Solving the non-unicost set covering problem by using cuckoo search and black hole optimization

Soto, Ricardo; Crawford, Broderick; Olivares, Rodrigo; Barraza, Jorge; Figueroa, I.A.; Johnson, Franklin; Paredes, Fernando; Olguín, Eduardo

doi:10.1007/s11047-016-9609-7

Cited by 28 publications

(21 citation statements)

References 40 publications

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“…As future works, we believe that using new approximate optimization algorithms will allow us to find better results to compare the SVM classifier performance. Moreover, we intend to incorporate an autonomous version of these algorithms so that the self-adaptive of its parameters is not complex and suited to the instance of the problem, as described in [ 9 , 11 ].…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, recently, several approaches have emerged, inspired by natural phenomena, that allow solving complex optimization and combinatorial problems in reduced time periods [ 9 – 12 ]. These techniques have been successful when the complexity of the problem is not linear, given that they do not explore the solution tree in their completeness.…”

Section: Introductionmentioning

confidence: 99%

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Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Castro-Muñoz

Olivares

Taramasco

et al. 2018

Computational Intelligence and Neuroscience

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Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Castro-Muñoz

Olivares

Taramasco

et al. 2018

Computational Intelligence and Neuroscience

Self Cite

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“…Black hole algorithm belongs to the swarm intelligence algorithm, which are inspired either by living bodies, like ants [88], bees [89], fishes [90], bats [91], krill herds [92], fireflies [93], fruit flies [94], bacteria's [95], or by other natural phenomena, like gravitation [96], big-bang [97], or intelligent water drop [98]. Black hole optimization is used in a wide range of NP-hard optimization problems, like investigating the critical slip surface of soil slope [99], solving the non-unicost set covering problem [100], optimization of consignment-store-based supply chain [101], thermodynamic optimization of a Penrose process [102], power flow optimization [103], and design of electromagnetic devices [104], but one of its most important application fields is the clustering. The black hole algorithms have six phases as follows:…”

Section: Black Hole Optimization-based Clusteringmentioning

confidence: 99%

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

et al. 2019

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In the context of Industry 4.0, the matrix production concept represents revolutionary solutions from a technological and logistics point of view. In a matrix production system, flexible, configurable production and assembly cells are arranged in a grid layout, and the in-plant supply is based on autonomous vehicles. Adaptable and flexible material handling solutions are required to perform the dynamically changing supply-demands of standardized and categorized manufacturing and assembly cells. Within the frame of this paper, the authors describe the in-plant supply process of matrix production and the optimization potential in these processes. After a systematic literature review, this paper introduces the structure of matrix production as a cyber-physical system focusing on logistics aspects. A mathematical model of this in-plant supply process is described including extended and real-time optimization from routing, assignment, and scheduling points of view. The optimization problem described in the model is an NP-hard problem. There are no known efficient analytical methods to find the best solution for this kind of problem; therefore, we use heuristics to find a suitable solution for the above-described problem. Next, a sequential black hole–floral pollination heuristic algorithm is described. The scenario analysis, which focuses on the clustering and routing aspects of supply demands in a matrix production system, validates the model and evaluates its performance to increase cost-efficiency and warrants environmental awareness of the in-plant supply in matrix production.

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“…BHO can be combined with other heuristic algorithms to improve its efficiency and convergence. The integration of the core of swarming optimization methods can increase the efficiency of preprocessing, transfer functions, and the discretization [47].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

2018

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Supply chain management applies more and more Industry 4.0 innovations to increase their availability, elasticity, sustainability, and efficiency. In interconnected logistics networks, operations are integrated from suppliers through 3rd party logistics providers to customers. There are different delivery models depending on the time and cost. In the last few years, a wide range of customers is willing to pay an extra fee for the same delivery or instant delivery. This fact led to the increased importance of the optimized design and control of first mile/last mile (FMLM) delivery solutions. Cyberphysical system-based service innovations make it possible to enhance the productivity of FMLM delivery in the big data environment. The design and operation problems can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on heuristic and metaheuristic algorithms. This research proposes an integrated supply model of FMLM delivery. After a careful literature review, this paper introduces a mathematical model to formulate the problem of realtime smart scheduling of FMLM delivery. The integrated model includes the assignment of first mile and last mile delivery tasks to the available resources and the optimization of operations costs, while constraints like capacity, time window, and availability are taken into consideration. Next, a black hole optimization-(BHO-) based algorithm dealing with a multiobjective supply chain model is presented. The sensitivity of the enhanced algorithm is tested with benchmark functions. Numerical results with different datasets demonstrate the efficiency of the proposed model and validate the usage of Industry 4.0 inventions in FMLM delivery.

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Solving the non-unicost set covering problem by using cuckoo search and black hole optimization

Cited by 28 publications

References 40 publications

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

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