A Quadratic Approximation-Based Local Search Procedure for Multiobjective Genetic Algorithms

2006 ◽  
Author(s):  
E.F. Wanner ◽  
F.G. Guimaraes ◽  
R.H.C. Takahashi ◽  
P.J. Fleming
Keyword(s):  
Genetic Algorithms ◽  
Local Search ◽  
Quadratic Approximation ◽  
Search Procedure ◽  
Multiobjective Genetic Algorithms ◽  
Local Search Procedure

A fast swap-based local search procedure for location problems

2007 ◽  
Vol 150 (1) ◽  
pp. 205-230 ◽  
Author(s):  
Mauricio G. C. Resende ◽  
Renato F. Werneck
Keyword(s):  
Local Search ◽  
Location Problems ◽  
Search Procedure ◽  
Local Search Procedure

scholarly journals Multi-Objective GRASP for Maximizing Diversity

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1232
Author(s):  
Pedro Casas-Martínez ◽  
Alejandra Casado-Ceballos ◽  
Jesús Sánchez-Oro ◽  
Eduardo G. Pardo
Keyword(s):  
Local Search ◽  
Statistical Tests ◽  
Solution Space ◽  
Efficient Solutions ◽  
Previous Method ◽  
Search Procedure ◽  
Adaptive Search ◽  
Neighborhood Structure ◽  
Multi Objective ◽  
Local Search Procedure

This work presents a novel greedy randomized adaptive search procedure approach for dealing with the maximum diversity problem from a multi-objective perspective. In particular, five of the most extended diversity metrics were considered, with the aim of maximizing all of them simultaneously. The metrics considered have been proven to be in conflict, i.e., it is not possible to optimize one metric without deteriorating another one. Therefore, this results in a multi-objective optimization problem where a set of efficient solutions that are diverse with respect to all the metrics at the same time must be obtained. A novel adaptation of the well-known greedy randomized adaptive search procedure, which has been traditionally used for single-objective optimization, was proposed. Two new constructive procedures are presented to generate a set of efficient solutions. Then, the improvement phase of the proposed algorithm consists of a new efficient local search procedure based on an exchange neighborhood structure that follows a first improvement approach. An effective exploration of the exchange neighborhood structure is also presented, to firstly explore the most promising ones. This feature allowed the local search proposed to limit the size of the neighborhood explored, resulting in an efficient exploration of the solution space. The computational experiments showed the merit of the proposed algorithm, when comparing the obtained results with the best previous method in the literature. Additionally, new multi-objective evolutionary algorithms derived from the state-of-the-art were also included in the comparison, to prove the quality of the proposal. Furthermore, the differences found were supported by non-parametric statistical tests.

Using Iterated Local Search for Efficiently Packing Unequal Disks in a Larger Circle

2012 ◽  
Vol 430-432 ◽  
pp. 1477-1481 ◽  
Author(s):  
Wen Qi Huang ◽  
Zhi Zhong Zeng ◽  
Ru Chu Xu ◽  
Zhang Hua Fu
Keyword(s):  
Local Search ◽  
Iterated Local Search ◽  
Critical Element ◽  
Search Procedure ◽  
Hard Problem ◽  
Bfgs Method ◽  
Real World Applications ◽  
Local Search Procedure ◽  
Np Hard Problem ◽  
World Records

Packing unequal disks in a container as small as possible without mutual overlap is a NP-hard problem with a plenty of real world applications. In this paper, we introduced iterated local search to tackle with this problem, and using Critical Element Guided Perturbation (CEGP) strategy to jump out the local minimal, and using BFGS method to get each neighborhood optimized in local search procedure. Experiments showed its efficiency by breaking several world records of a set of benchmark.

An Overview of the Last Advances and Applications of Greedy Randomized Adaptive Search Procedure

2018 ◽  
pp. 264-284 ◽  
Author(s):  
Airam Expósito Márquez ◽  
Christopher Expósito-Izquierdo
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Approximate Solutions ◽  
Search Procedure ◽  
Adaptive Search ◽  
Wide Range ◽  
Local Search Procedure ◽  
Complex Optimization

One of the most studied methods to get approximate solutions in optimization problems are the heuristics methods. Heuristics are usually employed to find good, but not necessarily optima solutions. The primary purpose of the chapter at hand is to provide a survey of the Greedy Randomized Adaptive Search Procedures (GRASP). GRASP is an iterative multi-start metaheuristic for solving complex optimization problems. Each GRASP iteration consists of a construction phase followed by a local search procedure. In this paper, we first describe the basic components of GRASP and the various elements that compose it. We present different variations of the basic GRASP in order to improve its performance. The GRASP has encompassed a wide range of applications, covering different fields because of its robustness and easy to apply.

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