Optimization Parallelizing for Discrete Programming Problems

Сергиенко, И. В.; Shilo, V. P.; Roshchin, V. A.

doi:10.1023/b:casa.0000034443.12493.40

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Luby et al [16] prove that the optimal restart strategy uses τ 1 = τ 2 = · · · = τ * , where τ * is a constant. Restart strategies in metaheuristics have been addressed in [1,14,18,19,27]. Some recent work on restart strategies can be found in [28,29].…”

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confidence: 99%

Restart strategies for GRASP with path-relinking heuristics

Resende

Ribeiro

2011

Optim Lett

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GRASP with path-relinking is a hybrid metaheuristic, or stochastic local search (Monte Carlo) method, for combinatorial optimization. A restart strategy in GRASP with path-relinking heuristics is a set of iterations {i 1 , i 2 , . . .} on which the heuristic is restarted from scratch using a new seed for the random number generator. Restart strategies have been shown to speed up stochastic local search algorithms. In this paper, we propose a new restart strategy for GRASP with pathrelinking heuristics. We illustrate the speedup obtained with our restart strategy on GRASP with path-relinking heuristics for the maximum cut problem, the maximum weighted satisfiability problem, and the private virtual circuit routing problem.

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confidence: 99%

Restart strategies for GRASP with path-relinking heuristics

Resende

Ribeiro

2011

Optim Lett

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“…A promising approach was proposed in [25] to parallelize the optimization process, where instead of the operations fulfilled by algorithm, its copies are parallelized. Let a stochastic algorithm be available to solve a discrete optimization problem, and its random behavior be defined by a randomizer.…”

Section: Tablementioning

confidence: 99%

“…According to the approach proposed in [25], copies of the initial algorithm are created and used to solve the discrete optimization problem involving all the processors available. In some cases, such an approach solves the problem faster than operation parallelizing does.…”

Section: Tablementioning

confidence: 99%

Problems of discrete optimization: Challenges and main approaches to solve them

Сергиенко

Shylo

2006

Cybern Syst Anal

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“…In general, restarting an algorithm as a way of improving its performance is widely used in the algorithmic design of metaheuristics; see, e.g., D'Apuzzo et al (2006), Nowicki and Smutnicki (2005), Palubeckis (2004), and Resende and Ribeiro (2011). For example, the study of the running time distributions of the tabu search algorithm and the effective restart strategies for the max-cut problem is reported in Sergienko et al (2004).…”

Section: Introductionmentioning

confidence: 99%

On Maximum Speedup Ratio of Restart Algorithm Portfolios

Mostovyi

Prokopyev

Shylo

2013

INFORMS Journal on Computing

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W e discuss two possible parallel strategies for randomized restart algorithms. Given a set of available algorithms, one can either choose the best performing algorithm and run its multiple copies in parallel (single algorithm portfolio) or choose some subset of algorithms to run in parallel (mixed algorithm portfolio). It has been previously shown that the latter approach may provide better results computationally. In this paper, we provide theoretical investigation of the extent of such improvement generalizing some of the known results from the literature. In particular, we estimate the computational value of mixing randomized restart algorithms with different properties. Under some mild assumptions, we prove that in the best case the mixed algorithm portfolio may perform approximately up to 1.58 times faster than the best single algorithm portfolio. We also show that the obtained upper bound is sharp. Furthermore, the constructive proof of the main result allows us to characterize algorithms that are likely to form an effective mixed algorithm portfolio.

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Optimization Parallelizing for Discrete Programming Problems

Cited by 10 publications

References 6 publications

Restart strategies for GRASP with path-relinking heuristics

Restart strategies for GRASP with path-relinking heuristics

Problems of discrete optimization: Challenges and main approaches to solve them

On Maximum Speedup Ratio of Restart Algorithm Portfolios

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