DE-VNS: Self-adaptive Differential Evolution with crossover neighborhood search for continuous global optimization

Kovačević, Darko; Mladenović, Nenad; Petrović, Bratislav; Milošević, Pavle

doi:10.1016/j.cor.2013.12.009

Cited by 39 publications

(9 citation statements)

References 29 publications

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“…Os valores são codificados e modificados com os mesmos operadores aplicados nos indivíduos (TC-AG:2). Kovačević (2014) [69] apresenta um método de controle para o fator de mutação. É definido um conjunto de valores para o parâmetro e as respectivas probabilidades de cada valor são ajustados de acordo com sua taxa de sucesso (TC-RS:1).…”

Section: Evolução Diferencialunclassified

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

André

Parpinelli

2014

RITA

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Nature has always been a great source of inspiration for the development of computational approaches for optimization. Two major groups representing this class of biologically inspired algorithms are Swarm Intelligence and Evolutionary Computation. Such algorithms are called metaheuristics and are recognized to be efficient approaches for solving complex problems. Both Swarm Intelligence and Evolutionary Computation share common features such as the use of stochastic components during the optimization process and various parameters for configuration. The setup of parameters of an algorithm has an important role in defining its behavior, guiding the search and biasing the quality of the solutions found. However, adjusting the parameters is not a simple task, becoming an optimization problem within the problem being optimized. In addition, an appropriate setting for the parameters may change during the optimization process making this task even harder. There are two ways to adjust the parameters of an algorithm. The offline control that is performed before running the algorithm and parameter values remains fixed and the online control where parameter values may change during the optimization process. This article focuses on reviewing the online parameter control strategies applied in Evolutionary Computation and Swarm Intelligence. As a result, this review analyzes and points out the key techniques and algorithms used and suggests some directions for future research.

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Section: Evolução Diferencialunclassified

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

André

Parpinelli

2014

RITA

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“…Many variants to this basic algorithm can be thought (see, for instance, [10][11][12][13][14]). In this paper we will apply this basic version of the algorithm to IMRT BAO problem.…”

Section: Differential Evolutionmentioning

confidence: 99%

IMRT beam angle optimization using differential evolution

Dias

Ferreira

Rocha

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Radiation therapy is one of the main treatments against cancer. Intensity Modulated Radiation Therapy (IMRT) is one type of radiation therapy that allows a high degree of conformity between the radiation intensities and the areas to treat. The planning of a radiation treatment for a given patient is crucial for obtaining the desired goals (being able to destroy cancer cells while preserving the healthy ones). In clinical practice, treatment planning is most of the times based on a lengthy trial-and-error procedure during which the planner interacts with a treatment planning system trying to find a treatment that complies with the medical prescription. One of the first decisions the planner has to make is on the angles to be used to deliver radiation. In clinical practice, most of the times, the number of angles to be used is defined a priori based on the experience of the planner with similar cases. Often, the solution that is used is the equidistant solution, where all angles are equally apart. In this paper we propose the use of Differential Evolution (DE) for determining in an automated way the set of angles that should be used in a given IMRT treatment. Solutions obtained after the DE optimization are then compared with the equidistant solution. Preliminary results considering ten already treated patients at the Portuguese Institute of Oncology of Coimbra IPOCFG are presented.

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“…On the other hand, DE based heuristics use only a few points during the search, even when the size of the problem is very large. Taking those empirical results into account, the following conclusions can be drawn: (i) we do not need n + 1 points to find good search direction in R n as NM method does, i.e., 4 or 5 points could be enough; (ii) a better direction than that defined in NM probably cannot be found, despite of the fact that updating of mutation and cross-over parameters within DE could be succesful [7]. Thus, we got an idea to reduce the number of points in direct search method from DE based methods, although DE methods have nothing in common with NM and SNM.…”

Section: Introductionmentioning

confidence: 98%

Less is more: Simplified Nelder-Mead method for large unconstrained optimization

Gonçalves-e-Silva

Aloise

Xavier‐de‐Souza

et al. 2018

YUGOSLAV J OPERATION

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Nelder-Mead method (NM) for solving continuous non-linear optimization problem is probably the most cited and the most used method in the optimization literature and in practical applications, too. It belongs to the direct search methods, those which do not use the first and the second order derivatives. The popularity of NM is based on its simplicity. In this paper we propose even more simple algorithm for larger instances that follows NM idea. We call it Simplified NM (SNM): instead of generating all n + 1 simplex points in R n , we perform search using just q + 1 vertices, where q is usually much smaller than n. Though the results cannot be better than after performing calculations in n + 1 points as in NM, significant speed-up allows to run many times SNM from different starting solutions, usually getting better results than those obtained by NM within the same cpu time. Computational analysis is performed on 10 classical con-154 K. Gonçalves-e-Silva, et al. / Less is more: Simplified Nelder-Mead Method vex and non-convex instances, where the number of variables n can be arbitrarily large. The obtained results show that SNM is more effective than the original NM, confirming that LIMA yields good results when solving a continuous optimization problem.

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DE-VNS: Self-adaptive Differential Evolution with crossover neighborhood search for continuous global optimization

Cited by 39 publications

References 29 publications

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

IMRT beam angle optimization using differential evolution

Less is more: Simplified Nelder-Mead method for large unconstrained optimization

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