Particle Swarm Optimization

Venter, Gerhard; Sobieszczanski‐Sobieski, Jaroslaw

doi:10.2514/2.2111

Cited by 446 publications

(174 citation statements)

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“…In Venter and Sobieszczanski-Sobieski (2003) it is suggested to use 0.35 < w < 1.4, and g w = 0.975. The constriction factor χ is used alternatively to w to limit the maximum velocity.…”

Section: Description Of Algorithm Psomentioning

confidence: 98%

“…A suitable value for w usually provides balance between global and local exploration abilities and consequently results in a reduction of the number of iterations required to locate the optimal solution. Experimental results indicate that it is better to initially set the inertia to a large value, in order to promote global exploration of the design variables space and gradually decrease it to get a more refined solution (Venter and Sobieszczanski-Sobieski 2003). For these reasons, an initial value for w is set and the decrease rate is calculated by…”

Section: Description Of Algorithm Psomentioning

confidence: 99%

“…(iv) Particles with violated constraints: The original PSO algorithm is defined only for unconstrained optimization problems. For this reason, when we are dealing with constrained problems we set equal to 0 the inertia weight term of particles with violated constraints (Venter and Sobieszczanski-Sobieski 2003;Pinto et al 2004). As a consequence, (14) is substituted by…”

Section: A New Pso Algorithm: Ddfpsomentioning

confidence: 99%

“…In basic PSO algorithm (Kennedy and Eberhart 1995) the authors propose c 1 = c 2 = 2, so that the mean of stochastic multipliers of (14) is 1. In Venter and Sobieszczanski-Sobieski (2003) different values for the two coefficients are used. In particular, c 1 = 1.5 and c 2 = 2.5 work well in their examples.…”

Section: Description Of Algorithm Psomentioning

confidence: 99%

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New global optimization methods for ship design problems

et al. 2009

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The aim of this paper is to solve optimal design problems for industrial\ud applications when the objective function value requires the evaluation of expensive\ud simulation codes and its first derivatives are not available. In order to achieve this goal\ud we propose two new algorithms that draw inspiration from two existing approaches:\ud a filled function based algorithm and a Particle Swarm Optimization method. In order\ud to test the efficiency of the two proposed algorithms, we perform a numerical compar-\ud ison both with the methods we drew inspiration from, and with some standard Global\ud Optimization algorithms that are currently adopted in industrial design optimization.\ud Finally, a realistic ship design problem, namely the reduction of the amplitude of the\ud heave motion of a ship advancing in head seas (a problem connected to both safety\ud and comfort), is solved using the new codes and other global and local derivative-free optimization methods. All the numerical results show the effectiveness of the\ud two new algorithms

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“…In Venter and Sobieszczanski-Sobieski (2003) it is suggested to use 0.35 < w < 1.4, and g w = 0.975. The constriction factor χ is used alternatively to w to limit the maximum velocity.…”

Section: Description Of Algorithm Psomentioning

confidence: 98%

Section: Description Of Algorithm Psomentioning

confidence: 99%

Section: A New Pso Algorithm: Ddfpsomentioning

confidence: 99%

Section: Description Of Algorithm Psomentioning

confidence: 99%

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New global optimization methods for ship design problems

et al. 2009

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“…They are often called acceleration coefficients [26]. Venter and Sobieski termed c 1 as self-confidence and c 2 as swarmconfidence [27]. From the different empirical researches, it has been proposed that the two acceleration constants should be c 1 = c 2 .…”

Section: Pso Basics and Optimization Mechanism Analysismentioning

confidence: 98%

Modified particle swarm optimization for BMDS interceptor resource planning

Fu-xian

Long

et al. 2015

Appl Intell

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One important mission of the strategic defense is to develop an integrated, layered Ballistic Missile Defense System (BMDS). We consider the problem of assigning interceptors to multiple waves of incoming ballistic missiles. This work addresses the issue of shot time constraints and directly allocates multiple targets to interceptors in the single engagement problem. A mathematical model and method for BMDS Interceptor resource planning (IRP) is presented. In addition to mathematical model, a modified particle swarm optimization RPPSO algorithm has been developed for solving complex IRP problem. This algorithm adapted a basic PSO algorithm to make it compatible with reasonable engineering problems. RPPSO was developed by embedding a reverse predictor within the basic algorithm to avoid premature convergence without significantly reducing convergence speed, and adding a repulsive force to keep the diversity of both local and global optima.

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