Adaptive Black Hole Algorithm for Solving the Set Covering Problem

Soto, Ricardo; Crawford, Broderick; Olivares, Rodrigo; Taramasco, Carla; Figueroa, I.A.; Gómez, Álvaro; Castro, Carlos; Paredes, Fernando

doi:10.1155/2018/2183214

Cited by 24 publications

(17 citation statements)

References 65 publications

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“…However, the most widely applied metaheuristics for solving SCP are SIAs. Some examples are the artificial bee colony (ABC) algorithm [13,40,41], the ant colony optimization (ACO) algorithm [42][43][44], the firefly algorithm (FA) [45,46], the teaching-learning-based optimization (TLBO) algorithm [47,48], the electromagnetism-like (EM-like) algorithm [49,50], the shuffled frog leaping algorithm (SFLA) [51], the fruit fly optimization algorithm (FFOA) [52], the cuckoo search algorithm (CSA) [53,54], the cat swarm optimization (CSO) algorithm [55,56], the jumping particle swarm optimization (JPSO) method [57], the black hole optimization [54,58], and the monkey search algorithm [59].…”

Section: Related Workmentioning

confidence: 99%

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

Lanza-Gutiérrez

Caballé

Crawford

et al. 2020

Mathematical Problems in Engineering

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The set covering problem (SCP) is an NP-complete optimization problem, fitting with many problems in engineering. The traditional SCP formulation does not directly address both solution unsatisfiability and set redundancy aspects. As a result, the solving methods have to control these aspects to avoid getting unfeasible and nonoptimized in cost solutions. In the last years, an alternative SCP formulation was proposed, directly covering both aspects. This alternative formulation received limited attention because managing both aspects is considered straightforward at this time. This paper questions whether there is some advantage in the alternative formulation, beyond addressing the two issues. Thus, two studies based on a metaheuristic approach are proposed to identify if there is any concept in the alternative formulation, which could be considered for enhancing a solving method considering the traditional SCP formulation. As a result, the authors conclude that there are concepts from the alternative formulation, which could be applied for guiding the search process and for designing heuristic feasibilit\y operators. Thus, such concepts could be recommended for designing state-of-the-art algorithms addressing the traditional SCP formulation.

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Section: Related Workmentioning

confidence: 99%

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

Lanza-Gutiérrez

Caballé

Crawford

et al. 2020

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

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“…Another frames the transit route network design problem (TrNDP) as a MO optimization problem, and uses the Route Constructive Genetic Algorithm (RCGA) to translate it into a SCP Owais et al (2015), which is then solved using randomized priority search as defined in Lan et al (2007). Additional algorithms used to solve the SCP are the black hole algorithm Soto et al (2018); García et al (2017), the Harmony Search Algorithm Lin (2015) and the hyperedge configuration checking strategy Wang et al (2017) .…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Database Queries in Combined Knapsack and Set Covering Problem Domains

Mochocki¹,

Lamont²,

Leishman³

et al. 2020

Preprint

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Database 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 polynomialtime 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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“…To confirm our hypothesis, the e-scooter-Chargers Allocation (ESCA) Problem is formulated as a mixed-integer linear programming (MILP) model. Because of the time complexity of the MILP, we adopted two other algorithms, namely the ACA and the BHO, which has polynomial complexity [12], [13]. Consequently, we compared the solutions of the ESCA using the three approaches and recommended ACA for large instances.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Approaches to Solve E-Scooter Assignment Problem

et al. 2019

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Nowadays, rapid urbanization causes a wide-range of congestion and pollution in megacities worldwide, which bears an urgent need for micromobility solutions such as electric scooters (e-scooter). Many e-scooter firms use freelancers to charge the scooter where they compete to collect and charge the e-scooters at their homes. This competition leads the chargers to travel long distances to collect e-scooters. In this paper, we developed a mixed-integer linear programming (MILP) model for a real-world e-scooter-Chargers Allocation (ESCA) problem. The proposed model allocates the e-scooters to the chargers with an emphasis on minimizing the chargers' average travelled distance to collect the e-scooters. The MILP returns optimal solutions in most cases; however, the ESCA is identified as a generalized assignment problem which classifies as an NP-complete combinatorial optimization problem. Moreover, we modelled the charging problem as a game between two sets of disjoint players, namely e-scooters and chargers. Then we adapted the college admission algorithm (ACA) to solve the ESCA problem. For the sake of comparison, we applied the black hole optimizer (BHO) algorithm to solve this problem using small and medium cases. The experimental results show that the ACA solutions are close to the optimal solutions obtained by the MILP. Furthermore, the BHO solutions are not as good as the ACA solutions, but the ACA solution consumes more time to solve large-scale real cases. Based on the obtained results, we recommend applying the ACA1 to find the near-optimal solution for large-scale instances, as the MILP is inapplicable to find the exact solution in comparison.INDEX TERMS Micromobility modes, e-scooter-chargers allocation, mixed-integer linear programming, heuristic.

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Adaptive Black Hole Algorithm for Solving the Set Covering Problem

Cited by 24 publications

References 65 publications

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

Exploring Further Advantages in an Alternative Formulation for the Set Covering Problem

Multi-Objective Database Queries in Combined Knapsack and Set Covering Problem Domains

Heuristic Approaches to Solve E-Scooter Assignment Problem

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