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QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2022年第2期82卷 525-559页

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

Particle Swarm Optimization (PSO) is a population-based metaheuristic belonging to the class of Swarm Intelligence (SI) algorithms. Nowadays, its effectiveness on many hard problems is no longer to be proven. Nevertheless, it is known to be strongly sensitive on the choice of its settings and weak for local search. In this paper, we propose a new algorithm, called QUAntum Particle Swarm Optimization (QUAPSO) based on quantum superposition to set the velocity PSO parameters, simplifying the settings of the algorithm. Another improvement, inspired by Kangaroo algorithm (KA), was added to PSO in order to optimize its efficiency in local search. QUAPSO was compared with a set of six well-known algorithms from the literature (two parameter sets of classical PSO, KA, Differential Evolution, Simulated Annealing Particle Swarm Optimization, Bat algorithm and Simulated Annealing Gaussian Bat algorithm). The experimental results show that QUAPSO outperforms the competing algorithms on a set of 30 test functions.

关键词： Particle Swarm Optimization Swarm intelligence algorithm auto-adaptive algorithm Self-organization

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A New Neighborhood Topology for QUAntum Particle Swarm Optimization (QUAPSO) 19

A New Neighborhood Topology for QUAntum Particle Swarm Optim...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick LiSSi EA 3956 Vitry Sur Seine France

ISBN: (纸本)9781450367486

Swarm Intelligence (SI) is a behavior, used first by Beni and Wang, corresponding to a system working with single and self-organized agents, interacting the ones with each other. This operating concept is implemented in many algorithms. Developed by Kennedy, Eberhart and Shi, Particle Swarm Optimization (PSO) is one of them. Its behavior is based on the movements of birds swarm, and its effectiveness, for looking for the optimal solution of a given problem, is well established. Nevertheless, PSO is known for its weakness in local search. Moreover, the behavior of PSO strongly depends on internal parameters settings. In order to address these problems, we propose a new type of self-adaptive Quantum PSO (QPSO), called QUAntum Particle Swarm Optimization (QUAPSO), based on quantum superposition to set the velocity parameters and hybridized with a Kangaroo algorithm in order to optimize its efficiency in local search. QUAPSO was compared with five known algorithms from the literature (classical PSO, Kangaroo algorithm, Simulated Annealing Particle Swarm Optimization, Bat algorithm and Simulated Annealing Gaussian Bat algorithm) on a set of 19 test functions. The results show that QUAPSO outperforms the competing algorithms.

关键词： Particle Swarm Optimization Swarm intelligence algorithm auto-adaptive algorithm Self-organization

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Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs

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MACHINE VISION AND APPLICATIONS 2021年第5期32卷 105-105页

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

Today, advances in 3D modeling make it possible to identically reproduce objects, animals, humans and even entire scenes. The broad applications concern video games, virtual reality or augmented reality and cinema, for example. In this article, we propose a new method to build a 3D scene directly from several complementary photographs. The positions of the objects for which we already have a 3D model will be determined by triangulation, thanks to the information extracted from the photographs, such as the outline of the objects on the images. Each pixel of the images is converted into a value that gives its distance to the nearest outline. The 3D model of the objects is then projected on the converted images, and the triangulation is done using a cost function that gives the distance of each projection of the objects to their respective outlines. A projection is considered perfect when its distance to its outlines is null, which means that the cost function gives a score of zero as well. We propose to solve this optimization problem by means of two algorithms, namely Simulated Annealing (SA) and quantum particle swarm optimization (QUAPSO).

关键词： Simulated Annealing Particle swarm optimization Swarm intelligence algorithm auto-adaptive algorithm 3D model

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