CES-485 Approximating the Set of Pareto Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm

Most existing multiobjective evolutionary algorithms aim at approximating the PF, the distribution of the Pareto optimal solutions in the objective space. In many real-life applications, however, a good approximation to the PS, the distribution of the Pareto optimal solutions in the decision space, is also required by a decision maker. This paper considers a class of MOPs, in which the dimensionalities of the PS and PF are different so that a good approximation to the PF might not approximate the PS very well. It proposes a probabilistic model based multiobjective evolutionary algorithm, called MMEA, for approximating the PS and the PF simultaneously for a MOP in this class. In the modelling phase of MMEA, the population is clustered into a number of subpopulations based on their distribution in the objective space, the PCA technique is used to detect the dimensionality of the centroid of each subpopulation, and then a probabilistic model is built for modelling the distribution of the Pareto optimal solutions in the decision space. Such modelling procedure could promote the population diversity in both the decision and objective spaces. To ease the burden of setting the number of subpopulations, a dynamic strategy for periodically adjusting it has been adopted in MMEA. The experimental comparison between MMEA and the two other methods, KP1 and Omni-Optimizer on a set of test instances, some of which are proposed in this paper, have been made in this paper. It is clear from the experiments that MMEA has a big advantage over the two other methods in approximating both the PS and the PF of a MOP when the PS is a nonlinear manifold, although it might not be able to perform significantly better in the case when the PS is a linear manifold

An Advanced Control and Extensible Configuration for Static Var Generator

An extensible configuration is proposed for static var generator (SVG) with advanced controller included for reactive power compensation of grid. Compared with the traditional configurations, the major advantage of such system configuration is that the power modules are very flexible and easy to extend or reduce without changing the main equipment of SVG under the different voltage levels. Furthermore, in order to solve the problems of modeling uncertainty, nonlinearities, and outside disturbance by using proportion integration (PI) controller, an advanced controller is proposed based on auto disturbance rejection control (ADRC). By controlling the amount and direction of reactive current, the reactive power is generated or absorbed from SVG into power grid with fast response, which can realize the excellent dynamic compensation for both the internal and external interferences. Simulations results show that the proposed controller has better performance of the transient and steady state than PI controller. Moreover, the verification tests are executed in 380 V, 6.5 kVA experiment systems, suggesting that the excellent dynamic performance and strong robustness are achieved

Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving the efficacy of SAEAs through the development of proficient model-assisted selection methods. However, generating high-quality solutions is a prerequisite for selection. The fundamental paradigm of evaluating a limited number of solutions in each generation within SAEAs reduces the variance of adjacent populations, thus impacting the quality of offspring solutions. This is a frequently encountered issue, yet it has not gained widespread attention. This paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs. The surrogate model is employed to identify high-quality solutions for direct generation of new solutions without evaluation. To ensure dependable selection, we have introduced two tailored relation models for the selection of the optimal solution and the unevaluated population. A comprehensive experimental analysis is performed on two test suites, which showcases the superiority of the relation model over regression and classification models in the selection phase. Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.Comment: 18 pages, 9 figure

Oscillation properties of nonlinear impulsive delay differential equations and applications to population models

AbstractComparison theorem and explicit sufficient conditions are obtained for oscillation and nonoscillation of solutions of nonlinear impulsive delay differential equations which can be utilized to population dynamic models. Our results in this paper generalize and improve several known results

System Structure Risk Metric Method Based on Information Flow

Part 5: Modelling and SimulationInternational audienceThe measurement of structure risk aims to analysis and evaluate the not occurred, potential, and the objectively exist risk in system structure. It is an essential way to validate system function and system quality. This paper proposes the risk metric model and algorithm based on information flow and analysis risk trend between traditional tree structure and network-centric structure

Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems

Cognitive diagnosis assessment is a fundamental and crucial task for student learning. It models the student-exercise interaction, and discovers the students' proficiency levels on each knowledge attribute. In real-world intelligent education systems, generalization and interpretability of cognitive diagnosis methods are of equal importance. However, most existing methods can hardly make the best of both worlds due to the complicated student-exercise interaction. To this end, this paper proposes a symbolic cognitive diagnosis~(SCD) framework to simultaneously enhance generalization and interpretability. The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters. Meanwhile, the accompanying challenge is that we need to tunnel the discrete symbolic representation and continuous parameter optimization. To address this challenge, we propose to hybridly optimize the representation and parameters in an alternating manner. To fulfill SCD, it alternately learns the symbolic tree by derivative-free genetic programming and learns the student and exercise parameters via gradient-based Adam. The extensive experimental results on various real-world datasets show the superiority of SCD on both generalization and interpretability. The ablation study verifies the efficacy of each ingredient in SCD, and the case study explicitly showcases how the interpretable ability of SCD works