Space trajectory design: Analysis of a real-world many-objective optimization problem

2013 ◽  
Author(s):  
Antonio Lopez Jaimes ◽  
Akira Oyama ◽  
Kozo Fujii
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Design Analysis ◽  
Trajectory Design ◽  
Space Trajectory Design

scholarly journals Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

2011 ◽  
Vol 225 (11) ◽  
pp. 1211-1227 ◽  
Author(s):  
M Vasile ◽  
F Zuiani
Keyword(s):  
Selection Criterion ◽  
Search Space ◽  
Trajectory Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Agent Based ◽  
Multi Objective ◽  
Collaborative Search ◽  
Multi Agent ◽  
Space Trajectory Design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.

scholarly journals A trajectory design method for RLV via artificialmemory-principle optimization

2018 ◽  
Vol 189 ◽  
pp. 10019
Author(s):  
Hao Li ◽  
Changzhu Wei
Keyword(s):  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Memory Cell ◽  
Optimization Method ◽  
Alternative Solution ◽  
Trajectory Design ◽  
Memory State ◽  
Artificial Memory

A trajectory optimization method for RLV based on artificial memory principles is proposed. Firstly the optimization problem is modelled in Euclidean space. Then in order to solve the complicated optimization problem of RLV in entry phase, Artificial-memory-principle optimization (AMPO) is introduced. AMPO is inspired by memory principles, in which a memory cell consists the whole information of an alternative solution. The information includes solution state and memory state. The former is an evolutional alternative solution, the latter indicates the state type of memory cell: temporary, short-and long-term. In the evolution of optimization, AMPO makes a various search (stimulus) to ensure adaptability, if the stimulus is good, memory state will turn temporary to short-term, even long-term, otherwise it not. Finally, simulation of different methods is carried out respectively. Results show that the method based on AMPO has better performance and high convergence speed when solving complicated optimization problems of RLV.

scholarly journals A Graph Regularized Multilinear Mixing Model for Nonlinear Hyperspectral Unmixing

2019 ◽  
Vol 11 (19) ◽  
pp. 2188
Author(s):  
Li ◽  
Zhu ◽  
Guo ◽  
Chen
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Spatial Information ◽  
Mixing Model ◽  
Spectral Unmixing ◽  
Hyperspectral Images ◽  
Hyperspectral Unmixing ◽  
Manifold Structure ◽  
Underlying Manifold

Spectral unmixing of hyperspectral images is an important issue in the fields of remotesensing. Jointly exploring the spectral and spatial information embedded in the data is helpful toenhance the consistency between mixing/unmixing models and real scenarios. This paper proposesa graph regularized nonlinear unmixing method based on the recent multilinear mixing model(MLM). The MLM takes account of all orders of interactions between endmembers, and indicates thepixel-wise nonlinearity with a single probability parameter. By incorporating the Laplacian graphregularizers, the proposed method exploits the underlying manifold structure of the pixels’ spectra,in order to augment the estimations of both abundances and nonlinear probability parameters.Besides the spectrum-based regularizations, the sparsity of abundances is also incorporated for theproposed model. The resulting optimization problem is addressed by using the alternating directionmethod of multipliers (ADMM), yielding the so-called graph regularized MLM (G-MLM) algorithm.To implement the proposed method on large hypersepectral images in real world, we proposeto utilize a superpixel construction approach before unmixing, and then apply G-MLM on eachsuperpixel. The proposed methods achieve superior unmixing performances to state-of-the-artstrategies in terms of both abundances and probability parameters, on both synthetic and real datasets.

scholarly journals Constructing banking networks under decreasing costs of link formation

2021 ◽  
Author(s):  
Dietmar Maringer ◽  
Ben Craig ◽  
Sandra Paterlini
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Balance Sheet ◽  
Financial Networks ◽  
Fixed Costs ◽  
Balance Sheets ◽  
Search Heuristics ◽  
Banking Networks ◽  
Link Formation ◽  
Do So

AbstractThe structure of networks plays a central role in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: banks’ assets and liabilities are typically known, but not who is lending how much and to whom. This paper adds to the existing literature in two ways. First, it shows how to simulate realistic networks that are based on balance-sheet information. To do so, we introduce a model where links cause fixed-costs, independent of contract size; but the costs per link decrease the more connected a bank is (scale economies). Second, to approach the optimization problem, we develop a new algorithm inspired by the transportation planning literature and research in stochastic search heuristics. Computational experiments find that the resulting networks are not only consistent with the balance sheets, but also resemble real-world financial networks in their density (which is sparse but not minimally dense) and in their core-periphery and disassortative structure.

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