Performance Comparison of Differential Evolution and Particle Swarm Optimization in Constrained Optimization

Iwan, Mahmud; Akmeliawati, Rini; Faisal, Tarig; Al-Assadi, Hayder M. A. Ali

doi:10.1016/j.proeng.2012.07.317

“…Therefore, a more accurate methodology for coordinate optimization is highly desirable, as investigated herein. The evaluation of εDE as an optimization method for this critical task was inspired by previous results obtained for other types of optimization problems where this approach displayed better performance than alternative evolutionary methods, such as genetic algorithms or particle swarm optimization [27][28][29] . Moreover, ε-based lexicological comparison of individual feature vectors makes this algorithm straightforward to apply to problems where several constraints must be balanced, as is the case in inverse QSAR.…”

Section: Resultsmentioning

confidence: 99%

Exploring differential evolution for inverse QSAR analysis

Miyao

¹

,

Funatsu

²

,

Bajorath

³

2017

View full text Add to dashboard Cite

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

show abstract

“…Therefore, a more accurate methodology for coordinate optimization is highly desirable, as investigated herein. The evaluation of εDE as an optimization method for this critical task was inspired by previous results obtained for other types of optimization problems where this approach displayed better performance than alternative evolutionary methods, such as genetic algorithms or particle swarm optimization 30– 32 . Moreover, ε-based lexicological comparison of individual feature vectors makes this algorithm straightforward to apply to problems where several constraints must be balanced, as is the case in inverse QSAR.…”

Section: Resultsmentioning

confidence: 99%

Exploring differential evolution for inverse QSAR analysis

Miyao

¹

,

Funatsu

²

,

Bajorath

³

2017

View full text Add to dashboard Cite

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

show abstract

“…However, these studies have provided some insights on the initialization of these parameters (Eberhart and Shi ; Van Den Bergh and Engelbrecht ; Iwan et al . ).…”

Section: Theory and Methodsmentioning

confidence: 97%

“…Empirical studies have shown that performances of EAs are sensitive to the input control parameters, especially DE and CPSO. However, these studies have provided some insights on the initialization of these parameters (Eberhart and Shi 2000;Van Den Bergh and Engelbrecht 2006;Iwan et al 2012). In practice, we followed these guidelines that turn out to be very robust and summarize in Table 2 the parameter values for each EA.…”

Section: Control Parameter Valuesmentioning

confidence: 99%

Towards large‐scale stochastic refraction tomography: a comparison of three evolutionary algorithms

Luu

¹

,

Noble

²

,

Gesret

³

et al. 2019

Geophysical Prospecting

1

0

View full text Add to dashboard Cite

The main goal of this study is to assess the potential of evolutionary algorithms to solve highly non‐linear and multi‐modal tomography problems (such as first arrival traveltime tomography) and their abilities to estimate reliable uncertainties. Classical tomography methods apply derivative‐based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative‐based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population‐based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the differential evolution, the competitive particle swarm optimization and the covariance matrix adaptation–evolution strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty. By performing scalability and statistical analysis over the results obtained with several runs, we assess the benefits and shortcomings of each algorithm.

show abstract

Performance Comparison of Differential Evolution and Particle Swarm Optimization in Constrained Optimization

Cited by 35 publications

References 15 publications

Exploring differential evolution for inverse QSAR analysis

Exploring differential evolution for inverse QSAR analysis

Exploring differential evolution for inverse QSAR analysis

Towards large‐scale stochastic refraction tomography: a comparison of three evolutionary algorithms

Contact Info

Product

Resources

About