demonstrate its effectiveness. None of the currently published wastewater minimisation techniques could solve the case study for a time horizon of 168h. However, through the application of the presented methodology, a time horizon of 168h for the case study was reduced to eight cycles with the cycle length of 23h, for which the CPU time for the optimum cycle is 64.53s. [Copyright &y& Elsevier]

Abstract: Simulation is generally used to study non-deterministic problems in industry. When a simulation process finds the solution to an NP-hard problem, its efficiency is lowered, and computational costs increase. This paper proposes a stochastic dynamic lot-sizing problem with asymmetric deteriorating commodity, in which the optimal unit cost of material and unit holding cost would be determined. This problem covers a sub-problem of replenishment planning, which is NP-hard in the computational complexity theory. Therefore, this paper applies a decision system, based on an artificial neural network (ANN) and modified ant colony optimization (ACO) to solve this stochastic dynamic lot-sizing problem. In the methodology, ANN is used to learn the simulation results, followed by the application of a real-valued modified ACO algorithm to find the optimal decision variables. The test results show that the intelligent system is applicable to the proposed problem, and its performance is better than response surface methodology. [Copyright &y& Elsevier]

• A multi-strategy boosted mutative WOA is proposed for global optimization. • A chaotic initialization strategy, a chaotic local search and Gaussian mutation are incorporated into WOA. • The extensive results on benchmark problems and three classical engineering design problems reveal the excellent performance. Image, graphical abstract This paper presents an improved Whale Optimization Algorithm (WOA) for global optimization. WOA is a recently introduced meta-heuristic algorithm mimicking the hunting behavior of humpback whales. Owing to its simplicity in exploratory and exploitative operators and the satisfactory efficacy, this algorithm has found its place among the well-established population-based approach utilized in many engineering and science areas. However, this method is easy to fall into local optimum when dealing with some optimization cases. In order to further enhance its exploratory and exploitative performance, three strategies are incorporated into the original method to keep a better balance between exploitation and exploration tendencies. First, the chaotic initialization phase is introduced into the optimizer to initiate the swarm of chaos-triggered whales. Then, Gaussian mutation is employed to intensify the diversity level of the evolving population. At last, a chaotic local search with a 'shrinking' strategy is used to enhance the exploitative leanings of the basic optimizer. In order to verify the effectiveness of the improved WOA, it is compared to four meta-heuristic and state-of-the-art evolutionary algorithms on representative benchmark functions. Trial results and simulations reveal that not only the proposed improved WOA is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches. Moreover, the proposed algorithm is successfully applied to realize three constrained engineering test cases, which the results suggest that the improved WOA can effectively deal with the constrained functions as well. [ABSTRACT FROM AUTHOR]