High Precision Particle Swarm Optimization Algorithm (HiPPSO)

Raß, Alexander

doi:10.5334/jors.282

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…3.2. The tests were performed with Raß' HiPPSO [Raß17], a high precision implementation of PSO, in order to rule out the influence of insufficient computer is relatively small if f (G) is far away from the (unique) optimum value and increases considerably when f (G) approaches the optimum. Additionally, one can see that the gradient of the absolute forcing frequency, i. e., the relative forcing frequency, tends to be constant for all i ≥ i 0 after some value i 0 .…”

Section: Methodsmentioning

confidence: 99%

Self-adaptive Potential-based Stopping Criteria for Particle Swarm Optimization

Bassimir,

Schmitt,

Wanka

2019

Preprint

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We study the variant of Particle Swarm Optimization (PSO) that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better solution candidates as it already has found. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud, and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good solution candidates can be found much faster than applying other criteria.

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Section: Methodsmentioning

confidence: 99%

Self-adaptive Potential-based Stopping Criteria for Particle Swarm Optimization

Bassimir,

Schmitt,

Wanka

2019

Preprint

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“…In our test = 10 −7 turned out to be a sufficient value to prevent such issues. This issue can be avoided by using a high precision PSO platform like HiPPSO [Raß (2020)].…”

Section: Termination Criteriamentioning

confidence: 99%

Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves

2020

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We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better candidate solutions than the already-found best solution. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good candidate solutions can be found much faster than setting upper bounds on the iterations and better solutions compared to applying other solutions from the literature.

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