Simulated annealing: a proof of convergence

Granville, V.; Křivánek, Mirko; Rasson, Jean‐Paul

doi:10.1109/34.295910

Cited by 314 publications

(165 citation statements)

References 10 publications

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“…To use an analogy, exploring design possibilities is like simulated annealing [Granville 1994]. Creative work often benefits from broadly exploring a design space with high entropy before optimizing in one direction.…”

Section: Did Parallel Feedback Impact How Participants Learned?mentioning

confidence: 99%

Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-efficacy

Dow¹,

Glassco²,

Kass³

et al. 2011

Design Thinking Research

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Iteration can help people improve ideas. It can also give rise to fixation, continuously refining one option without considering others. Does creating and receiving feedback on multiple prototypes in parallel, as opposed to serially, affect learning, self-efficacy, and design exploration? An experiment manipulated whether independent novice designers created graphic Web advertisements in parallel or in series. Serial participants received descriptive critique directly after each prototype. Parallel participants created multiple prototypes before receiving feedback. As measured by clickthrough data and expert ratings, ads created in the Parallel condition significantly outperformed those from the Serial condition. Moreover, independent raters found Parallel prototypes to be more diverse. Parallel participants also reported a larger increase in task-specific self-confidence. This article outlines a theoretical foundation for why parallel prototyping produces better design results and discusses the implications for design education.

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Section: Did Parallel Feedback Impact How Participants Learned?mentioning

confidence: 99%

Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-efficacy

Dow¹,

Glassco²,

Kass³

et al. 2011

Design Thinking Research

View full text Add to dashboard Cite

show abstract

“…In many optimization problems, the condition of differentiability, convexity and continuity is required, while SA technique does not need it, which is its main advantage. Many researchers used SA in diverse field of engineering, like the transmission network expansion planning problem, 3D face recognition and unit commitment problems [20][21][22].…”

Section: Proposed Methodologiesmentioning

confidence: 99%

Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

Zaman¹,

Qureshi²,

Naveed³

et al. 2012

PIER B

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“…This technique can be readily applied to our multi-agent setting. There are some analytical guarantees of convergence to a global minimum, however they generally require strict conditions on the cost function [11]. Another possibility is to use branch and bound techniques, in which a lower and upper bound on the minimum are used to rule out sections of the state space.…”

Section: Implications and Future Directionsmentioning

confidence: 99%

Unifying Geometric, Probabilistic, and Potential Field Approaches to Multi-robot Coverage Control

Schwager

Slotine

Rus

2011

Springer Tracts in Advanced Robotics

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This paper unifies and extends several different existing strategies for deploying groups of robots in an environment. A cost function is proposed that can be specialized to represent widely different multi-robot deployment tasks. It is shown that geometric and probabilistic deployment strategies that were previously seen as distinct are in fact related through this cost function, and differ only in the value of a single parameter. These strategies are also related to potential field based controllers through the same cost function, though the relationship is not as simple. Distributed controllers are then obtained from the gradient of the cost function and are proved to converge to a local minimum of the cost function. Three special cases are derived as examples: a Voronoi based coverage control task, a probabilistic minimum variance task, and a task using artificial potential fields. The performance of the three different controllers are compared in simulation. A result is also proved linking multi-robot deployment to nonconvex optimization problems, and multi-robot consensus (i.e. all robots moving to the same point) to convex optimization problems, which implies that multi-robot deployment is inherently more difficult than multi-robot consensus.

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Simulated annealing: a proof of convergence

Cited by 314 publications

References 10 publications

Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-efficacy

Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-efficacy

Amplitude and Directional of Arrival Estimation: Comparison Between Different Techniques

Unifying Geometric, Probabilistic, and Potential Field Approaches to Multi-robot Coverage Control

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