An intelligent algorithm with interactive learning mechanism for high‐dimensional optimization problem based on improved animal migration optimization

2020 ◽  
Author(s):  
Zhaolin Lai ◽  
Xiaochun Hu ◽  
Caoqing Jiang
Keyword(s):  
Optimization Problem ◽  
Interactive Learning ◽  
High Dimensional ◽  
Intelligent Algorithm ◽  
Learning Mechanism ◽  
Animal Migration Optimization ◽  
Animal Migration ◽  
Dimensional Optimization

A New Simple Micro-PSO for High Dimensional Optimization Problem

2012 ◽  
Vol 236-237 ◽  
pp. 1195-1200
Author(s):  
Wen Hua Han
Keyword(s):  
Optimization Problem ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Population Based ◽  
Global Optimum ◽  
Stochastic Search ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Search Optimization ◽  
Dimensional Optimization

The particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search optimization technique, which has already been widely used to various of fields. In this paper, a simple micro-PSO is proposed for high dimensional optimization problem, which is resulted from being introduced escape boundary and perturbation for global optimum. The advantages of the simple micro-PSO are more simple and easily implemented than the previous micro-PSO. Experiments were conducted using Griewank, Rosenbrock, Ackley, Tablets functions. The experimental results demonstrate that the simple micro-PSO are higher optimization precision and faster convergence rate than PSO and robust for the dimension of the optimization problem.

scholarly journals Efficient Ensemble Refinement by Reweighting

2019 ◽  
Author(s):  
Juergen Koefinger ◽  
Lukas S. Stelzl ◽  
Klaus Reuter ◽  
Cesar Allande ◽  
Katrin Reichel ◽  
...  
Keyword(s):  
Experimental Data ◽  
Structural Biology ◽  
Optimization Problem ◽  
Structural Models ◽  
High Dimensional ◽  
Biological Macromolecules ◽  
Structural Ensembles ◽  
Integrative Structural Biology ◽  
Dynamics Simulations ◽  
Dimensional Optimization

<pre>In integrative structural biology/hybrid modeling approaches, we integrate structural models of macromolecules and experimental data to obtain faithful representations of the structures underlying the data. For example, in ensemble refinement by reweighting we first generate structural ensembles of flexible and dynamic biological macromolecules in molecular simulations. In a subsequent reweighting step, we refine the statistical weights of the structures to strike a balance between the information provided by simulations and by experimental data. For the "Bayesian inference of ensembles" approach (BioEn), we present two complementary methods to solve the underlying challenging high-dimensional optimization problem. We systematically investigate reliability, accuracy, and efficiency of these methods and integrate molecular dynamics simulations of the disordered peptide Ala-5 and NMR J-couplings. We provide an open-source library free of charge at <a href="https://github.com/bio-phys/BioEN/optimize">https://github.com/bio-phys/BioEn</a>.</pre>

scholarly journals Efficient Ensemble Refinement by Reweighting

2019 ◽  
Author(s):  
Juergen Koefinger ◽  
Lukas S. Stelzl ◽  
Klaus Reuter ◽  
Cesar Allande ◽  
Katrin Reichel ◽  
...  
Keyword(s):  
Experimental Data ◽  
Structural Biology ◽  
Optimization Problem ◽  
Structural Models ◽  
High Dimensional ◽  
Biological Macromolecules ◽  
Structural Ensembles ◽  
Integrative Structural Biology ◽  
Dynamics Simulations ◽  
Dimensional Optimization

<pre>In integrative structural biology/hybrid modeling approaches, we integrate structural models of macromolecules and experimental data to obtain faithful representations of the structures underlying the data. For example, in ensemble refinement by reweighting we first generate structural ensembles of flexible and dynamic biological macromolecules in molecular simulations. In a subsequent reweighting step, we refine the statistical weights of the structures to strike a balance between the information provided by simulations and by experimental data. For the "Bayesian inference of ensembles" approach (BioEn), we present two complementary methods to solve the underlying challenging high-dimensional optimization problem. We systematically investigate reliability, accuracy, and efficiency of these methods and integrate molecular dynamics simulations of the disordered peptide Ala-5 and NMR J-couplings. We provide an open-source library free of charge at <a href="https://github.com/bio-phys/BioEN/optimize">https://github.com/bio-phys/BioEn</a>.</pre>

scholarly journals Improved Chicken Swarm Optimization Method for Reentry Trajectory Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yu Wu ◽  
Bo Yan ◽  
Xiangju Qu
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problem ◽  
High Dimensional ◽  
Local Optimum ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization ◽  
Chicken Swarm Optimization ◽  
Dimensional Optimization

Reentry trajectory optimization has been researched as a popular topic because of its wide applications in both military and civilian use. It is a challenging problem owing to its strong nonlinearity in motion equations and constraints. Besides, it is a high-dimensional optimization problem. In this paper, an improved chicken swarm optimization (ICSO) method is proposed considering that the chicken swarm optimization (CSO) method is easy to fall into local optimum when solving high-dimensional optimization problem. Firstly, the model used in this study is described, including its characteristic, the nonlinear constraints, and cost function. Then, by introducing the crossover operator, the principles and the advantages of the ICSO algorithm are explained. Finally, the ICSO method solving the reentry trajectory optimization problem is proposed. The control variables are discretized at a set of Chebyshev collocation points, and the angle of attack is set to fit with the flight velocity to make the optimization efficient. Based on those operations, the process of ICSO method is depicted. Experiments are conducted using five algorithms under different indexes, and the superiority of the proposed ICSO algorithm is demonstrated. Another group of experiments are carried out to investigate the influence of hen percentage on the algorithm’s performance.

scholarly journals Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

2018 ◽  
Vol 11 (1) ◽  
pp. 300 ◽  
Author(s):  
Samar Bashath ◽  
Amelia Ritahani Ismail
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
High Dimensional ◽  
High Dimensions ◽  
Solution Accuracy ◽  
Dimensional Optimization ◽  
Real World Problems ◽  
Comprehensive Study

<p>High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting one algorithm among others for solving the high dimensional optimization problem is not an easily accomplished task. This paper presents a comprehensive study of two swarm intelligence based algorithms: 1-particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions.</p><p> </p>

scholarly journals Efficient Ensemble Refinement by Reweighting

2018 ◽  
Author(s):  
Juergen Koefinger ◽  
Lukas S. Stelzl ◽  
Klaus Reuter ◽  
Cesar Allande ◽  
Katrin Reichel ◽  
...  
Keyword(s):  
Experimental Data ◽  
Structural Biology ◽  
Optimization Problem ◽  
Structural Models ◽  
High Dimensional ◽  
Biological Macromolecules ◽  
Structural Ensembles ◽  
Integrative Structural Biology ◽  
Dynamics Simulations ◽  
Dimensional Optimization

<pre>In integrative structural biology/hybrid modeling approaches, we integrate structural models of macromolecules and experimental data to obtain faithful representations of the structures underlying the data. For example, in ensemble refinement by reweighting we first generate structural ensembles of flexible and dynamic biological macromolecules in molecular simulations. In a subsequent reweighting step, we refine the statistical weights of the structures to strike a balance between the information provided by simulations and by experimental data. For the "Bayesian inference of ensembles" approach (BioEn), we present two complementary methods to solve the underlying challenging high-dimensional optimization problem. We systematically investigate reliability, accuracy, and efficiency of these methods and integrate molecular dynamics simulations of the disordered peptide Ala-5 and NMR J-couplings. We provide an open-source library free of charge at <a href="https://github.com/bio-phys/BioEN/optimize">https://github.com/bio-phys/BioEn</a>.</pre>

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