8,421 research outputs found
Real-time trajectory optimization using a constrained genetic algorithm
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1993.Includes bibliographical references (leaves 69-70).by Paul G. van Deventer.M.S
Comparative study on the application of evolutionary optimization techniques to orbit transfer maneuvers
Orbit transfer maneuvers are here considered as benchmark cases for comparing performance of different optimization
techniques in the framework of direct methods. Two different classes of evolutionary algorithms, a
conventional genetic algorithm and an estimation of distribution method, are compared in terms of performance
indices statistically evaluated over a prescribed number of runs. At the same time, two different types of problem
representations are considered, a first one based on orbit propagation and a second one based on the solution of
Lambertâs problem for direct transfers. In this way it is possible to highlight how problem representation affects
the capabilities of the considered numerical approaches
Optimal design of water distribution systems based on entropy and topology
A new multi-objective evolutionary optimization approach for joint topology and pipe size design of water distribution systems is presented. The algorithm proposed considers simultaneously the adequacy of flow and pressure at the demand nodes; the initial construction cost; the network topology; and a measure of hydraulic capacity reliability. The optimization procedure is based on a general measure of hydraulic performance that combines statistical entropy, network connectivity and hydraulic feasibility. The topological properties of the solutions are accounted for and arbitrary assumptions regarding the quality of infeasible solutions are not applied. In other words, both feasible and infeasible solutions participate in the evolutionary processes; solutions survive and reproduce or perish strictly according to their Pareto-optimality. Removing artificial barriers in this way frees the algorithm to evolve optimal solutions quickly. Furthermore, any redundant binary codes that result from crossover or mutation are eliminated gradually in a seamless and generic way that avoids the arbitrary loss of potentially useful genetic material and preserves the quality of the information that is transmitted from one generation to the next. The approach proposed is entirely generic: we have not introduced any additional parameters that require calibration on a case-by-case basis. Detailed and extensive results for two test problems are included that suggest the approach is highly effective. In general, the frontier-optimal solutions achieved include topologies that are fully branched, partially- and fully-looped and, for networks with multiple sources, completely separate sub-networks
Visualization of Global Trade-Offs in Aerodynamic Problems by ARMOGAs
Trade-offs is one of important elements for engineering design problems characterized by multiple conflicting design objectives to be simultaneously improved.
In many design problems such as aerodynamic design, due to computational reasons, only a limited number of evaluations can be allowed for industrial use.
Efficient MOEAs, Adaptive Range Multi-Objective Genetic Algorithms (ARMOGAs), to identify trade-offs using a small number of function evaluations have been developed.
In this study, ARMOGAs are applied to aerodynamic designs problems to identify trade-offs efficiently.
In addition to identify trade-offs, trade-off analysis is also important to obtain useful knowledge about the design problem.
To analyze the high-dimensional data of aerodynamic optimization problem, Self-Organizing Maps are applied to understand the trade-offs
A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study
The main real-coded genetic algorithm (RCGA) research effort has been spent on developing
efficient crossover operators. This study presents a taxonomy for this operator that groups its
instances in different categories according to the way they generate the genes of the offspring
from the genes of the parents. The empirical study of representative crossovers of all the
categories reveals concrete features that allow the crossover operator to have a positive influence
on RCGA performance. They may be useful to design more effective crossover models
Multi-concentric optimal charging cordon design
The performance of a road pricing scheme varies greatly by its actual design and implementation. The design
of the scheme is also normally constrained by several practicality requirements. One of the practicality
requirements which is tackled in this paper is the topology of the charging scheme. The cordon shape of the
pricing scheme is preferred due to its user-friendliness (i.e. the scheme can be understood easily). This has
been the design concept for several real world cases (e.g. the schemes in London, Singapore, and Norway).
The paper develops a methodology for defining an optimal location of a multi-concentric charging cordons
scheme using Genetic Algorithm (GA). The branch-tree structure is developed to represent a valid charging
cordon scheme which can be coded using two strings of node numbers and number of descend nodes. This
branch-tree structure for a single cordon is then extended to the case with multi-concentric charging cordons.
GA is then used to evolve the design of a multi-concentric charging cordons scheme encapsulated in the twostring
chromosome. The algorithm developed, called GA-AS, is then tested with the network of the Edinburgh
city in UK. The results suggest substantial improvements of the benefit from the optimised charging cordon
schemes as compared to the judgemental ones which illustrate the potential of this algorithm
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
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