Particle Swarms in Optimization and Control

Ast, Jelmer Marinus van; Babuška, Robert; Schutter, Bart De

doi:10.3182/20080706-5-kr-1001.00862

Cited by 19 publications

(6 citation statements)

References 39 publications

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“…the so-called Fast-Lipschitz strategies [23], [24], exploit particular structures of the objective functions and constraints to increase the convergence speed at the cost of being suitable only for particular optimization problems. Finally, alternative distributed optimization approaches can be based on heuristics, like swarm optimization [25], or genetic algorithms [26], however their convergence and performance properties are difficult to be studied analytically.…”

Section: A Previous Workmentioning

confidence: 99%

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Newton-Raphson consensus for distributed convex optimization

Zanella

Varagnolo

Cenedese

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-We study the problem of unconstrained distributed optimization in the context of multi-agents systems subject to limited communication connectivity. In particular we focus on the minimization of a sum of convex cost functions, where each component of the global function is available only to a specific agent and can thus be seen as a private local cost. The agents need to cooperate to compute the minimizer of the sum of all costs. We propose a consensus-like strategy to estimate a Newton-Raphson descending update for the local estimates of the global minimizer at each agent. In particular, the algorithm is based on the separation of time-scales principle and it is proved to converge to the global minimizer if a specific parameter that tunes the rate of convergence is chosen sufficiently small. We also provide numerical simulations and compare them with alternative distributed optimization strategies like the Alternating Direction Method of Multipliers and the Distributed Subgradient Method.

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Section: A Previous Workmentioning

confidence: 99%

“…Applying the changes of variables induced by the isolated root of (25), namely v(t) := v(t) − g (x) , w(t) := w(t) − h (x)…”

Section: Appendixmentioning

confidence: 99%

Newton-Raphson consensus for distributed convex optimization

Zanella

Varagnolo

Cenedese

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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“…The obstacle avoidance can ensure the robots to have abilities of automatically computing a collision-free and shortest path to the target in the working environment, which has achieved remarkable results, such as the A* algorithm, 4 grid method, 5 visibility graph method, 6 ant colony algorithm, 7 particle swarm optimization algorithm 8 and the rapidly expanding random tree search algorithm. 9,10 Artificial potential field (APF) is one of the classical approaches that are used for obstacle avoidance, which is first proposed by Khatib.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm

Pan¹,

Li²,

Yang³

et al. 2019

Robotica

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SummaryAs for the obstacle avoidance and formation control for the multi-robot systems, this paper presents an obstacle-avoidance method based on the improved artificial potential field (IAPF) and PID adaptive tracking control algorithm. In order to analyze the dynamics and kinematics of the robot, the mathematical model of the robot is built. Then we construct the motion situational awareness map (MSAM), which can map the environment information around the robot on the MSAM. Based on the MSAM, the IAPF functions are established. We employ the rotating potential field to solve the local minima and oscillations. As for collisions between robots, we build the repulsive potential function and priority model among the robots. Afterwards, the PID adaptive tracking algorithm is utilized to multi-robot formation control. To demonstrate the validity of the proposed method, a series of simulation results confirm that the approaches proposed in this paper can successfully address the obstacle- and collision-avoidance problem while reaching formation.

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“…Heuristic or ad-hoc methods may use several different techniques, e.g., swarm optimization (Van Ast et al, 2008) or genetic algorithms. Other approaches are instead tailored for suitable classes of cost functions, e.g., the Fast-Lipschitz methods (Fischione, 2011;Fischione and Jönsson, 2011), and may have convergences faster than the ones of ADMMs or DSMs.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Newton-Raphson Consensus for Distributed Convex Optimization

Zanella

Varagnolo

Cenedese

et al. 2012

IFAC Proceedings Volumes

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We consider the distributed unconstrained minimization of separable convex cost functions, where the global cost is given by the sum of several local and private costs, each associated to a specific agent of a given communication network. We specifically address an asynchronous distributed optimization technique called Newton-Raphson Consensus. Beside having low computational complexity, low communication requirements and being interpretable as a distributed Newton-Raphson algorithm, the technique has also the beneficial properties of requiring very little coordination and naturally supporting time-varying topologies. In this work we analytically prove that under some assumptions it shows either local or global convergence properties, and corroborate this result by the means of numerical simulations.

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Particle Swarms in Optimization and Control

Cited by 19 publications

References 39 publications

Newton-Raphson consensus for distributed convex optimization

Newton-Raphson consensus for distributed convex optimization

Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm

Asynchronous Newton-Raphson Consensus for Distributed Convex Optimization

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