Approach for Optimal Nesting Using Genetic Algorithm and Local Minimization Algorithm.

Amindavar

EURASIP J. Adv. Signal Process.

2011

We consider the localization problem of multiple wideband sources in a multi-path environment by coherently taking into account the attenuation characteristics and the time delays in the reception of the signal. Our proposed method leaves the space for unavailability of an accurate signal attenuation model in the environment by considering the model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared with only utilizing the signal attenuation information or the time delays. In this article, the localization problem is modeled as a cost function in terms of the source locations, attenuation model parameters, and the multi-path parameters. To globally perform the minimization, we propose a hybrid algorithm combining the differential evolution algorithm with the Levenberg-Marquardt algorithm. Besides the proposed combination of optimization schemes, supporting the technical details such as closed forms of cost function sensitivity matrices are provided. Finally, the validity of the proposed method is examined in several localization scenarios, taking into account the noise in the environment, the multi-path phenomenon and considering the sensors being not synchronized.

Section: Minimization Strategymentioning

confidence: 99%

A hybrid global minimization scheme for accurate source localization in sensor networks

Amindavar

EURASIP J. Adv. Signal Process.

2011

“…Hybrid algorithms are motivated by the idea that a superior search algorithm may be obtained by taking advantage of the strength of two or more different algorithms. In case of Evolutionary Algorithms, some of the early hybrid algorithms combined an evolutionary search with a local newtonian-based search [108,109]. Every time the Evolutionary Algorithm generated a new offspring, a local newtonian search would be performed to locate the closest local optimum.…”

Section: Hybrid and Alternative Evolutionary Algorithmsmentioning

confidence: 99%

On Natural Based Optimization

Nobakhti

2010

Cogn Comput

Nature has always been a source of great inspiration for engineers and mathematicians. Evolutionary Algorithms are the latest in a line of natural-based innovations which have had a profound effect on the application of optimization in science and engineering. Although based on nature, Evolutionary Algorithms are nonetheless distinctly different from natural evolution in several areas. This paper outlines early and recent developments of Evolutionary Algorithms while covering those areas of difference. Practical issues related to the use of Evolutionary Algorithms, key parameters that affect the quality of the search and impact of user choices in problem formulation are also covered in this paper.

Advances in Soft Computing

“…Their characteristics can be seen in Table 2. We also added a problem from the literature [6], which was scaled by a factor of 10 in order to have the sheet size of 300 × 300. Instances of type G are the only problems with unknown optimal number of objects, since they were produced after alterations to randomly generated problems with known optimum.…”

Section: Problem Instancesmentioning

confidence: 99%

Approximating Multi-Objective Hyper-Heuristics for Solving 2D Irregular Cutting Stock Problems

Gómez

Terashima-Marín

2010

Abstract. This article presents a method based on the multi-objective evolutionary algorithm NSGA-II to approximate hyper-heuristics for solving irregular 2D cutting stock problems under multiple objectives. In this case, additionally to the traditional objective of minimizing the number of sheets used to fit a finite number of irregular pieces, the time required to perform the placement task is also minimized, leading to a bi-objective minimization problem with a tradeoff between the number of sheets and the time required for placing all pieces. We solve this problem using multi-objective hyper-heuristics (MOHHs), whose main idea consists of finding a set of simple heuristics which can be combined to find a general solution for a wide range of problems, where a single heuristic is applied depending on the current condition of the problem, instead of applying a unique single heuristic during the whole placement process. The MOHHs are approximated after going through a learning process by mean of the NSGA-II, which evolves combinations of condition-action rules producing at the end a set of Pareto-optimal MOHHs. We tested the approximated MMOHHs on several sets of benchmark problems, having outstanding results for most of the cases.