Applications of river formation dynamics

Rabanal, Pablo; Rodrı́guez, Ismael; Rubio, Fernando

doi:10.1016/j.jocs.2017.08.002

Cited by 28 publications

(6 citation statements)

References 56 publications

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“…That is, we would like to find the approximation class the problem belongs to (see, e.g., [19,20]). Moreover, we are interested in exploring alternative heuristic methods (like River Formation Dynamics [21]) that could be used to find suboptimal solutions.…”

Section: Discussionmentioning

confidence: 99%

On the Hardness of Lying under Egalitarian Social Welfare

2021

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When it comes to distributing resources among different agents, there are different objectives that can be maximized. In the case of egalitarian social welfare, the goal is to maximize the utility of the least satisfied agent. Unfortunately, this goal can lead to strategic behaviors on the part of the agents: if they lie about their utility functions, then the dealer might grant them more goods than they would be entitled to. In this work, we study the computational complexity of obtaining the optimal lie in this context. We show that although it is extremely easy to obtain the optimal lie when we do not impose any restrictions on the lies used, the problem becomes Σ2P-complete by imposing simple limits on the usable lies. Thus, we prove that we can easily make it hard to lie in the context of egalitarian social welfare.

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Section: Discussionmentioning

confidence: 99%

On the Hardness of Lying under Egalitarian Social Welfare

2021

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“…In particular, due to its intrinsic absence of cycles, it has been used to design several routing protocols in computer networks (see, e.g., [24,25]), to test systems [26], or to improve robot navigation [27]. A survey describing the main application domains of RFD can be found in [28].…”

Section: Rfd: River Formation Dynamicsmentioning

confidence: 99%

Water‐Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP‐Hard Problems

Rubio

Rodrı́guez

2019

Complexity

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Many water-based optimization metaheuristics have been introduced during the last decade, both for combinatorial and for continuous optimization. Despite the strong similarities of these methods in terms of their underlying natural metaphors (most of them emulate, in some way or another, how drops collaboratively form paths down to the sea), in general the resulting algorithms are quite different in terms of their searching approach or their solution construction approach. For instance, each entity may represent a solution by itself or, alternatively, entities may construct solutions by modifying the landscape while moving. A researcher or practitioner could assume that the degree of similarity between two water-based metaheuristics heavily depends on the similarity of the natural water mechanics they emulate, but this is not the case. In order to bring some clarity to this mosaic of apparently related metaheuristics, in this paper we introduce them, explain their mechanics, and highlight their differences.

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“…Further, continuous movement of water drops causes wearing away of soil and rocks, which leads to erosion (erodePaths()), i.e. when a drop travels from n x to n y , Alt(n x ) is eroded (reduced) and the new altitude, Alt 0 (n x ) is defined as follows (Rabanal et al, 2017):…”

Section: River Formation Dynamicsmentioning

confidence: 99%

“…depositSediments(). In such cases, the amount of sediment deposition at any node n k depends upon the amount of erosion in the previous step as follows (Rabanal et al, 2017):…”

Section: River Formation Dynamicsmentioning

confidence: 99%

Minimizing area of VLSI power distribution networks using river formation dynamics

Dash

Dey

Joshi

et al. 2018

JSIT

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Purpose The purpose of this paper is to demonstrate the application of river formation dynamics to size the widths of power distribution network for very large-scale integration designs so that the wire area required by power rails is minimized. The area minimization problem is transformed into a single objective optimization problem subject to various design constraints, such as IR drop and electromigration constraints. Design/methodology/approach The minimization process is carried out using river formation dynamics heuristic. The random probabilistic search strategy of river formation dynamics heuristic is used to advance through stringent design requirements to minimize the wire area of an over-designed power distribution network. Findings A number of experiments are performed on several power distribution benchmarks to demonstrate the effectiveness of river formation dynamics heuristic. It is observed that the river formation dynamics heuristic outperforms other standard optimization techniques in most cases, and a power distribution network having 16 million nodes is successfully designed for optimal wire area using river formation dynamics. Originality/value Although many research works are presented in the literature to minimize wire area of power distribution network, these research works convey little idea on optimizing very large-scale power distribution networks (i.e. networks having more than four million nodes) using an automated environment. The originality in this research is the illustration of an automated environment equipped with an efficient optimization technique based on random probabilistic movement of water drops in solving very large-scale power distribution networks without sacrificing accuracy and additional computational cost. Based on the computation of river formation dynamics, the knowledge of minimum area bounded by optimum IR drop value can be of significant advantage in reduction of routable space and in system performance improvement.

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Applications of river formation dynamics

Cited by 28 publications

References 56 publications

On the Hardness of Lying under Egalitarian Social Welfare

On the Hardness of Lying under Egalitarian Social Welfare

Water‐Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP‐Hard Problems

Minimizing area of VLSI power distribution networks using river formation dynamics

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