scholarly journals Local Search-based Hybrid Algorithms for Finding Golomb Rulers

Constraints ◽  
2007 ◽  
Vol 12 (3) ◽  
pp. 263-291 ◽  
Author(s):  
Carlos Cotta ◽  
Iván Dotú ◽  
Antonio J. Fernández ◽  
Pascal Van Hentenryck
Keyword(s):  
Local Search ◽  
Hybrid Algorithms ◽  
Golomb Rulers

Hybridizing Shuffled Frog Leaping and Shuffled Complex Evolution Algorithms Using Local Search Methods

2014 ◽  
Vol 5 (1) ◽  
pp. 30-51
Author(s):  
Morteza Alinia Ahandani ◽  
Hosein Alavi-Rad
Keyword(s):  
Local Search ◽  
Success Rate ◽  
Hybrid Algorithm ◽  
Hybrid Algorithms ◽  
Search Method ◽  
High Dimensional ◽  
Shuffled Complex Evolution ◽  
Random Optimization ◽  
Shuffled Frog Leaping ◽  
Low Dimensional

In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two meta-heuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hybrid strategy that is combined from evolutionary process of each subset in related algorithm and a local search method. These hybrid algorithms are evaluated on low and high dimensional continuous benchmark functions and the obtained results are compared with their non-hybrid competitors. The obtained results demonstrate that the hybrid algorithm combined from shuffled frog leaping and Nelder–Mead simplex has a better success rate but a higher number of function evaluations on low-dimensional functions than the shuffled frog leaping. Whereas on high-dimensional functions it has a better success rate and a faster performance. Also the hybrid algorithm combined from shuffled complex evolution and bidirectional random optimization obtains a better performance in terms of success rate and function evaluations than shuffled complex evolution on low dimensional functions; whereas on high-dimensional functions, it obtains a better success rate but a slower performance. Also a comparison of our hybrid algorithms with the other evolutionary algorithms reported in the literature confirms our proposed algorithms have the best performance among all compared algorithms.

Investigating of Hybrid Meta-Heuristics to Solve the Large-Scale Multi-Source Weber Problems and Performance Measuring of them with Statistical Tests

2013 ◽  
pp. 171-197
Author(s):  
Abdolsalam Ghaderi
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Large Scale ◽  
Statistical Tests ◽  
Hybrid Algorithms ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Local Optima ◽  
Location Allocation ◽  
Large Scale Problems

The location–allocation problems are a class of complicated optimization problems that requires finding sites for m facilities and to simultaneously allocate n customers to those facilities to minimize the total transportation costs. Indeed, these problems, belonging to the class NP-hard, have a lot of local optima solutions. In this chapter, three hybrid meta-heuristics: genetic algorithm, variable neighborhood search and particle swarm optimization, and a hybrid local search approach. These are investigated to solve the uncapacitated continuous location-allocation problem (multi-source Weber problem). In this regard, alternate location allocation and exchange heuristics are used to find the local optima of the problem within the framework of hybrid algorithms. In addition, some large-scale problems are employed to measure the effectiveness and efficiency of hybrid algorithms. Obtained results from these heuristics are compared with local search methods and with each other. The experimental results show that the hybrid meta-heuristics produce much better solutions to solve large-scale problems. Moreover, the results of two non-parametric statistical tests detected a significant difference in hybrid algorithms such that the hybrid variable neighborhood search and particle swarm optimization algorithm outperform the others.

Hybrid Genetic Algorithms in Data Mining Applications

2011 ◽  
pp. 993-998
Author(s):  
Sancho Salcedo-Sanz ◽  
Gustavo Camps-Valls ◽  
Carlos Bousoño-Calzón
Keyword(s):  
Data Mining ◽  
Genetic Algorithms ◽  
Local Search ◽  
Hybrid Methods ◽  
Hybrid Genetic Algorithm ◽  
Building Blocks ◽  
Search Space ◽  
Hybrid Algorithms ◽  
Search Heuristic ◽  
Local Search Heuristic

Genetic algorithms (GAs) are a class of problem solving techniques which have been successfully applied to a wide variety of hard problems (Goldberg, 1989). In spite of conventional GAs are interesting approaches to several problems, in which they are able to obtain very good solutions, there exist cases in which the application of a conventional GA has shown poor results. Poor performance of GAs completely depends on the problem. In general, problems severely constrained or problems with difficult objective functions are hard to be optimized using GAs. Regarding the difficulty of a problem for a GA there is a well established theory. Traditionally, this has been studied for binary encoded problems using the so called Walsh Transform (Liepins & Vose, 1991), and its associated spectrum (Hordijk & Stadler, 1998), which provides an idea of the distribution of the important schemas (building blocks) in the search space. Several methods to enhance the performance of GAs in difficult applications have been developed. Firstly, the encoding of a problem determines the search space where the GA must work. Therefore, given a problem, the selection of the best encoding is an important pre-processing step. Operators which reduce the search space are then interesting in some applications. Secondly, variable length or transformed encodings are schemes, which can be successfully applied to some difficult problems. The hybridization of a GA with local search algorithms can also improve the performance of the GA in concrete applications. There are two types of hybridization: • If the GA is hybridized with a local search heuristic in order to tackle the problem constraints, it is usually known as a hybrid genetic algorithm. • If the GA is hybridized with a local search heuristic in order to improve its performance, then it is known as a memetic algorithm. In this chapter we revise several hybrid methods involving GAs that have been applied to data mining problems. First, we provide a brief background with several important definitions on genetic algorithms, hybrid algorithms and operators for improving its performance. In the Main Trust section, we present a survey of several hybrid algorithms, which use GAs as search heuristic, and their main applications in data mining. Finally, we finish the chapter giving some conclusions and future trends.

A Chaotic Local Search Algorithm with Adaptive Exchange of Elements for Quadratic Assignment Problems

2014 ◽  
Vol 2 ◽  
pp. 362-365
Author(s):  
Akio Watanabe ◽  
Kaori Kuroda ◽  
Kantaro Fujiwara ◽  
Tohru Ikeguchi
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Quadratic Assignment ◽  
Local Search Algorithm ◽  
Assignment Problems ◽  
Quadratic Assignment Problems

Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 114-118
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Local Search ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Premature Convergence ◽  
Speed Of Convergence ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.

Character Reorganization and Categorization using Hybrid Algorithms by Scanned Documents

2019 ◽  
Vol 8 (6S2) ◽  
pp. 943-948
Keyword(s):  
Research Work ◽  
Hybrid Algorithms ◽  
Research Topic ◽  
Image Processing Techniques ◽  
Digital Books ◽  
Otsu Method ◽  
Challenging Research ◽  
Processing Techniques ◽  
Scanned Documents ◽  
Day By Day

Detection and reorganization of text may save a lot of time while reproducing old books text and its chapters. This is really challenging research topic as different books may have different font types and styles. The digital books and eBooks reading habit is increasing day by day and new documents are producing every day. So in order to boost the process the text reorganization using digital image processing techniques can be used. This research work is using hybrid algorithms and morphological algorithms. For sample we have taken an letter pad where the text and images are separated using algorithms. The another objective of this research is to increase the accuracy of recognized text and produce accurate results. This research worked on two different concepts, first is concept of Pixel-level thresholding processing and another one is Otsu Method thresholding.

Sign in / Sign up

Export Citation Format

Share Document