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• 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]

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multilinear model predictive control (MLMPC) can regulate a nonlinear process with wide operating regions based on a set of linear models. Although online computational cost is reduced compare to nonlinear MPC (NMPC), it is difficult to obtain a reliable full nonlinear model or set of linear models in practice. In this paper, we propose a combination of MLMPC with differential dynamic programming (DDP), so that the system can be controlled offset-free in the absence of a full nonlinear model. DDP is a 'trajectory-centric' optimization technique that solves nonlinear optimal control problems. The trajectory can be optimized even if the full model for the system is unknown, because DDP uses only the gradients around the visited trajectory, which is easily obtained by input excitations. Moreover, the gradient information can provide linear models in the subsequent MLMPC step. In the proposed scheme, a novel model selection based on gap metric and weighting method are employed for MLMPC. We prove the offset tracking property of DDP assisted MLMPC. A continuous stirred tank reactor (CSTR) process is studied to demonstrate the effectiveness of the proposed algorithms. Simulation studies show that CDDP designed by the proposed algorithm improves the trajectory over iterations, and the resulting MLMPC achieves offset-free tracking property regardless of an initial point and a set-point in the operating region. • Data-driven DDP is proposed to find the optimal trajectory without models of nonlinear systems. • LTV model is obtained from Data-driven DDP and exploited for the model of MLMPC. • A novel clustering method based on gap metric is proposed to choose linear models for MLMPC. • MLMPC with a novel prediction-based weighting method is proposed and its stability is discussed. • The proposed MLMPC strategy achieves offset-free control over large operating ranges. [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]

This paper proposes a novel robust supervised subspace learning (RSSL) method for output-relevant prediction and detection against outliers. RSSL learns the robust subspaces by optimizing a joint problem over both the prediction of output and the reconstruction of input. To this end, the learned subspaces/data representations are informative, i.e., they are encapsulated with the critic information related to both the input and output, and thus can benefit the following tasks of output-related modeling and detection. Besides, we separate sparse items from the raw measurements to suppress the effects of outliers. An efficient optimization algorithm is designed to solve the optimization problem of RSSL. We further conduct post orthogonal decomposition upon the subspaces provided by RSSL so that the trimmed subspaces are more suitable for output-related detection. The efficacy of the proposed method is extensively verified by synthesis data and benchmark data. • We learn robust latent subspaces by a joint optimization of input and output, where the negative effects of outliers are suppressed by separating sparse items from the original data. • We develop an efficient algorithm to solve the proposed formulation of RSSL. The theoretical and empirical analysis demonstrate the effectiveness of the designed optimization algorithm. • The proposed RSSL is competent to the tasks of output prediction and output-related detection, where, for the latter task, post-decomposition is performed to further trim the subspaces generated by RSSL. [ABSTRACT FROM AUTHOR]

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. [Display omitted] • A multi-objective optimization model of cement calcination process is developed. • A TDRM-Jaya algorithm is proposed applied to solve the optimization model. • The multi-objective collaborative optimization using data-driven model is studied. • Computational results show that the expected optimization effect can be achieved. [ABSTRACT FROM AUTHOR]

Abstract Capturing an uncontrolled space target is a tremendously challenging research topic. Target capture by a space robot can be well planned according to predicted motion of the target. In this paper, motion prediction of an uncontrolled space target is studied and a motion prediction algorithm is proposed. In the proposed algorithm, firstly a method for identifying the parameters of motion state and inertial property of the target is established; and then through substituting the identified parameters into the dynamic equations of the target, the motion of the target can be predicted as the solution of the equations. In the identification of the parameters, the unscented Kalman filter (UKF) is applied. In order to support the UKF, a method for estimating noise level of the observation data is developed, so our motion prediction algorithm is noise adaptive. A practical convergent criterion is also designed to determine the time when the estimated result of the UKF is accurate enough, such that the predicted motion is credible enough. After that, the accuracy of the prediction is further improved by an optimization method. In the end of this paper, numerical simulations are done to verify the validity of the proposed motion prediction algorithm. Simulation results indicate that the proposed algorithm is able to predict the motion of the target precisely. [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]

Today, the Structural Health Monitorin (SHM) methodology is the main way to deal with the detection and identification of damages in a range of engineering sectors, mainly in the military and civil aerospace industry. The need to monitor damages and failures in increasingly complex structures has led to the development of various detection techniques. Identification of damages through intelligent signal processing and optimization algorithms are particularly emphasized. The methods discussed here are mainly elaborated by the evaluation of modal data due to the great potential of their application. Moreover, the optimization of damage identification is approached through methods of optimal positioning of sensors for the acquisition of the data that must be evaluated so that conclusions can be made. This article discusses the use of computational and intelligent techniques for structural monitoring in the form of a review with emphasis on composite materials. Despite the excellent mechanical performance already known about composite materials, they have a weak point. While damages, in a metallic material are easily visible (in some cases), composite materials often have the superficial appearance as if in perfect condition, when, inside, there are serious damages. This paper can be seen as a guideline or a starting point for developing and improving SHM systems. The contents of this paper aim to help engineers and researchers find a starting point in developing a better solution to their specific structural monitoring problems, either by inverse methods, pattern recognition, and intelligent signal processing. [ABSTRACT FROM AUTHOR]

Let B be a set of b blue points and R be a set of r red points in the plane. In this paper we study the problem of finding rectangles that contain the maximum number of blue points without containing any red points, known as the maximum-box problem . First we study this problem for axis-aligned rectangles, and propose an exact worst-case optimal O ( r 2 + r b + b log ⁡ b ) time algorithm using O ( r + b ) space to find all maximum boxes. We also provide a 2-approximation algorithm running in O ( ( r + b ) log ⁡ ( r + b ) ) time and using O ( r + b ) space to find a single maximum box in the axis-aligned case. Then we generalize the exact algorithm for the axis-aligned case to find all arbitrarily oriented maximum boxes leading to a worst-case optimal O ( ( r + b ) 2 ( r + log ⁡ b ) ) time algorithm using O ( ( r + b ) 2 ) space to solve the problem. We conclude the paper by discussing time and space trade-offs. Our results improve the previously best known solutions to the maximum-box problem. [ABSTRACT FROM AUTHOR]

This paper considers the cooperative attack with multiple missiles on a stationary target and a two-step procedure is proposed. First the states of all missiles are guided to achieve minimum time consensus, and then, by using the proportional navigation guidance law with identical navigation gain each missile arrives at the target simultaneously or sequentially. Among which, the strategy to realize state consensus in minimum time is the focus of the issue. In the paper the coordination algorithms are developed based on bang–bang control strategy due to its simply structure, subsequently, it is proved that the time optimal consensus problem can be transformed into a parameter optimization problem. Besides, the proposed schemes are able to significantly reduce the number of parameters to be optimized and finally solve the problem with little computation cost. Different from the impact time control methods, the strategy for state consensus does not demand the information of time-to-go. Meanwhile only the initial values of missiles' states are required for the coordination algorithms which lead to less communication burden. Two different examples are provided to demonstrate the validity and efficiency of our proposed strategy. [ABSTRACT FROM AUTHOR]

Discretization of the state and control is indispensable for solving a trajectory planning problem numerically via convex optimization, and uniform-grid discretization is the most common strategy in recent studies. However, when a higher-precision solution is desired, this kind of discretization will greatly increase the scale of optimization variables and then reduce the optimization efficiency. To remedy this weakness, a novel trajectory planning strategy for spacecraft relative motion is proposed in this paper by combining convex optimization and multi-resolution technique (MRT). In this optimization strategy, convex optimization works as an inner-layer algorithm for trajectory optimization, and the MRT works as an outer-layer algorithm for mesh refinement. Moreover, the no-fly zone constraints are considered in the trajectory optimization, and the affine approximations of the spherical and ellipsoidal no-fly zones are derived by the analytical formula of the tangent plane. Numerical simulations demonstrate the effectiveness of the proposed methods. Results show that the combined optimization method can adaptively adjust the local grid density according to the designed resolution level and performs better in computing efficiency than the traditional uniform discretization. • Convex optimization and mesh refinement are combined for trajectory optimization. • Affine approximation of no-fly zone is derived by surface tangent-plane formula. • Mesh refinement for collision avoidance is proposed to enhance trajectory safety. [ABSTRACT FROM AUTHOR]

This paper proposes a solution for multiple-impulse orbital maneuvers near circular orbits for special cases where orbital observations are not globally available and the spacecraft is being observed through a limited window from a ground or a space-based station. The current study is particularly useful for small private launching companies with limited access to global observations around the Earth and for orbital maneuvers around other planets for which the orbital observations are limited to the in situ equipment. An appropriate cost function is introduced for the sake of minimizing the total control/impulse effort as well as the orbital uncertainty. It is subsequently proved that for a circle-to-circle maneuver, the optimization problem is quasi-convex with respect to the design variables. For near circular trajectories the same cost function is minimized via a gradient based optimization algorithm in order to provide a sub-optimal solution that is efficient both with respect to energy effort and orbital uncertainty. As a relevant case study, a four-impulse orbital maneuver between circular orbits under Mars gravitation is simulated and analyzed to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

This paper presents a new optimization algorithm called poor and rich optimization (PRO). This algorithm is inspired by the efforts of the two groups of the poor and the rich to achieve wealth and improve their economic situation. The rich always try to increase their class gap with the poor by gaining wealth from different ways. The rich are always trying to increase their class gap with the poor by acquiring wealth from different ways. On the other hand, the poor try to gain wealth and reduce their class gap with the rich. On the other hand, the poor try to gain wealth and reduce their class gap by modeling the rich. This struggle is always going on and should be mention that the poor may get rich and vice versa. The proposed algorithm is evaluated using 33 test functions and the simulation results are compared with a number of new and well-known optimization algorithms. The evaluation domain includes uni-modal, multi-modal, fixed dimension, hybrid and large scale functions. In addition, for more precise evaluation, Tension/compression spring design, pressure vessel design, Gear drain design, and three-bar truss design problems are solved by PRO algorithm. PRO algorithm has had better performance in these four problems by finding optimal values of parameters as compared to other algorithms. Finally, PRO algorithm was used to estimate software effort by UCP for more accurate evaluation. The obtained results confirmed the superiority of PRO in exploration, exploitation and convergence aspects, compared to other algorithms. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective optimization (EMO) algorithms are actively used for answering optimization problems with multiple contradictory objectives and scheming interpretable and precise real-time applications. A majority of existing EMO algorithms performs better on two or three objectives non-dominated problems; however, they meet complications in managing and maintaining a set of optimal solutions to multi-objective optimization problems. This paper proposes a search space-based multi-objective evolutionary algorithm (SSMOEA) for multi-objective optimization problems. To accomplish the potential of the search space-based method for solving multi-objective optimization problems and to reinforce the selection procedure toward the ideal direction while sustaining an extensive and uniform distribution of solutions is our key objective. To the best of our knowledge, this paper is the first attempt to propose a search space-based multi-objective evolutionary algorithm for multi-objective optimization. The experimental setup used showed that the proposed algorithm is good and competitive in comparison to the existing EMO algorithms from the viewpoint of finding a scattered and estimated solution set in multi-objective optimization problems. SSMOEA can achieve a good trade-off between search space convergence and search space diversity in the appropriate experimental setup. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to review the practice of the theory of inventive problem solving (TRIZ) in Green Supply Chain (GSC) problems and to identify new research challenges focusing on the question: “To what extent is it necessary to evolve TRIZ tools, methods and theoretical grounding for addressing GSC inventive problems?” First, a review of the past contributions of TRIZ based methods to GSC problem resolution is presented. As the result of the papers review did not provide a comprehensive understanding of the limitations and areas of potential application of TRIZ in GSC, three experiments were conducted to complete the literature review, in order to provide a more comprehensive answer to the posed question and identify research challenges. The experiments addressing GSC problems were also conducted to explore to what extent the more mature meta-methods of classical TRIZ, namely ARIZ 85 A, C and the related sub-methods, can be used as in GSM problems. The examples were chosen to explore types of GSC problems that were not yet addressed with TRIZ. The experiment results highlight limitations on the use of the TRIZ in GSC inventive problems, which were not mentioned in the GSC literature. Moreover it highlights the limitation of using the more mature meta-methods of TRIZ (ARIZ 85A and ARIZ 85C) when the conflict to overcome contains more than two evaluation parameters and one action parameter. Finally, research challenges to overcome the limitations and to improve the use of TRIZ in GSC inventive problems are stated. Among them, methods for quickly establishing the existence of classical TRIZ contradictions or for informing the problem solver when no TRIZ contradictions are present in a given inventive problem in GSC should be proposed. Such methods would permit determining whether ARIZ 85C could be used and avoid a long and fruitless search for a system of contradictions. Find alternatives to the algorithms proposed in the past to be able to establish the generalized contradictions of inventive problems. Make evolve meta-methods ARIZ 85C or substitute it with methods which can address the inventive problems that cannot be treated by ARIZ 85C. [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]

• The significant and impressive performance of the Chimp Optimization Algorithm (ChOA) has been a great inspiration for this paper to study on its multi-objective version called MOChOA. • A memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. • Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. The Multi-Objective Chimp Optimization Algorithm (MOChOA), a multi-objective variation of the recently proposed ChOA, is developed in this research to address multi-objective optimization issues in various engineering problems. The optimization community has recently been offered numerous evolutionary and meta -heuristic optimization strategies for tackling optimization challenges. When analyzing multi-objective optimization (MOO) problems, these approaches frequently produce poor solutions because they do not accurately estimate the Pareto optimal solutions or increase the distribution across all objectives. The significant and impressive performance of the ChOA has been a great inspiration for this paper to study on its multi-objective version called MOChOA. In this approach, a memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. According to results, MOChOA can provide competitive results and outperform well-known intelligent algorithms such as ANSGAIII, ARMOEA, dMOPSO, EFRRR, IBEA, SPEA2SDE, SPEAR, and tDEA. Alongside the remarkable performance of the proposed algorithm, its light-weight structure causes a fast execution. The proposed MOChOA can be applied in various multi-objective optimization application, including engineering, science, chemical processes, economics and logistics when appropriate decisions must be made in the face of competing objectives. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

• This paper develops a data aggregation technique using optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally data aggregation. • At first, the WSN-based IoT nodes are simulated, which are subjected to multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to base station. • In base station, the data aggregation is done using proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. • Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in WSN-based IoT network. The Internet of Things (IoT) is emerging that acquired the interest of several researchers, as it inspires each object to offer services and data to users by collaborating with each other. The Wireless sensor network (WSN) is a major constituent of IoT in transmitting data. Various wireless sensor-based protocols are developed for path selection. This paper develops a data aggregation technique using an optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally, data aggregation. At first, the WSN-based IoT nodes are simulated, which are subjected to a multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to the base station. In the base station, the data aggregation is done using the proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using the Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in a WSN-based IoT network. The developed DECSA+HABLMS offered superior performance with minimal prediction error of 0.0147. [ABSTRACT FROM AUTHOR]

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before. [ABSTRACT FROM AUTHOR]

This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC'2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising. • This paper introduces a modified mAGDE based on the integration of a modified three-phases AGDE with the DE_SPA algorithm. • A modified three-phases AGDE has been introduced to maintain diversity and convergence of the evolutionary process. • The CEC'2020 test suite problems are used to demonstrate the performance mAGDE against other competitors. • Applying for first time mAGDE to identify the optimal parameters of SOFC model. • The robustness of the proposed strategy-based mAGDE is proved. [ABSTRACT FROM AUTHOR]

Highlights • Developing a new Spiking Neural P Grey Wolf Optimization System. • Novel strategies for solving non-determinism problem of Spiking Neural P systems. • Design a new mathematical model of the grey wolf optimization algorithm. • Control the copying spikes process among neurons is developed. • Consider the time control approach to avoid non-determinism inside neurons. Abstract Spiking neural P systems (SN P systems, in short) are the latest branch of membrane computing; inspired by the biological behavior of spiking neurons. They are considered true distributed and parallel systems; modeled to solve time consumption problem and presented the concept of parallelism usage in the computing field. This paper proposes novel strategies for solving non-determinism problem of SN P systems. The proposed algorithm relies on the parallelism feature to simulate the social hierarchy, tracking, encircling, and attacking behaviors in the grey wolf optimizer. It is modeled by collaboration between a set of SN P systems to get a feasible solution in polynomial time. Moreover, a new method named the power of signal is proposed to control the copying spikes process between neurons and differentiate between the arithmetic operations. Additionally, a time control approach is proposed to avoid non-determinism inside neurons that applied the determinism feature during firing rules. The theoretical and empirical experiments proved that the algorithm can successfully halt and in addition to the effectiveness of the proposed neural systems in getting an optimal solution in a reasonable time. As a result, this study is counted as a significant advancement in intelligent and optimization systems, whereas it has a direct impact on enhancing the performance of these systems and their applications. [ABSTRACT FROM AUTHOR]

Highlights • The proposed algorithm considers a double pair of evolutionary operators. • The proposed approach uses local consensus formulations with reactive responses. • The proposed method guarantees leaderless movement decisions. • The proposed algorithm outperforms several evolutionary methods. Abstract Evolutionary Computation (EC) algorithms have been proposed as stochastic methods to solve complex optimization problems. The design of EC methods typically involves the construction of empirical operators based on abstractions of animal behaviors or physical and biological phenomena. Through its operators, every EC approach proposes a particular solution to the exploration-exploitation balance which is currently considered an unsolved problem within EC literature. On the other hand, multi-agent systems have been utilized as intelligent, cooperative and self-organized structures where the synergy of simple rules creates complex interactions among agents. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO is based on typical processes present in multi-agent systems, such as local consensus formulations and reactive responses. These operations are conducted by using appropriate operators that are applied in each evolutionary stage. A traditional EC algorithm considers in its operation the application of every operator without examining its final impact in the searching process. In contrast to other EC techniques, the proposed method uses additional operators to avoid the undesirable effects produced by the over-exploitation or suboptimal exploration of conventional operations. In order to illustrate the performance and accuracy of the proposed NCCO approach, it is compared to several well-known, state-of-the-art algorithms over a set of benchmark functions and real-world design applications. The experimental results demonstrate that NCCO's performance is superior to the test algorithms. [ABSTRACT FROM AUTHOR]

Highlights • Depending on clustering algorithm used, the results may change. • Based on Multi-Algorithm Voting, robustness of cluster solutions can be improved. • OMAV is developed that uses an optimization algorithm as integrative method. • OMAV is applied to the example of country clustering using GLOBE data. • Increased robustness and reduced subjectivity are demonstrated. Abstract Currently, the influence of a single statistical cluster algorithm on the results of clustering procedures represents a major threat to the objectivity in clustering. To exemplify this question, this paper refers to country clustering in cross-cultural research. In this field, previous research has determined differing numbers of clusters, depending on choices available for the clustering procedure, leading to a high number of inconsistent results. Hence, it is argued that the variety in cluster solutions induced by the choice of different statistical cluster algorithms should be reduced. To this end, this study builds on Multi-Algorithm Voting (MAV) procedure introduced by Bittmann and Gelbard (2007) and presents an advancement to the MAV method. Specifically, MAV procedure is refined for the analysis of larger data sets using the simulated annealing algorithm for optimization. The use of this Optimized MAV (OMAV) is then demonstrated for country clustering in cross-cultural research. Specifically, a set of 57 countries is divided into 12 clusters based on work-related values obtained from GLOBE database reported in House et al. (2004). Thus, results clearly show that the objectivity of clustering results can be significantly improved based on OMAV. Implications for expert and intelligent systems on the use of OMAV are discussed. Namely, OMAV represents a powerful tool supporting the decision-making process in cluster analysis reducing the number of subjective and arbitrary decisions. Taken together, this study contributes to existing literature by providing an integrative and robust method of country clustering using OMAV and by presenting country clusters applicable to various settings. [ABSTRACT FROM AUTHOR]

Highlights • Two binary variants of the butterfly optimization algorithm (BOA) are proposed. • Two transfer functions are used to map the continuous search space to discrete one. • Twenty one UCI datasets are utilized in the experiments. • A superior performance of the proposed binary variants is proved in the experiments. Abstract In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. [ABSTRACT FROM AUTHOR]

Highlights • Quantitative approach to select master degrees of freedom in shear building models. • Massive simulation were executed and their results analyzed. • Three modal error norms and one displacement error signal norm are defined. • Symmetric choice of master degrees of freedom yields the smallest errors. • Results quantitatively confirm "educated" choices made in Civil Engineering. Abstract A quantitative criteria to select master degrees of freedom when identifying reduced order models (ROMs) of one and three dimensional building models is proposed. The goal is achieved by performing intensive simulations of seismic responses of a large set of regular shear building models, that is constructed around a nominal model, whose inter-story mass and stiffness are randomly modified. Different levels of model reduction are selected and, for each one, an exhaustive selection of master degrees of freedom is realized. The parameters corresponding to all the ROMs are recovered by applying recursive least squares (RLS). Later, the response of the full order model (FOM) is compared with each one of the multiple ROMs response to the same seismic excitation signal, using for that purpose three error norms related with modal information: period, mode shape and participation factors and one error norm related to displacement errors. To analyze results, ROMs are classified in three groups that depend on their selection; the first group corresponds to lower-middle-upper story selection, the second group for lower-upper stories and the third group to all other possible cases. Results indicate first that the modal error norms can be used as a good predictor of the displacement error norms and second that symmetric selections of stories in the ROMs are the most accurate. These results are consistent with qualitative or "educated" choices made normally in Civil Engineering, that however, lacked a quantitative criteria similar to the one presented in this paper. Finally, a simulated example where a twelve stories model is reduced to a three stories model is presented to illustrate the results. [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to present a signal processing algorithm that, applied to the raw Locked Mode signal, allows us to obtain a disruption indicator in principle exploitable on different tokamaks. A common definition of such an indicator for different machines would facilitate the development of portable systems for disruption prediction, which is becoming of increasingly importance for the next tokamak generations. Moreover, the indicator allows us to overcome some intrinsic problems in the diagnostic system such as drift and offset. The behavior of the proposed indicator as disruption predictor, based on crossing optimized thresholds of the signal amplitude, has been analyzed using data of both JET and ASDEX Upgrade experiments. A thorough analysis of the disruption prediction performance shows how the indicator is able to recover some missed and tardy detections of the raw signal. Moreover, it intervenes and corrects premature or even wrong alarms due to, e.g., drifts and/or offsets. [ABSTRACT FROM AUTHOR]

Abstract Bacterial foraging optimization (BFO), based on the social foraging behaviors of bacteria, is a new intelligent optimizer. It has been widely accepted as an optimization algorithm of current interest for a variety of fields. However, compared with other optimizers, the BFO possesses a poor convergence performance over complex optimization problems. To improve the optimization capability of the BFO, in this paper a bare bones bacterial foraging optimization (BBBFO) algorithm is developed. First, a chemotactic strategy based on Gaussian distribution is incorporated into this method through making use of both the historical information of individual and the share information of group. Then the swarm diversity is introduced in the reproduction strategy to promote the exploration ability of the algorithm. The performance of BBBFO is verified on various benchmark functions, the comparative results reveal that the proposed approach is more superior to its counterparts. [ABSTRACT FROM AUTHOR]

Highlights • Introduction and modeling of the Packaging and Shipping Problem motivated by a real-life e-commerce application. • Design of a three-phase matheuristic to efficiently tackle the Packaging and Shipping Problem. • Computational comparison with a state-of-the art commercial solver. Abstract E-commerce has been continuously growing in the last years to a primary retail market. Recently in France, the threshold of 1 billion of online transactions was overcome. Due to a high demand fluctuation of e-commerce, the workforce sizing for the logistic chain is a challenging problem. Companies have to develop good strategies to have a sustainable workforce size while guaranteeing a high-level service. In this paper, we consider the management of the workforce for a warehouse of an e-commerce company. Specifically, we address issues as i) How the workforce at the warehouse can be determined; ii) What is the daily operational production planning; iii) How the demand peaks can be smoothed, and the production maintained ideally constant over the time horizon. To provide answers to these issues, we introduce the Packaging and Shipping Problem (PSP). The PSP looks for a solution approach that jointly determines the workforce over a multi-period horizon and daily operational plans while minimizing the total logistics cost. We consider two strategies that aim to enhance the flexibility of the process and the efficiency of resources use: reassignment and postponement. To tackle the Packaging and Shipping Problem we propose a model, and a three-phase matheuristic. This heuristic is proved to be competitive with respect to the direct solution of the model with a commercial solver on real-life based instances. [ABSTRACT FROM AUTHOR]