A Simulated Annealing White Balance Algorithm for Foreign Fiber Detection

Zhou, Zhichao; Yu-hong, DU; Du, Yuqin; Yun, Jintian; Liu, Renjie

doi:10.1145/3278198.3278214

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Applications Of Simulated Annealing1

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2021

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Cited by 2 publications

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“…For example, Dinh et al (2019) line balancing problem. Zhichao et al (2018) use SA to detect foreign fibers in images of cotton to enable removal of those foreign fibers. Liang et al (2018) optimize the placement of bus stops in an urban bus transportation system using SA.…”

Section: Applications Of Simulated Annealingmentioning

confidence: 99%

Optimizing the Modified Lam Annealing Schedule

Cicirello¹

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Section: Applications Of Simulated Annealingmentioning

confidence: 99%

Optimizing the Modified Lam Annealing Schedule

Cicirello¹

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving

Cicirello

2021

Applied Sciences

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The runtime behavior of Simulated Annealing (SA), similar to other metaheuristics, is controlled by hyperparameters. For SA, hyperparameters affect how “temperature” varies over time, and “temperature” in turn affects SA’s decisions on whether or not to transition to neighboring states. It is typically necessary to tune the hyperparameters ahead of time. However, there are adaptive annealing schedules that use search feedback to evolve the “temperature” during the search. A classic and generally effective adaptive annealing schedule is the Modified Lam. Although effective, the Modified Lam can be sensitive to the scale of the cost function, and is sometimes slow to converge to its target behavior. In this paper, we present a novel variation of the Modified Lam that we call Self-Tuning Lam, which uses early search feedback to auto-adjust its self-adaptive behavior. Using a variety of discrete and continuous optimization problems, we demonstrate the ability of the Self-Tuning Lam to nearly instantaneously converge to its target behavior independent of the scale of the cost function, as well as its run length. Our implementation is integrated into Chips-n-Salsa, an open-source Java library for parallel and self-adaptive local search.

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A Simulated Annealing White Balance Algorithm for Foreign Fiber Detection

Cited by 2 publications

References 4 publications

Optimizing the Modified Lam Annealing Schedule

Optimizing the Modified Lam Annealing Schedule

Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving

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