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Found 28 papers, 2 papers with code
Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers
Research focused on the conjunction between quantum computing and routing problems has been very prolific in recent years.
Multiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations
Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP.
Age-Friendly Route Planner: Calculating Comfortable Routes for Senior Citizens
The application of routing algorithms to real-world situations is a widely studied research topic.
Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing
Also, we contextualize the IOP within the complete platform in which it is embedded, describing how a user can benefit from its use.
A Quantum Computing-based System for Portfolio Optimization using Future Asset Values and Automatic Reduction of the Investment Universe
One of the problems in quantitative finance that has received the most attention is the portfolio optimization problem.
Hybrid classical-quantum computing: are we forgetting the classical part in the binomial?
The expectations arising from the latest achievements in the quantum computing field are causing that researchers coming from classical artificial intelligence to be fascinated by this new paradigm.
Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach
The Bin Packing Problem is a classic problem with wide industrial applicability.
Benchmark dataset and instance generator for Real-World Three-Dimensional Bin Packing Problems
In this article, a benchmark for real-world bin packing problems is proposed.
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
AI-based digital twins are at the leading edge of the Industry 4. 0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis.
Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers
Efficient packing of items into bins is a common daily task.
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations
Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development.
Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future
Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years.
Focusing on the Hybrid Quantum Computing -- Tabu Search Algorithm: new results on the Asymmetric Salesman Problem
Quantum Computing is an emerging paradigm which is gathering a lot of popularity in the current scientific and technological community.
Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions
In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process.
Hybrid Quantum Computing -- Tabu Search Algorithm for Partitioning Problems: preliminary study on the Traveling Salesman Problem
Quantum Computing is considered as the next frontier in computing, and it is attracting a lot of attention from the current scientific community.
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously.
A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphs
The second contribution of this paper is the application field, which is the optimal partitioning of graph instances whose connections among nodes are directed and weighted.
CURIE: A Cellular Automaton for Concept Drift Detection
Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream.
Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges
In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances.
Fairness in Bio-inspired Optimization Research: A Prescription of Methodological Guidelines for Comparing Meta-heuristics
In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task.
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems
Within this specific branch, approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has lately gained a notable momentum when tackling multiple optimization tasks.
COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking
In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand.
Diseño e implementación de una meta-heurística multi-poblacional de optimización combinatoria enfocada a la resolución de problemas de asignación de rutas a vehículos
Four well-known routing problems have been used in this experimentation, as well as two classical combinatorial optimization problems.
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration.
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization
A thorough experimentation is presented and discussed so as to assess the performance of the framework, its comparison to the traditional methodology for Transfer Learning in terms of convergence, speed and policy quality , and the intertask relationships found and exploited over the search process.
An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems
Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats.
