Lihua Dou

Optimal Contraction Theorem for Exploration–Exploitation Tradeoff in Search and Optimization

Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and convergence speed of optimization algorithms. In this paper, definitions of exploration and exploitation are first given based on information correlation among samplings. Then, some general indicators of optimization hardness are presented to characterize problem difficulties. By analyzing a typical contraction-based three-stage optimization process, optimal contraction theorem is presented to show that T:Er&Ei depends on the optimization hardness of problems to be optimized. T:Er&Ei will gradually lean toward exploration as optimization hardness increases. In the case of great optimization hardness, exploration-dominated optimizers outperform exploitation-dominated optimizers. In particular, random sampling will become an outstanding optimizer when optimization hardness reaches a certain degree. Besides, the optimal number of contraction stages increases with optimization hardness. In an optimal contraction way, the whole sampling cost is evenly distributed in all contraction stages, and each contraction takes the same contracting ratio. Furthermore, the characterization of optimization hardness is discussed in detail. The experiments with several typical global optimization algorithms used to optimize three groups of test problems validate the correctness of the conclusions made by T:Er&Ei analysis.

An Efficient Rule-Based Constructive Heuristic to Solve Dynamic Weapon-Target Assignment Problem

In this paper, we propose an efficient rule-based heuristic to solve asset-based dynamic weapon-target assignment (DWTA) problems. The main idea of the proposed heuristic is to utilize the domain knowledge of DWTA problems to directly achieve weapon assignment, without large number of function evaluations. We update the saturation states of constraints in the assignment process to guarantee the feasibility of generated solutions. For the purpose of testing the performance of the proposed heuristic, we build a general Monte Carlo simulation-based DWTA framework. For comparison, we also employ a Monte Carlo method (MCM) to make DWTA decisions in different defense scenarios. From simulations with DWTA instances under different scales, the heuristic has obvious advantages over the MCM with regard to solution quality and computation time. The proposed method can solve large-scale DWTA problems (e.g., those including 100 weapons, 100 targets, and four defense stages) within only a few seconds.

Efficient Decision Makings for Dynamic Weapon-Target Assignment by Virtual Permutation and Tabu Search Heuristics

The dynamic weapon-target assignment (DWTA) problem is a typical constrained combinatorial optimization problem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic asset-based DWTA model is established, especially for the warfare scenario of force coordination, to formulate this problem. Four categories of constraints, involving capability constraints, strategy constraints, resource constraints (i.e., ammunition constraints), and engagement feasibility constraints, are taken into account in the DWTA model. The concept of virtual permutation (VP) is proposed to facilitate the generation of feasible decisions. A construction procedure (CP) converts VPs into feasible DWTA decisions. With constraint satisfaction guaranteed by the synergy of VPs and the CP, an elaborate local search (LS) operator, namely move-to-head operator, is constructed to avoid repeatedly generating the same decisions. The operator is integrated into two tabu search (TS) algorithms to solve DWTA problems. Comparative experiments involving a random sampling method, an LS method, a hybrid genetic algorithm, a hybrid ant-colony optimization algorithm, and our TS algorithms show that the proposed TS heuristics for DWTA outperform their competitors in most test cases and they are competent for high-quality real-time DWTA decision makings.

Formation control of multiple Euler-Lagrange systems via null-space-based behavioral control

This paper addresses the formation control problem of multiple Euler-Lagrange systems with model uncertainties in the environment containing obstacles. Utilizing the null-space-based (NSB) behavioral control architecture, the proposed problem can be decomposed into elementary missions (behaviors) with different priorities and implemented by each individual system. A class of novel coordination control algorithms is constructed and utilized to achieve accurate formation task while avoiding obstacles and guaranteeing the model uncertainty rejection objective. By using sliding mode control and Lyapunov theory, the formation performance in closed-loop multi-agent systems is proven achievable if the state-dependent gain of the obstacle avoidance mission is appropriately designed. Finally, simulation examples demonstrate the effectiveness of the algorithms.创新点本文研究了在具有模型不确定的欧拉-拉格朗日群体系统在带有障碍物环境条件下的编队控制问题。利用基于零空间行为控制理论,依据子任务的优先级将总编队任务分解为若干个子任务元素。构建了一套新颖的协同控制算法,用于实现精确的编队任务,并保证了有效的壁障。并且有效处理了模型不确定性对编队实现的影响。本文利用滑模控制和李雅普诺夫稳定性分析理论,证明了多智能体系统及编队任务的稳定性。最后,仿真验证了所提算法的有效性。

Evolutionary decision-makings for the dynamic weapon-target assignment problem

The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general “virtual” representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.

The Comparison and Application of Corner Detection Algorithms

Corners in images represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, two widely used corner detection algorithms, SUSAN and Harris corner detection algorithms which are both based on intensity, were compared in stability, noise immunity and complexity quantificationally via stability factor η, anti-noise factor ρ and the runtime of each algorithm. It concluded that Harris corner detection algorithm was superior to SUSAN corner detection algorithm on the whole. Moreover, SUSAN and Harris detection algorithms were improved by selecting an adaptive gray difference threshold and by changing directional differentials, respectively, and compared using these three criterions. In addition, SUSAN and Harris corner detectors were applied to an image matching experiment. It was verified that the quantitative evaluations of the corner detection algorithms were valid through calculating match efficiency, defined as correct matching corner pairs dividing by matching time, which can reflect the performances of a corner detection algorithm comprehensively. Furthermore, the better corner detector was used into image mosaic experiment, and the result was satisfied. The work of this paper can provide a direction to the improvement and the utilization of these two corner detection algorithms.

Adaptive nonsingular fast terminal sliding mode control for electromechanical actuator

An adaptive nonsingular fast terminal sliding mode control scheme consisting of an adaptive control term and a robust control term for electromechanical actuator is proposed in this article. The adaptive control term with an improved composite adaptive law can estimate the uncertain parameters and compensate for the modelled dynamical uncertainties. While the robust control term, which is based on a modified nonsingular fast terminal sliding mode control method with fast terminal sliding mode (TSM) reaching law, provides fast convergence of errors, and robustifies the design against unmodelled dynamics. Furthermore, the control method eliminates the singular problems in conventional TSM control. On the basis of the finite-time stability theory and the differential inequality principle, it is proved that the resulting closed-loop system is stable and the trajectory tracking error converges to zero in finite time. Finally the effectiveness of the proposed method is illustrated by simulation and experimental study.

The Comparison of Two Typical Corner Detection Algorithms

Corners in images represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, two widely used corner detection algorithms, SUSAN and Harris corner detection algorithms which are both based on intensity, were compared in stability, noise immunity and complexity quantificationally via stability factor eta, anti-noise factor rho and the runtime of each algorithm. It concluded that Harris corner detection algorithm was superior to SUSAN corner detection algorithm on the whole. And the comparison result was applied to an image matching experiment. It was verified that the quantitative evaluations of these two corner detection algorithms were valid through calculating match efficiency, defined as correct matching corner pairs dividing by matching time, which can reflect the performances of a corner detection algorithm comprehensively. The work of this paper can provide a direction to the improvement and the utilization of these two corner detection algorithms.

A Fast Averaging Synchronization Algorithm for Clock Oscillators in Nonlinear Dynamical Network with Arbitrary Time-delays

Abstract This paper investigates the synchronization problem of clock oscillators in nonlinear dynamical network with arbitrary time-delays. First, a dynamic synchronization algorithm based on consensus control strategy, named fast averaging synchronization algorithm (FASA), is presented to find a solution to the synchronization problem. This algorithm can compensate the clock skew and offset differences between clock nodes, achieving the synchronization of clock nodes in a shorter time as compared to previous synchronization methods. Second, because of the dynamical performance of FASA, it is characterized from the perspective of compartmental dynamical system with arbitrary time-delays. In this case, the algorithm guarantees the states of all clock nodes in dynamical network converge to Lyapunov stable equilibria. Finally, numerical simulations and experimental results demonstrate the correctness and efficiency of the FASA, which means that the clock nodes can reach global consensus, and the synchronization error can reach nanosecond order of magnitude.

Flocking with obstacle avoidance and connectivity maintenance in multi-agent systems

The problems of flocking with both connectivity maintenance and obstacle avoidance for the network of dynamic agents are addressed. In the case where the initial network is connected, a set of decentralized flocking control protocols is presented by utilizing artificial potential functions combined with stream functions to enable the group to asymptotically achieve the desired stable flocking motion, which could not only maintain the network connectivity of the dynamic multi-agent systems for all time but also make all the agents avoid obstacles smoothly without trapping into local minima. Finally, nontrivial simulations and experiments are worked out to verify the effectiveness of the theoretical methods.