Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator

Kennedy, James; Spears, William M.

doi:10.1109/icec.1998.699326

Cited by 293 publications

(170 citation statements)

References 3 publications

Supporting

Mentioning

162

Contrasting

Unclassified

Order By: Relevance

“…It aims at increasing the probability of encountering global extremum points without performing a comprehensive search of the entire search space. PSO can easily be implemented and its performance is comparable to other stochastic optimization technique, such as genetic algorithm and simulated annealing [14].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Optimal design of double folded stub microstrip filter by neural network modelling and particle swarm optimization

Banookh

Barakati

2012

J. Microw. Optoelectron. Electromagn. Appl.

View full text Add to dashboard Cite

Abstract-Optimization of design parameters based on electromagnetic simulation of microwave circuits is a timeconsuming and iterative procedure. To provide a fast and accurate frequency response for a given case study, this paper employs a neural network modelling approach. First, one of the case study's outputs, i.e., scattering parameter (|S 21 |) in dB, is predicted using a neural network model. Then the particle swarm optimization is employed to optimize the design parameters. The proposed method in designing the filter compares with two others methods for a case study. The simulation results show the capability of the proposed method in designing an optimized filter in a proper time.Index Terms-neural networks, Microwave structures, particle swarm optimization, modelling.I. INTRODUCTION Neural networks are information processing systems inspired by the human brain's ability to learn from observations [1]. Neural networks are efficient alternatives to conventional methods, such as numerical modelling methods which could be computationally expensive, or analytical methods which could be difficult to obtain, or empirical models whose range and accuracy could be limited.Due to their ability and adaptability to learn, generalizability, fast real-time operation, and ease of implementation, they have been used in microwave design problems, RF, and microwave computeraided design problems [2].In microwave applications, a neural network can train to mimic the electrical behavior of the circuit. Then, it can be used in high-level simulation and design, providing a fast response to the learned task [3], [4]. Moreover, neural networks can be employed along with evolutionary algorithms such as genetic algorithm [5] and particle swarm optimization [6] to optimize the system under study.In this application, a neuarl network can use with an electromagnetic packages, such as HFSS, CST, ADS, to optimize the design.In this paper, to accelerate the frequency sweep required in the full-wave simulators, for calculating the S-parameters of a double folded stub microstrip filter, an artificial neural network is employed.The neural network is trained, by achieved patterns from an electromagnetic package, to approximate the S-parameters in the region of interest. The proposed model has capability to perform the frequency Sistan and Baluchestan, Iran Email: amirbanookh@ece.usb.ac.ir, smbaraka@ece.usb.ac.ir Journal of Microwaves, Optoelectronics and Electromagnetic Applications, Vol. 11, No. 1, June 2012 Brazilian The neural network structure is explained in the next section. Section 3 is dedicated to describe the of particle swarm optimization. The case study is introduced in section 4. To verify the proposed model, simulation results are illustrated in section 5. The last section is conclusions.II. THE STRUCTURE OF NEURAL NETWORK In this section, the structure of neural networks and its ability to model the behavior of RF and microwave components are described.The MLP is a commonly used neural network structure, in which th...

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Optimal design of double folded stub microstrip filter by neural network modelling and particle swarm optimization

Banookh

Barakati

2012

J. Microw. Optoelectron. Electromagn. Appl.

View full text Add to dashboard Cite

show abstract

“…However, so far it has not been used for the determination of unsaturated hydraulic properties. PSO is a relatively new algorithm for evolutionary computation methodology, but its performance has proven to be comparable to various other, more established methodologies (Kennedy and Spears, 1998;Shi et al, 1999). One of the main advantages of PSO is the easiness of its implementation (Liang et al, 2006).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A comprehensive numerical analysis of the hydraulic behavior of a permeable pavement

Brunetti

Šimůnek

Piro

2016

Journal of Hydrology

116

View full text Add to dashboard Cite

a b s t r a c tThe increasing frequency of flooding events in urban catchments related to an increase in impervious surfaces highlights the inadequacy of traditional urban drainage systems. Low Impact Development (LID) techniques have proven to be a viable and effective alternative by reducing stormwater runoff and increasing the infiltration and evapotranspiration capacity of urban areas. However, the lack of adequate modeling tools represents a barrier in designing and constructing such systems. This paper investigates the suitability of a mechanistic model, HYDRUS-1D, to correctly describe the hydraulic behavior of permeable pavement installed at the University of Calabria. Two different scenarios of describing the hydraulic behavior of the permeable pavement system were analyzed: the first one uses a singleporosity model for all layers of the permeable pavement; the second one uses a dual-porosity model for the base and sub-base layers. Measured and modeled month-long hydrographs were compared using the Nash-Sutcliffe efficiency (NSE) index. A Global Sensitivity Analysis (GSA) followed by a Monte Carlo filtering highlighted the influence of the wear layer on the hydraulic behavior of the pavement and identified the ranges of parameters generating behavioral solutions. Reduced ranges were then used in the calibration procedure conducted with the metaheuristic Particle swarm optimization (PSO) algorithm for the estimation of hydraulic parameters. The best fit value for the first scenario was NSE = 0.43; for the second scenario, it was NSE = 0.81, indicating that the dual-porosity approach is more appropriate for describing the variably-saturated flow in the base and sub-base layers. Estimated parameters were validated using an independent, month-long set of measurements, resulting in NSE values of 0.43 and 0.86 for the first and second scenarios, respectively. The improvement in correspondence between measured and modeled hydrographs confirmed the reliability of the combination of GSA and PSO in dealing with highly dimensional optimization problems. Obtained results have demonstrated that PSO, due to its easiness of implementation and effectiveness, can represent a new and viable alternative to traditional optimization algorithms for the inverse estimation of unsaturated hydraulic properties. Finally, the results confirmed the suitability and the accuracy of HYDRUS-1D in correctly describing the hydraulic behavior of permeable pavements.

show abstract

“…Eberhart and Kennedy found PSO performs on par with GA on the Schaffer f6 function [4], [9]. In work by Kennedy and Spears [10], a version of PSO outperforms GA in a factorial time-series experiment. Fourie showed that PSO appears to outperform GA in optimizing several standard size and shape design problems [6].…”

Section: Introductionmentioning

confidence: 99%

Parallel learning in heterogeneous multi-robot swarms

Pugh

Martinoli

2007

2007 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

Abstract-Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers in an automated fashion. In multi-robot systems, robots learning in parallel can share information to dramatically increase the evolutionary rate. However, manufacturing variations in robotic sensors may result in perceptual differences between robots, which could impact the learning process. In this paper, we explore how varying sensor offsets and scaling factors affects parallel swarm-robotic learning of obstacle avoidance behavior using both Genetic Algorithms and Particle Swarm Optimization. We also observe the diversity of robotic controllers throughout the learning process in an attempt to better understand the evolutionary process.

show abstract

Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator

Cited by 293 publications

References 3 publications

Optimal design of double folded stub microstrip filter by neural network modelling and particle swarm optimization

Optimal design of double folded stub microstrip filter by neural network modelling and particle swarm optimization

A comprehensive numerical analysis of the hydraulic behavior of a permeable pavement

Parallel learning in heterogeneous multi-robot swarms

Contact Info

Product

Resources

About