scholarly journals Selection of search strategies for solving 3-SAT problems

2014 ◽  
Vol 24 (2) ◽  
pp. 283-297
Author(s):  
Andrzej Pułka
Keyword(s):  
Electronic System ◽  
Search Space ◽  
Commonsense Reasoning ◽  
Sat Solving ◽  
Multiple Variable ◽  
Pruning Algorithms ◽  
System Verification ◽  
Satisfiability Checking ◽  
Search Space Pruning ◽  
Selection Of

Abstract The paper concerns the problem of Boolean satisfiability checking, which is recognized as one of the most important issues in the field of modern digital electronic system verification and design. The paper analyzes different strategies and scenarios of the proving process, and presents a modified and extended version of the author’s FUDASAT algorithm. The original FUDASAT methodology is an intuitive approach that employs a commonsense reasoning methodology. The main objective of the work is to investigate the SAT-solving process and try to formulate a set of rules controlling the reasoning process of the FUDASAT inference engine. In comparison with the author’s previous works, the paper introduces new mechanisms: hypergraph analysis, multiple variable assignments and search space pruning algorithms. The approach considers only 3-SAT class functions, although a generalization of the method is discussed as well. The presented approach has been tested on various benchmarks and compared with the original pure FUDASAT algorithm as well as with other algorithms known from the literature. Finally, the benefits of the proposed SAT solving technique are summarized.

An Interactive Visualization of Genetic Algorithm on 2-D Graph

2012 ◽  
Vol 4 (1) ◽  
pp. 34-54 ◽  
Author(s):  
Humera Farooq ◽  
Nordin Zakaria ◽  
Muhammad Tariq Siddique
Keyword(s):  
Genetic Algorithm ◽  
Growth Process ◽  
Search Space ◽  
Human Intervention ◽  
Branching Structures ◽  
Multidimensional Search ◽  
L System ◽  
User Fatigue ◽  
Search Data ◽  
Selection Of

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithm (GA). The authors propose a novel way for representation of multidimensional search space of the GA using 2-D graph. This is carried out based on the gene values of the current generation, and human intervention is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and improve the searching process of the GA with human’s intention in different generations. Besides the selection of best individual or parents for the next generation, interference of human is required to propose a new individual in the search space. Active human intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).

Effective Search-Space Pruning for Solvers of String Equations, Regular Expressions and Length Constraints

2015 ◽  
pp. 235-254 ◽  
Author(s):  
Yunhui Zheng ◽  
Vijay Ganesh ◽  
Sanu Subramanian ◽  
Omer Tripp ◽  
Julian Dolby ◽  
...  
Keyword(s):  
Search Space ◽  
Regular Expressions ◽  
Search Space Pruning

scholarly journals Mobile robot path planning using an improved ant colony optimization

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877467 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Search Space ◽  
Step Length ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Feedback Information ◽  
Intelligent Optimization Algorithm ◽  
Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

scholarly journals Random search with k -prototypes algorithm for clustering mixed datasets

2011 ◽  
Vol 467 (2132) ◽  
pp. 2387-2403 ◽  
Author(s):  
Duc-Truong Pham ◽  
Maria M. Suarez-Alvarez ◽  
Yuriy I. Prostov
Keyword(s):  
Optimization Algorithm ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
The Other ◽  
Bees Algorithm ◽  
Neighbourhood Search ◽  
Promising Solution ◽  
Number Of Iterations ◽  
Selection Of

A new algorithm to cluster datasets with mixed numerical and categorical values is presented. The algorithm, called RANKPRO (random search with k -prototypes algorithm), combines the advantages of a recently introduced population-based optimization algorithm called the bees algorithm (BA) and k -prototypes algorithm. The BA works with elite and good solutions, and continues to look for other possible extrema solutions keeping the number of testing points constant. However, the improvement of promising solutions by the BA may be time-consuming because it is based on random neighbourhood search. On the other hand, an application of the k -prototypes algorithm to a promising solution may be very effective because it improves the solution at each iteration. The RANKPRO algorithm balances two objectives: it explores the search space effectively owing to random selection of new solutions, and improves promising solutions fast owing to employment of the k -prototypes algorithm. The efficiency of the new algorithm is demonstrated by clustering several datasets. It is shown that in the majority of the considered datasets when the average number of iterations that the k -prototypes algorithm needs to converge is over 10, the RANKPRO algorithm is more efficient than the k -prototypes algorithm.

A method of search space pruning based on search state dominance

1994 ◽  
Vol 25 (4) ◽  
pp. 1-12
Author(s):  
Takayuki Fujino ◽  
Hideo Fujiwara
Keyword(s):  
Search Space ◽  
Search Space Pruning
Sign in / Sign up

Export Citation Format

Share Document