scholarly journals Balancing Exploration-Exploitation in the Set Covering Problem Resolution with a Self-adaptive Intelligent Water Drops Algorithm

2020 ◽  
Vol 6 (1) ◽  
pp. 134-145
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Gino Astorga ◽  
Jos´e Lemus-Romani ◽  
Sanjay Misra ◽  
...  
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Problem Resolution ◽  
Intelligent Water Drops Algorithm ◽  
Water Drops ◽  
Intelligent Water Drops ◽  
Exploration Exploitation ◽  
Self Adaptive

A self-adaptive biogeography-based algorithm to solve the set covering problem

2019 ◽  
Vol 53 (3) ◽  
pp. 1033-1059
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Rodrigo Olivares ◽  
Luis Riquelme ◽  
Gino Astorga ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Industrial Applications ◽  
Original Version ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Control Parameters ◽  
Production Crew ◽  
Classical Optimization ◽  
Self Adaptive

Using the approximate algorithms, we are faced with the problem of determining the appropriate values of their input parameters, which is always a complex task and is considered an optimization problem. In this context, incorporating online control parameters is a very interesting issue. The aim is to vary the parameters during the run so that the studied algorithm can provide the best convergence rate and, thus, achieve the best performance. In this paper, we compare the performance of a self-adaptive approach for the biogeography-based optimization algorithm using the mutation rate parameter with respect to its original version and other heuristics. This work proposes altering some parameters of the metaheuristic according to its exhibited efficiency. To test this approach, we solve the set covering problem, which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, databases, among several others. We illustrate encouraging experimental results, where the proposed approach is capable of reaching various global optimums for a well-known instance set taken from the Beasleys OR-Library, and sometimes, it improves the results obtained by the original version of the algorithm.

scholarly journals Hybrid Intelligent Water Drops Algorithm for Examination Timetabling Problem

2021 ◽  
Author(s):  
Bashar A. Aldeeb ◽  
Mohammed Azmi Al-Betar ◽  
Norita Md Norwawi ◽  
Khalid A. Alissa ◽  
Mutasem K. Alsmadi ◽  
...  
Keyword(s):  
Examination Timetabling ◽  
Timetabling Problem ◽  
Intelligent Water Drops Algorithm ◽  
Water Drops ◽  
Intelligent Water Drops

scholarly journals Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1839
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
José Lemus-Romani ◽  
Marcelo Becerra-Rozas ◽  
José M. Lanza-Gutiérrez ◽  
...  
Keyword(s):  
Combinatorial Problems ◽  
Set Covering ◽  
Covering Problem ◽  
Integration Framework ◽  
Q Learning ◽  
Whale Optimization ◽  
Sine Cosine Algorithm ◽  
Exploration Exploitation ◽  
Selection Of

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

scholarly journals Multi-objective database queries in combined knapsack and set covering problem domains

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sean A. Mochocki ◽  
Gary B. Lamont ◽  
Robert C. Leishman ◽  
Kyle J. Kauffman
Keyword(s):  
Relational Database ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Database Queries ◽  
Multi Objective ◽  
Data Representations ◽  
Np Hard Problem ◽  
Institute Of Technology ◽  
The One

AbstractDatabase queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.

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