Iterated Local Search Algorithm for the Linear Ordering Problem with Cumulative Costs (LOPCC)

2010 ◽  
pp. 395-404 ◽  
Author(s):  
Jesús David Terán Villanueva ◽  
Héctor Joaquín Fraire Huacuja ◽  
Rodolfo Pazos Rangel ◽  
Juan Martín Carpio Valadez ◽  
Héctor José Puga Soberanes ◽  
...  
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Linear Ordering ◽  
Local Search Algorithm ◽  
Linear Ordering Problem

scholarly journals DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1429
Author(s):  
Jui-Chung Hung
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Doa Estimation ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Noise Variance ◽  
Swarm Optimization ◽  
Noise Effects ◽  
Low Snr ◽  
Uniform Noise

In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments.

A Parallel Iterated Local Search for Two-Agent Flow Shop Scheduling

2014 ◽  
Vol 926-930 ◽  
pp. 3476-3484 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Flow Shop Scheduling ◽  
Local Search Algorithm ◽  
Shop Scheduling ◽  
Transition Mechanism ◽  
Neighborhood Structures ◽  
Agent Flow

In this paper a two-agent flow shop scheduling problem is studied and a simple parallel iterated local search algorithm is proposed to minimize the makespan of jobs from the first agent and the total tardiness of jobs from the second agent simultaneously. Parallelization is implemented by applying multiple independent searches, each of which uses three neighborhood structures with dynamical transition mechanism. The current solution of each independent search is replaced with a solution, which is randomly chosen from the non-dominated set and perturbed. The computational experiments show the promising advantage of the proposed method when compared to other algorithms of the problem.

An Iterated Local Search Algorithm for Task Assignment in Distributed Computing Systems

2012 ◽  
Vol 588-589 ◽  
pp. 1308-1311
Author(s):  
Qin Ma Kang ◽  
Hong He ◽  
Hai Ning Jiang
Keyword(s):  
Distributed Computing ◽  
Local Search ◽  
Search Algorithm ◽  
Task Assignment ◽  
Computation Time ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Wide Range

This paper considers the problem of task assignment in heterogeneous distributed computing systems with the goal of minimizing the total execution and communication costs. An iterated local search algorithm is proposed for finding the suboptimal task assignment in a reasonable amount of computation time. We study the performance of the proposed algorithm over a wide range of parameters such as the problem scales, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness of the algorithm is manifested by comparing it with other competing algorithms in the relevant literature.

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