Generic Support for Precomputation-Based Global Routing Constraints in Local Search Optimization

2020 ◽  
pp. 151-165
Author(s):  
Renaud De Landtsheer ◽  
Fabian Germeau ◽  
Thomas Fayolle ◽  
Gustavo Ospina ◽  
Christophe Ponsard
Keyword(s):  
Local Search ◽  
Global Routing ◽  
Search Optimization

scholarly journals Local Search Optimization for a Multi-state Series-parallel System

2016 ◽  
Vol 82 ◽  
pp. 01017
Author(s):  
Ming-Ang Yin ◽  
Zhi-Li Sun ◽  
Jian Wang ◽  
Yu Guo
Keyword(s):  
Local Search ◽  
Parallel System ◽  
Search Optimization

AlineaGA—a genetic algorithm with local search optimization for multiple sequence alignment

2009 ◽  
Vol 32 (2) ◽  
pp. 164-172 ◽  
Author(s):  
Fernando José Mateus da Silva ◽  
Juan Manuel Sánchez Pérez ◽  
Juan Antonio Gómez Pulido ◽  
Miguel A. Vega Rodríguez
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Search Optimization

scholarly journals CoRSO (Collaborative Reactive Search Optimization): Blending Combinatorial and Continuous Local Search

Informatica ◽  
2016 ◽  
Vol 27 (2) ◽  
pp. 299-322 ◽  
Author(s):  
Mauro Brunato ◽  
Roberto Battiti
Keyword(s):  
Local Search ◽  
Search Optimization

scholarly journals LOCAL SEARCH OPTIMIZATION: A COMPARISON OF ALGORITHMS FOR NONHYPERBOLIC TRAVEL-TIME ANALYSIS

2019 ◽  
Vol 7 (4) ◽  
pp. 321-328
Author(s):  
Nelson Ricardo Flores Zuniga
Keyword(s):  
Local Search ◽  
Travel Time ◽  
Processing Time ◽  
Function Analysis ◽  
Optimization Algorithms ◽  
Time Analysis ◽  
Converted Wave ◽  
Search Optimization ◽  
Calculated Curve ◽  
Inversion Procedure

In the last decade, many works compared nonhyperbolic multiparametric travel-time approximations to perform velocity analysis. In these works, some analyses were accomplished, such as accuracy analysis and objective function analysis. However, no previous works compared the optimization algorithms to perform the inversion procedure concerning the processing time and the accuracy of each algorithm. As the shifted hyperbola showed the best results among the unimodal approximations in previous works, it was selected to be used in a comparison with five local search optimization algorithms. Each algorithm was compared concerning the accuracy by the minimization of the calculated curve to the observed curve. The travel-time curves tested here are conventional (PP) and converted wave (PS) reflection events from an offshore model. With this set of tests, it is possible to define which optimization algorithm presents the most reliable result when used with the shifted hyperbola equation concerning the processing time and the accuracy.

A Local Search Approach to Solve a Financial Portfolio Design Problem

2015 ◽  
Vol 6 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Fatima Zohra Lebbah ◽  
Yahia Lebbah
Keyword(s):  
Local Search ◽  
Design Problem ◽  
Optimization Technique ◽  
Quadratic Program ◽  
Evaluation Functions ◽  
Search Optimization ◽  
Quadratic Integer ◽  
Financial Portfolio ◽  
Search Approach ◽  
Portfolio Design

This paper introduces a local search optimization technique for solving efficiently a financial portfolio design problem which consists to affect assets to portfolios, allowing a compromise between maximizing gains and minimizing losses. This practical problem appears usually in financial engineering, such as in the design of CDO-squared portfolios. This problem has been modeled by Flener et al. who proposed an exact method to solve it. It can be formulated as a quadratic program on the 0-1 domain. It is well known that exact solving approaches on difficult and large instances of quadratic integer programs are known to be inefficient. That is why this work has adopted a local search method. It proposes neighborhood and evaluation functions specialized on this problem. To boost the local search process, it also proposes a greedy algorithm to start the search with an optimized initial configuration. Experimental results on non-trivial instances of the problem show the effectiveness of this work's approach.

Simple and Population Local Search Approaches for Portfolio Design Problem

2020 ◽  
pp. 119-147
Keyword(s):  
Local Search ◽  
Design Problem ◽  
Practical Problem ◽  
Optimization Technique ◽  
Quadratic Program ◽  
Integer Programs ◽  
Evaluation Functions ◽  
Search Optimization ◽  
Quadratic Integer ◽  
Portfolio Design

This chapter introduces a local search optimization technique for solving efficiently a ðnancial portfolio design problem that consists of assigning assets to portfolios, allowing a compromise between maximizing gains, and minimizing losses. This practical problem appears usually in ðnancial engineering, such as in the design of CDO-squared portfolios. This problem has been modeled by Flener et al., who proposed an exact method to solve it. It can be formulated as a quadratic program on the (0,1) domain. It is well known that exact solving approaches on difficult and large instances of quadratic integer programs are known inefficient. That is why the authors have adopted local search methods, namely simple local search and population local search. They propose neighborhood and evaluation functions specialized on this problem. To boost the local search process, they propose also a greedy algorithm to start the search with an optimized initial configuration. Experimental results on non-trivial instances of the problem show the effectiveness of the incomplete approach.

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