scholarly journals Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models

2017 ◽  
Author(s):  
Adithya Sagar ◽  
Rachel LeCover ◽  
Christine Shoemaker ◽  
Jeffrey Varner
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Real World ◽  
Particle Swarm ◽  
Coagulation Cascade ◽  
Benchmark Problems ◽  
Test Functions ◽  
Swarm Optimization ◽  
Particle Swarms

AbstractBackgroundMathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS). DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.ResultsWe tested DOPS using classic optimization test functions, biochemical benchmark problems and real-world biochemical models. We performed trials with function evaluations per trial, and compared the performance of DOPS with other commonly used meta-heuristics such as differential evolution (DE), simulated annealing (SA) and dynamically dimensioned search (DDS). On average, DOPS outperformed other common meta-heuristics on the optimization test functions, benchmark problems and a real-world model of the human coagulation cascade.ConclusionsDOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. DOPS source code is available for download under a MIT license at http://www.varnerlab.org.

scholarly journals Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 597
Author(s):  
Kun Miao ◽  
Qian Feng ◽  
Wei Kuang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Small Region ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Searches ◽  
Engineering Design Problems ◽  
Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

scholarly journals DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS

Irriga ◽  
2018 ◽  
Vol 23 (4) ◽  
pp. 798-817
Author(s):  
Saulo de Tarso Marques Bezerra ◽  
José Eloim Silva de Macêdo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Water Distribution ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computational Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Irrigation Network

DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS     SAULO DE TARSO MARQUES BEZERRA1 E JOSÉ ELOIM SILVA DE MACÊDO2   1 Universidade Federal de Pernambuco, Campus Agreste, Núcleo de Tecnologia, Avenida Campina Grande, S/N, Bairro Nova Caruaru, CEP 55014-900, Caruaru, Pernambuco, Brasil. [email protected]. 2 Centro Universitário Maurício de Nassau, Departamento de Engenharia Civil, BR 104, Km 68, S/N, Bairro Agamenon Magalhães, CEP 55000-000, Caruaru, Pernambuco, Brasil. [email protected].     1 RESUMO   Apresenta-se, neste trabalho, um modelo de otimização para o dimensionamento de sistemas pressurizados de distribuição de água para projetos de irrigação. A metodologia empregada é fundamentada no algoritmo Otimização por Enxame de Partículas (PSO), que é inspirada na dinâmica e comportamento social observados em muitas espécies de pássaros, insetos e cardumes de peixes. O PSO proposto foi aplicado em dois benchmark problems reportados na literatura, que correspondem à Hanoi network e a um sistema de irrigação localizado na Espanha. O dimensionamento resultou, para as mesmas condições de contorno, na solução de ótimo global para a Hanoi network, enquanto a aplicação do PSO na Balerma irrigation network demonstrou que o método proposto foi capaz de encontrar soluções quase ótimas para um sistema de grande porte com um tempo computacional razoável.   Palavras-chave: água, irrigação, análise econômica.     BEZERRA, S. T. M.; MACÊDO, J. E. S. LOOPED WATER DISTRIBUTION NETWORKS DESIGN VIA PARTICLE SWARM OPTIMIZATION ALGORITHM     2 ABSTRACT   This paper presents an optimization model for the design of pressurized water distribution systems for irrigation projects. The methodology is based on the Particle Swarm Optimization algorithm (PSO), which is inspired by the social foraging behavior of some animals such as flocking behavior of birds and the schooling behavior of fish. The proposed PSO has been tested on two benchmark problems reported in the literature, which correspond to the Hanoi network and an irrigation system located in Spain. The design resulted in the global optimum for the Hanoi network, while the application of PSO in Balerma irrigation network demonstrated that the proposed method was able to find almost optimal solutions for a large-scale network with reasonable computational time.   Keywords: water, irrigation, economic analysis. O desempenho do método foi comparado com trabalhos prévios, demonstrando convergência rápida e resultados satisfatórios na busca da solução ótima de um sistema com elevado exigência computacional.

A PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEMS

2005 ◽  
Vol 02 (03) ◽  
pp. 419-430 ◽  
Author(s):  
H. W. GE ◽  
Y. C. LIANG ◽  
Y. ZHOU ◽  
X. C. GUO
Keyword(s):  
Particle Swarm Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Traditional Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Novel Concept

A novel particle swarm optimization (PSO)-based algorithm is developed for job-shop scheduling problems (JSSP), which are the most general and difficult issues in traditional scheduling problems. Our goal is to develop an efficient algorithm based on swarm intelligence for the JSSP. Thereafter a novel concept for the distance and velocity of particles in the PSO is proposed and introduced to pave the way for the JSSP. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing systems, with simulation results showing the possibilities of high quality solutions for typical benchmark problems.

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