scholarly journals Generative Art with Swarm Landscapes

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1284
Author(s):  
Diogo de Andrade ◽  
Nuno Fachada ◽  
Carlos M. Fernandes ◽  
Agostinho C. Rosa
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Generative Art ◽  
Mathematical Expressions ◽  
Perlin Noise ◽  
Visual Artifacts

We present a generative swarm art project that creates 3D animations by running a Particle Swarm Optimization algorithm over synthetic landscapes produced by an objective function. Different kinds of functions are explored, including mathematical expressions, Perlin noise-based terrain, and several image-based procedures. A method for displaying the particle swarm exploring the search space in aesthetically pleasing ways is described. Several experiments are detailed and analyzed and a number of interesting visual artifacts are highlighted.

scholarly journals Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhaojuan Zhang ◽  
Wanliang Wang ◽  
Ruofan Xia ◽  
Gaofeng Pan ◽  
Jiandong Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Experimental Results ◽  
The Other ◽  
Ancestral Genome ◽  
Swarm Optimization ◽  
Median Approach

Abstract Background Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. Results In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. Conclusions Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

2013 ◽  
pp. 31-42
Author(s):  
Yuhong Chi ◽  
Fuchun Sun ◽  
Langfan Jiang ◽  
Chunyang Yu ◽  
Chunli Chen
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quotient Space ◽  
Particle Swarm ◽  
Search Space ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Speed

To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle swarm optimization algorithm, this paper applies the theory of topology, and proposed a quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism. In QsaBC, Search space-zoomed factor and Attractor are introduced according to the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions are selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can achieve the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective with errant particles, and has easier calculation and better robustness than other methods.

Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

2011 ◽  
Vol 3 (1) ◽  
pp. 78-89
Author(s):  
Yuhong Chi ◽  
Fuchun Sun ◽  
Langfan Jiang ◽  
Chunyang Yu ◽  
Chunli Chen
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quotient Space ◽  
Particle Swarm ◽  
Search Space ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Speed

To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle swarm optimization algorithm, this paper applies the theory of topology, and proposed a quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism. In QsaBC, Search space-zoomed factor and Attractor are introduced according to the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions are selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can achieve the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective with errant particles, and has easier calculation and better robustness than other methods.

scholarly journals Movement Particle Swarm Optimization Algorithm

2017 ◽  
Vol 12 (1) ◽  
pp. 148 ◽  
Author(s):  
Amjad A. Hudaib ◽  
Ahmad Kamel AL Hwaitat
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Grey Wolf Optimizer ◽  
Swarm Optimization ◽  
Movement Function ◽  
Sine Cosine Algorithm

Particle Swarm Optimization (PSO) ia a will known meta-heuristic that has been used in many applications for solving optimization problems. But it has some problems such as local minima. In this paper proposed a optimization algorithm called Movement Particle Swarm Optimization (MPSO) that enhances the behavior of PSO by using a random movement function to search for more points in the search space. The meta-heuristic has been experimented over 23 benchmark faction compared with state of the art algorithms: Multi-Verse Optimizer (MFO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO) and particle Swarm Optimization (PSO). The Results showed that the proposed algorithm has enhanced the PSO over the tested benchmarked functions.

An Improved Particle Swarm Optimization Algorithm Based on Quotient Space Theory

2012 ◽  
Vol 4 (2) ◽  
pp. 1-13
Author(s):  
Yuhong Chi ◽  
Fuchun Sun ◽  
Weijun Wang ◽  
Chunming Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quotient Space ◽  
Particle Swarm ◽  
Search Space ◽  
Local Optimum ◽  
Space Theory ◽  
Swarm Optimization ◽  
Search Speed

To control the swarm to fly inside the limited search space and deal with the problems of slow search speed and premature convergence in particle swarm optimization algorithm, the authors applied the theory of topology, and proposed a novel quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism in this paper. In QsaBC, Search space-zoomed factor and Attractor factor are introduced according to analyzing the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions were selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can get the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective to do with errant particles, easier to calculate and has better robustness than other experienced methods.

scholarly journals Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems

2018 ◽  
Vol 16 (1/2) ◽  
pp. 117-136 ◽  
Author(s):  
Mohamed A. Tawhid ◽  
Kevin B. Dsouza
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Feature Space ◽  
Search Space ◽  
Swarm Optimization ◽  
Selection Problems ◽  
Enhanced Particle Swarm Optimization

In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm (HBBEPSO). In the proposed HBBEPSO algorithm, we combine the bat algorithm with its capacity for echolocation helping explore the feature space and enhanced version of the particle swarm optimization with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBBEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBBEPSO algorithm to search the feature space for optimal feature combinations.

Sign in / Sign up

Export Citation Format

Share Document