Even Search in a Promising Region for Constrained Multi-Objective Optimization

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

A 3D Nonhydrostatic Compressible Atmospheric Dynamic Core by Multi-moment Constrained Finite Volume Method

A 3D compressible nonhydrostatic dynamic core based on a three-point multi-moment constrained finite-volume (MCV) method is developed by extending the previous 2D nonhydrostatic atmospheric dynamics to 3D on a terrainfollowing grid. The MCV algorithm defines two types of moments: the point-wise value (PV) and the volume-integrated average (VIA). The unknowns (PV values) are defined at the solution points within each cell and are updated through the time evolution formulations derived from the governing equations. Rigorous numerical conservation is ensured by a constraint on the VIA moment through the flux form formulation. The 3D atmospheric dynamic core reported in this paper is based on a three-point MCV method and has some advantages in comparison with other existing methods, such as uniform third-order accuracy, a compact stencil, and algorithmic simplicity. To check the performance of the 3D nonhydrostatic dynamic core, various benchmark test cases are performed. All the numerical results show that the present dynamic core is very competitive when compared to other existing advanced models, and thus lays the foundation for further developing global atmospheric models in the near future.

Novel Adaptive Simulated Annealing Algorithm for Constrained Multi-Objective Optimization

In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.

Novel constrained multi-objective biogeography-based optimization algorithm for robot path planning

A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives, and the distance from the obstacles was constraint. In CMBOA, a new migration operator with disturbance factor was designed and applied to the feasible population to generate many more non-dominated feasible individuals; meanwhile, some infeasible individuals nearby feasible region were recombined with the nearest feasible ones to approach the feasibility. Compared with classical multi-objective evolutionary algorithms, the current study indicates that CM- BOA has better performance for RPP.

EQUILIBRIUM EXISTENCE FOR MULTI-LEADER-FOLLOWER GENERALIZED CONSTRAINED MULTIOBJECTIVE GAMES IN LOCALLY FC-UNIFORM SPACES

In this article, we introduce and study some new classes of multi-leader-follower generalized constrained multiobjective games in locally FC-uniform spaces where the number of leaders and followers may be finite or infinite and the objective functions of the followers obtain their values in infinite-dimensional spaces. Each leader has a constrained correspondence. By using a collective fixed point theorem in locally FC-uniform spaces due to author, some existence theorems of equilibrium points for the multi-leader-follower generalized constrained multiobjective games are established under nonconvex settings. These results generalize some corresponding results in recent literature.

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

Constrained multi-degree reduction of rational Bézier curves using reparameterization

Applying homogeneous coordinates, we extend a newly appeared algorithm of best constrained multi-degree reduction for polynomial Bezier curves to the algorithms of constrained multi-degree reduction for rational Bezier curves. The idea is introducing two criteria, variance criterion and ratio criterion, for reparameterization of rational Bezier curves, which are used to make uniform the weights of the rational Bezier curves as accordant as possible, and then do multi-degree reduction for each component in homogeneous coordinates. Compared with the two traditional algorithms of ＂cancelling the best linear common divisor＂ and ＂shifted Chebyshev polynomial＂, the two new algorithms presented here using reparameterization have advantages of simplicity and fast computing, being able to preserve high degrees continuity at the end points of the curves, do multi-degree reduction at one time, and have good approximating effect.

New Zircon U-Pb Age of the Babu Ophiolites in Southeast Yunnan,China and Constrains of Plate Subduction Time

Objective The Babu ophiolite in Malipo County of southeastern Yunnan is interpreted as remanant ocean crust and represents a possible branch of Paleo-Tethyan Ocean in South China. It consists mainly of mafic and ultramafic rocks. These rocks are very important to understand the evolution of the Paleo-Tethyan Ocean. However, the Babu ophiolite is still disputed and the mafic and ultramafic rocks have been inferred to be part of the Emeishan large igneous province （LIP） by some researchers. In this paper, we present zircon U-Pb data on the metabasalts in Malipo to reveal the formation time of mafic and ultramafic rocks and their tectonic nature.

An iterated local coordinate-exchange algorithm for constructing experimental designs for multi-dimensional constrained spaces

Iterated local search(ILS)is used to construct the optimal experimental designs for multi-dimensional constrained spaces,in which the inner loop is based on the stochastic coordinate-exchange(SCE)algorithm.Every time a local optimal solution is found by the SCE algorithm,the perturbation operator is applied to it,and then a new solution is explored in the areas where the exchange of coordinates may produce improvement,so as to retain the features and attributes of the current optimal solution and avoid the defects of random restart.We implement the iterated local coordinate-exchange algorithm for experimental designs in the multi-dimensional constrained spaces.In addition,sensitivity analysis was conducted to analyze the impacts of the parameters on the performance of the proposed algorithm.Also we compared the performance of the proposed algorithm to the SCE algorithm using the random restart strategy.The analysis shows that the proposed algorithm is better than the SCE algorithm in terms of efficiency and quality,especially in the experimental designs for high-dimensional constrained space.

Immune Optimization Approach for Dynamic Constrained Multi-Objective Multimodal Optimization Problems

This work investigates one immune optimization approach for dynamic constrained multi-objective multimodal optimization in terms of biological immune inspirations and the concept of constraint dominance. Such approach includes mainly three functional modules, environmental detection, population initialization and immune evolution. The first, inspired by the function of immune surveillance, is designed to detect the change of such kind of problem and to decide the type of a new environment;the second generates an initial population for the current environment, relying upon the result of detection;the last evolves two sub-populations along multiple directions and searches those excellent and diverse candidates. Experimental results show that the proposed approach can adaptively track the environmental change and effectively find the global Pareto-optimal front in each environment.

Discovery of the Dagele Eclogite in East Kunlun,Western China and Its Zircon SHRIMP U-Pb Ages:New Constrains on the Central Kunlun Suture Zone

Objective Eclogites are important indicators of ancient plate boundaries or paleosuture zones. Despite their great geological significance, very few investigations have been carried out in the Kunlun region. The Central East Kunlun fault zone was believed to be an Early Paleozoic suture zone, but there has been no reliable evidence for this, though studies on ophiolite, granite, and basic granulite indicate that the Early Paleozoic orogeny occurred in the East Kunlun. This work focused on the Dagele eclogites in Central East Kunlun to provide new constraints for the Central East Kunlun suture zone.

A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems

The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.

Petrogenesis of the Early Cretaceous Laojunshan monzogranite at the southern margin of the North China Craton: Constrains on the transition of the tectonic regime

1 Introduction Voluminous Mesozoic magmatic rocks containing abundant Au-Mo polymetallic mineralization resources are developed in the Xiaoqinling-Xiong’ershan district of the southern margin of the North China Craton(NCC).

MULTI-LAYER PIEZOELECTRIC ACTUATOR AND ITS APPLICATION IN CONTROLLABLE CONSTRAINED DAMPING TREATMENT

A kind of novel multi-layer piezoelectric actuator is proposed and integrated with controllable constrained damping treatment to perform hybrid vibration control. The governing equation of the system is derived based on the constitutive equations of elastic, viscoelastic and piezoelectric materials, which shows that the magnitude of control force exerted by multi-layer piezoelectric actuator is the quadratic function of the number of piezoelectric laminates used but in direct proportion to control voltage. This means that the multi-layer actuator can produce greater actuating force than that by piezoelectric laminate actuator with the same area under the identical control voltage. The optimal location placement of the multi-layer piezoelectric actuator is also discussed. As an example, the hybrid vibration control of a cantilever rectangular thin-plate is numerically simulated and carried out experimentally. The simulated and experimental results validate the power of multi-layer piezoelectric actuator and indicate that the present hybrid damping technique can effectively suppress the low frequency modal vibration of the experimental thin-plate structure.

The Budget Constrained Multi-product Newsboy Problem with Reactive Production:A Problem from Entrepreneurial Network Construction

This paper develops an extended newsboy model and presents a formula- tion for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be used to describe the status of entrepreneurial network construction. We use the Lagrange multiplier procedure to deal with our problem, but it is too complicated to get the exact solu-tion. So we introduce the homotopy method to deal with it. We give the flow chart to describe how to get the solution via the homotopy method. We also illustrate our model in both the classical procedure and the homotopy method. Comparing the two methods, we can see that the homotopy method is more exact and efficient.

Low-rank constrained weighted discriminative regression for multi-view feature learning

In recent years,multi-view learning has attracted much attention in the fields of data mining,knowledge discovery and machine learning,and been widely used in classification,clustering and information retrieval,and so forth.A new supervised feature learning method for multi-view data,called low-rank constrained weighted discriminative regression(LWDR),is proposed.Different from previous methods handling each view separately,LWDR learns a discriminative projection matrix by fully exploiting the complementary information among all views from a unified perspective.Based on least squares regression model,the highdimensional multi-view data is mapped into a common subspace,in which different views have different weights in ptojection.The weights are adaptively updated to estimate the roles of all views.To improve the intra-class similarity of learned features,a low-rank constraint is designed and imposed on the multi-view features of each class,which improves the feature discrimination.An iterative optimization algorithm is designed to solve the LWDR model efficiently;Experiments on four popular datasets,including Handwritten,CaltechlOl,PIE and AwA,demonstrate the effectiveness of the proposed method.