Simulated Annealing and Combinatorial Optimization

Nahar, Surendra; Sahni, Sartaj; Shragowitz, Eugene

doi:10.1109/dac.1986.1586103

Cited by 52 publications

(22 citation statements)

References 13 publications

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“…9 A simple, yet efficient heuristic is iterative improvement (II). [Nahar et al 1986]Starting from a random initial state (called seed), it performs a random series of moves and accepts all downhill ones, until a local minimum is detected. This process is repeated until a time limit is reached, each time with a different seed.…”

Section: Randomized Search Algorithmsmentioning

confidence: 99%

Multiway spatial joins

Mamoulis

Papadias

2001

ACM Trans. Database Syst.

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Due to the evolution of Geographical Information Systems, large collections of spatial data having various thematic contents are currently available. As a result, the interest of users is not limited to simple spatial selections and joins, but complex query types that implicate numerous spatial inputs become more common. Although several algorithms have been proposed for computing the result of pairwise spatial joins, limited work exists on processing and optimization of multiway spatial joins. In this paper we review pairwise spatial join algorithms and show how they can be combined for multiple inputs. In addition, we explore the application of synchronous traversal (ST), a methodology that processes synchronously all inputs without producing intermediate results. Then, we integrate the two approaches in an engine that includes ST and pairwise algorithms, using dynamic programming to determine the optimal execution plan. The results show that in most cases multiway spatial joins are best processed by combining ST with pairwise methods. Finally, we study the optimization of very large queries by employing randomized search algorithms.

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Section: Randomized Search Algorithmsmentioning

confidence: 99%

Multiway spatial joins

Mamoulis

Papadias

2001

ACM Trans. Database Syst.

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“…We can do this either by doing a large number of iterations at a few temperatures or a small number of iterations at many temperature, or a balance between the two. Moreover, according to [12] the value of a should be between 0.8 and 0.99. The higher the value of a, the longer it will take to decrement the temperature to the stopping criterion, which means that SA performs exhaustive search.…”

Section: Update Sa Parameters: T = A*t Update Temperature Iteratiomentioning

confidence: 99%

“…The choice of the initial temperature value To, temperature decrement function and decrement factor a, algorithm's stop ping criterion STOPc and number of iterations iterations per temperature, strongly influence the quality of the final solution [12]. Theory states that we should allow enough iterations at each temperature so that the system stabilizes at that temperature [11].…”

Section: Update Sa Parameters: T = A*t Update Temperature Iteratiomentioning

confidence: 99%

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Energy-aware joint power, packet and topology optimization by simulated annealing for WSNs

Xenakis

Foukalas

Stamoulis

et al. 2013

2013 7th IEEE GCC Conference and Exhibition (GCC)

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Energy-aware connectivity in wireless sensor net works (WSNs) can increase their network lifetime. This can be achieved by optimizing the power control, a particular communication mechanism and the topology control. To this end, in this paper ', an energy-aware joint power, packet and topology optimization for WSNs is provided. More specifically, we assume power control based on feedback information, packet trans mission through automatic-repeat-request(ARQ) mechanism and topology control assuming specific coverage constraints. This par ticular joint optimization problem is considered as NP-complete and heuristic methods are usually applied in such complicated cases. In particular, we employ Simulated Annealing (SA) for a step-by-step evaluation of the average energy consumption of sensor nodes, which is considered the objective function of our optimization problem. In each step of the SA algorithm, the resultant topology is evaluated and at the convergence point the near optimal transmit power and packet as well as node placements are obtained jointly.

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“…The optimal block flipping problem has been proven to be NP-complete [6]. However, many heuristics [7][8][9][10][11][12][13] were proposed to obtain sub-optimal solutions. A symbolic algorithm [14] based on Boolean decision diagram (BDD) was proposed that can solve the problem optimally to obtain the minimum wirelength for small-sized circuits, e.g.…”

Section: Introductionmentioning

confidence: 99%