A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization

Souza, Daniel Leal; Teixeira, Otávio Noura; Monteiro, Dionne Cavalcante; Oliveira, Roberto Célio Limão de

doi:10.1007/978-3-642-36651-2_6

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…The results obtained in competition with classical versions of PSO were indeed promising and this was demonstrated by several authors and in several publications. Early reports as well as more recent works [2]- [16] confirmed the quality and reliability of the algorithm as well as its good performance in a diversity of domains. The EPSO algorithm then received further improvement and the latest version is available from [17], where examples and a source code are made public.…”

Section: Introductionsupporting

confidence: 60%

Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid

Miranda

Alves

2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

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This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.

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Section: Introductionsupporting

confidence: 60%

Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid

Miranda

Alves

2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

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“…The research of Tan and Ding [19] proposes that when an SI algorithm is parallelized through the GPU, the implementation must fall in one of the following four categories: (1) naïve parallel model, (2) multiphase parallel model, (3) all-GPU parallel model, or (4) multiswarm parallel model. They identified those models by observing several parallel implementations of SI algorithms [20][21][22][23][24], most of which are based on the Particle Swarm Optimization heuristic. We selected HSA for this study as it differs from other SI algorithms on the following basis: (1) it can be used for real-valued optimization problems, while other heuristics are limited to discrete problems, (2) it is not explicitly designed for route searching problems, and (3) according to our knowledge, the parallelization of HSA has not been proposed or developed for object tracking.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of the Honeybee Search Algorithm for Object Tracking

Perez-Cham

Puente

Soubervielle‐Montalvo

et al. 2020

Applied Sciences

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Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.

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“…This version is denoted DEEPSO [9], for Differential EPSO, as some flavor from DE (Differential Evolution) was added to the canonical EPSO. This choice of metaheuristic is well justified: EPSO had successively been shown to outperform other meta-heuristics in a number of power system and other area problems [10]- [13], and a DEEPSO version was the winner in 2014 of the competition on the Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems, set up by the Working Group on Modern Heuristic Optimization of the IEEE PES [14].…”

Section: Introductionmentioning

confidence: 99%

Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem

Carvalho

Loureiro

Sumaili

et al. 2015

2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)

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The optimal solution provided by metaheuristics can be viewed as a random variable, whose behavior depends on the value of the algorithm's strategic parameters and on the type of penalty function used to enforce the problem's soft constraints. This paper reports the use of parametric and non-parametric statistics to compare three different penalty functions implemented to solve the Security Constrained Optimal Power Flow (SCOPF) problem using the new enhanced metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO). To obtain the best performance for the three types of penalty functions, the strategic parameters of DEEPSO are optimized by using an iterative algorithm based on the two-way analysis of variance (ANOVA). The results show that the modeling of soft constraints significantly influences the best achievable performance of the optimization algorithm.

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A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization

Cited by 14 publications

References 13 publications

Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid

Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid

Parallelization of the Honeybee Search Algorithm for Object Tracking

Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem

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