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In this paper we present a gradient method to iteratively update local controllers of a distributed linear system driven by stochastic disturbances. The control objective is to minimize the sum of the variances of states and inputs in all nodes. We show that the gradients of this objective can be estimated distributively using data from a forward simulation of the system model and a backward simulation...
We consider a class of multiuser optimization problems in which user interactions are seen through congestion cost functions or coupling constraints. Our primary emphasis lies on the convergence and error analysis of distributed algorithms in which users communicate through aggregate user information. Traditional implementations are reliant on strong convexity assumptions, require coordination across...
For a right-invariant and controllable driftless system on SU(n), we consider a time-periodic reference trajectory along which the linearized control system generates su(n): such trajectories always exist and constitute the basic ingredient of Coron's return method. The open-loop controls that we propose, which rely on a left-invariant tracking error dynamics and on a fidelity-like Lyapunov function,...
Local convergence is a limitation of many optimization approaches for multimodal functions. For hybrid model learning, this can mean a compromise in accuracy. We develop an approach for learning the model parameters of hybrid discrete-continuous systems that avoids getting stuck in locally optimal solutions. We present an algorithm that implements this approach that 1) iteratively learns the locations...
We consider a distributed multi-agent network system where the goal is to minimize an objective function that can be written as the sum of component functions, each of which is known partially (with stochastic errors) to a specific network agent. We propose an asynchronous algorithm that is motivated by random gossip schemes where each agent has a local Poisson clock. At each tick of its local clock,...
In this paper we consider the problem of constructing a distributed feedback law to achieve synchronization for a group of k agents whose states evolve on SO(n) and which exchange only partial state information along communication links. The partial state information is given by the action of the state on reference vectors in ??n. We propose a gradient based control law which achieves exponential...
This paper proposes a general class of distributed potential-based control laws with the connectivity preserving property for single integrator agents. Given a connected information flow graph, the potential functions are designed in such a way that when an edge in the graph is about to lose connectivity, the gradient of the potential function lies in the direction of the corresponding edge aiming...
In this article we extend previous results for back-stepping and passivity-based design of cooperative control laws to a class of chained form systems that includes certain drift-free nonholonomic systems. We exploit the cascaded structure and feedback equivalence to passive systems to derive suitable control laws using a modified backstepping methodology. A virtual output is obtained and shared within...
Since Witsenhausen put forward his remarkable counterexample in 1968, there have been many attempts to develop efficient methods for solving this non-convex functional optimization problem. However there are few methods designed from game theoretic perspectives. In this paper, after discretizing the Witsenhausen counterexample and re-writing the formulation in analytical expressions, we use fading...
The so-called PageRank algorithm has been used at Google for properly ranking search results. It quantifies the importance of each page by the structure of links in the web. In our recent work, we have proposed a distributed randomized approach for the PageRank computation, where the pages find their own values by communicating with linked pages. This paper builds upon this approach to improve the...
By theoretical analysis and iterative trialing, based on routine isoparametric coordinate transformation method, through a compatible revision to the incompatible element boundary displacement which doesn't affect the completeness of the incompatible element, a new compatible parallelogram thin plate element is developed in the paper. At the same time, compatibility and completeness of the element...
In the present study a Modified Differential Evolution (MDE) algorithm is proposed. This algorithm is different in three ways from basic DE. For initialization it utilizes opposition-based learning while in basic DE uniform random numbers serve this task. Secondly, in basic DE mutant individual is random while in MDE it is tournament best and finally MDE utilizes only one set of population as against...
Applying triangulation theory of the Van der laan-Talman algorithm, an improved genetic algorithm is proposed to solve optimal problems in this paper. The algorithm operates on a simplicial subdivision of searching space and generates the integer labels at the vertices, and then crossover operators and increasing dimension operators relying on the integer labels are designed. In this case, whether...
As an effective tool for optimization, differential evolution (DE) has aroused much interest. But the premature convergence of it often gives rise to erroneous results so should be improved. In this paper, a novel differential evolutionary algorithm (DECH) based on chaos local search (CLS) is proposed, which divides DE algorithm into two stages. Firstly, DECH runs with original DE model 'DE/best/1/bin'...
A fast deterministic packet marking scheme (FDPM) for IP traceback against distributed denial of service attacks is presented, which applies a novel marking algorithm and significantly improves IP traceback in two aspects: (1) the victim doesn't need to accommodate fragments for recovery, so it needs several packets to identify an ingress router with lower false positives; (2) FDPM can scales to large...
Under the linear loss, we consider the test problem of the life parameter in the exponential distribution using empirical Bayes (EB) approach and present a monotone EB test possessing a rate of convergence which can be arbitrarily close to O(n-1) under the condition that the past samples are S\phiS-mixing.
Hybridization is a useful method to enhance the performance of particle swarm optimizer (PSO). In this paper, a novel particle swarm optimizer (NHPSO) combining PSO with a constriction factor (CF-PSO) and the fully informed particle swarm optimizer (FIPSO) in cycles is proposed, in order to balance the convergence speed and search accuracy. Six most commonly used benchmarks are used to evaluate the...
This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond with increasing dimension. To this end, we report the algorithms' performance on the 50, 100, 200 and...
A method for optimization of continuous nonlinear functions is introduced. Seed Throwing Optimization is a probabilistic metaheuristic. It has roots in hill climbing and the evolutionary computation like technique harmony search. The relationship to these algorithms is shown in this paper. Our method is tested in a benchmark and compared to other metaheuristics. Seed Throwing Optimization is a randomized...
In genetic algorithms (GAs) technique, offspring chromosomes are created by merging two parent chromosomes using a crossover operator. A new real coded crossover operator, called the fitness-based recombination operator (FBRO), is proposed. This recombination operator, making use of difference between two parents' fitness, regarding themselves as centers respectively, divides the space containing...
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