A tutorial on multiobjective optimization: fundamentals and evolutionary methods
MTM Emmerich, AH Deutz - Natural computing, 2018 - Springer
In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …
has been so useful as in solving multiobjective optimization problems. The idea of using a …
Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently
The amino acid sequence of a protein affects both its structure and its function. Thus, the
ability to modify the sequence, and hence the structure and activity, of individual proteins in …
ability to modify the sequence, and hence the structure and activity, of individual proteins in …
Multi-objective optimisation using evolutionary algorithms: an introduction
K Deb - Multi-objective evolutionary optimisation for product …, 2011 - Springer
As the name suggests, multi-objective optimisation involves optimising a number of
objectives simultaneously. The problem becomes challenging when the objectives are of …
objectives simultaneously. The problem becomes challenging when the objectives are of …
Multi-objective optimization
CONTENTS 3.1 Introduction.................................................... 146 3.2 MO
Basics..................................................... 1463.2. 1 Principles of MO............................................ 147 …
Basics..................................................... 1463.2. 1 Principles of MO............................................ 147 …
A multi-facet survey on memetic computation
Memetic computation is a paradigm that uses the notion of meme (s) as units of information
encoded in computational representations for the purpose of problem-solving. It covers a …
encoded in computational representations for the purpose of problem-solving. It covers a …
A unified evolutionary optimization procedure for single, multiple, and many objectives
Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve
mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts …
mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts …
Particle swarm optimization with sequential niche technique for dynamic finite element model updating
Due to uncertainties associated with material properties, structural geometry, boundary
conditions, and connectivity of structural parts as well as inherent simplifying assumptions in …
conditions, and connectivity of structural parts as well as inherent simplifying assumptions in …
Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield
M Anemangely, A Ramezanzadeh… - Journal of Natural Gas …, 2017 - Elsevier
Among petrophysical logs, shear wave velocity is known to provide more accurate results in
terms of mechanical rock properties. However, due to higher costs associated with acquiring …
terms of mechanical rock properties. However, due to higher costs associated with acquiring …
A method for constrained multiobjective optimization based on SQP techniques
We propose a method for constrained and unconstrained nonlinear multiobjective
optimization problems that is based on an SQP-type approach. The proposed algorithm …
optimization problems that is based on an SQP-type approach. The proposed algorithm …
[HTML][HTML] Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models
To achieve carbon dioxide (CO 2) storage through enhanced oil recovery, accurate
forecasting of CO 2 subsurface storage and cumulative oil production is essential. This study …
forecasting of CO 2 subsurface storage and cumulative oil production is essential. This study …