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Sungjin Park and Sangkyun Kim
Clustering is widely used in client-facing businesses to categorize their customer base and deliver personalized services. This study proposes an algorithm to stochastically search for an optimum solution based on the outcomes of a data clustering proces...
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Stanislav Marochok and Pavol Zajac
Cryptographic S-boxes are vectorial Boolean functions that must fulfill strict criteria to provide security for cryptographic algorithms. There are several existing methods for generating strong cryptographic S-boxes, including stochastic search algorith...
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Nima Khodadadi, Ehsan Harati, Francisco De Caso and Antonio Nanni
This article highlights the absence of published paradigms hybridized by The Cuckoo Search (CS) and Stochastic Paint Optimizer (SPO) for optimizing truss structures using composite materials under natural frequency constraints. The article proposes a nov...
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Dávid Huri and Tamás Mankovits
In rubber bumper design, the most important mechanical property of the product is the force?displacement curve under compression and its fulfillment requires an iterative design method. Design engineers can handle this task with the modification of the p...
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Okan Asik, Fatma Basak Aydemir and Hüseyin Levent Akin
The number of agents exponentially increases the complexity of a cooperative multi-agent planning problem. Decoupled planning is one of the viable approaches to reduce this complexity. By integrating decoupled planning with Monte Carlo Tree Search, we pr...
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Andrei V. Panteleev and Anna A. Kolessa
A new hybrid metaheuristic method for optimizing the objective function on a parallelepiped set of admissible solutions is proposed. It mimics the behavior of a school of river perch when looking for food. The algorithm uses the ideas of several methods:...
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Mehrdad Amirghasemi
This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a...
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Qibing Jin, Nan Lin and Yuming Zhang
K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colon...
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Ding-Peng Liu, Tsung-Yueh Lin and Hsin-Haou Huang
When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). M...
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Babak Salamat and Andrea M. Tonello
The aim of this paper is to provide a realistic stochastic trajectory generation method for unmanned aerial vehicles that offers a tool for the emulation of trajectories in typical flight scenarios. Three scenarios are defined in this paper. The trajecto...
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Babak Salamat and Andrea M. Tonello
The aim of this paper is to provide a realistic stochastic trajectory generation method for unmanned aerial vehicles that offers a tool for the emulation of trajectories in typical flight scenarios. Three scenarios are defined in this paper. The trajecto...
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Lulu Cheng, Ning Zhao, Kan Wu and Zhibin Chen
Autonomous mobile robots (AMRs) play a crucial role in transportation and service tasks at hospitals, contributing to enhanced efficiency and meeting medical demands. This paper investigates the optimization problem of scheduling strategies for AMRs at s...
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Abdelhak Etchiali, Fethallah Hadjila and Amina Bekkouche
Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user?s request is known t...
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Roberto Rosario Corsini, Antonio Costa, Sergio Fichera and Vincenzo Parrinello
This research deals with the same-day chemotherapy outpatient scheduling problem that is recognized as a leading strategy to pursue the objective of reducing patient waiting time. Inspired by a real-world context and different from the other studies, we ...
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Rafael D. Tordecilla, Pedro J. Copado-Méndez, Javier Panadero, Carlos L. Quintero-Araujo, Jairo R. Montoya-Torres and Angel A. Juan
The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be so...
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Vincenzo Cutello, Georgia Fargetta, Mario Pavone and Rocco A. Scollo
Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interaction...
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Matheus H. M. Pericini, Lucas G. M. Leite, Francisco H. De Carvalho-Junior, Javam C. Machado and Cenez A. Rezende
MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the ...
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Matheus H. M. Pericini, Lucas G. M. Leite, Francisco H. De Carvalho-Junior, Javam C. Machado and Cenez A. Rezende
MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the ...
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Zhu Wang, Junfeng Cheng and Hongtao Hu
Port operations have been suffering from hybrid uncertainty, leading to various disruptions in efficiency and tenacity. However, these essential uncertain factors are often considered separately in literature during berth and quay crane assignments, lead...
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