A mathematical model and a Clustering Search metaheuristic for planning the helicopter transportation of employees to the production platforms of oil and gas

Rosa, Rodrigo de Alvarenga; Machado, André Manhães; Ribeiro, Glaydston Mattos; Mauri, Geraldo Regis

doi:10.1016/j.cie.2016.09.006

Cited by 27 publications

(11 citation statements)

References 16 publications

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“…There are two work-groups: machine learning algorithms to enhance metaheuristics and metaheuristics to improve machine learning techniques. For the first group, is it possible to find (i) metaheuristics for improving clustering methods using the artificial bee colony algorithm [42], local search [43], particle swarm optimization [44] and ensemble-based metaheuristics [45]; (ii) metaheuristics to efficiently address the feature selection topic [46]; and (iii) metaheuristics for improving classification algorithms [47][48][49].…”

Section: Related Workmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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Section: Related Workmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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“…In [52] an algorithm was proposed that uses a bee colony to solve a problem of grouping energy efficiency data obtained from a wireless sensor network. In [53], a metaheuristic was used to find centroids in planning the transportation of employees from an oil platform via helicopters. In the case of neural networks, metaheuristics have contributed to different types of integration.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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“…A bee colony metaheuristic was used for energy efficient clustering in wireless sensor networks [51]. In [52], a clustering search metaheuristic was applied for the capacitated helicopter routing problem. In [53], a hybrid-encoding scheme was used to find the optimal number of hidden neurons and connection weights in neutral networks.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

2020

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The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

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A mathematical model and a Clustering Search metaheuristic for planning the helicopter transportation of employees to the production platforms of oil and gas

Cited by 27 publications

References 16 publications

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

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