Undecidable optimization problems for database logic programs

1993 ◽  
Vol 40 (3) ◽  
pp. 683-713 ◽  
Author(s):  
Haim Gaifman ◽  
Harry Mairson ◽  
Yehoshua Sagiv ◽  
Moshe Y. Vardi
Keyword(s):  
Optimization Problems ◽  
Logic Programs ◽  
Database Logic

scholarly journals Solving distributed constraint optimization problems using logic programming

2017 ◽  
Vol 17 (4) ◽  
pp. 634-683
Author(s):  
TIEP LE ◽  
TRAN CAO SON ◽  
ENRICO PONTELLI ◽  
WILLIAM YEOH
Keyword(s):  
Logic Programming ◽  
Optimization Problems ◽  
Answer Set Programming ◽  
Constraint Optimization ◽  
Logic Programs ◽  
Distributed Constraint Optimization ◽  
Multi Agent ◽  
Constraint Optimization Problems ◽  
Answer Set

AbstractThis paper explores the use ofAnswer Set Programming (ASP)in solvingDistributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) it shows how one can formulate DCOPs as logic programs; (2) it introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) it experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) it demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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