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Metaheuristics : 15th International Conference, MIC 2024, Lorient, France, June 4-7, 2024, Proceedings, Part I
Metaheuristics : 15th International Conference, MIC 2024, Lorient, France, June 4-7, 2024, Proceedings, Part I
Autore Sevaux Marc
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2024
Descrizione fisica 1 online resource (404 pages)
Altri autori (Persone) OlteanuAlexandru-Liviu
PardoEduardo G
SifalerasAngelo
MakboulSalma
Collana Lecture Notes in Computer Science Series
ISBN 9783031629129
9783031629112
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Advances in Combinatorial Optimization -- Breakout Local Search for Heaviest Subgraph Problem -- 1 Introduction -- 2 Breakout Local Search for HSP -- 2.1 General Framework -- 2.2 Initial Solution -- 2.3 Local Search -- 2.4 Adaptive Perturbation -- 3 Experimental Results -- 3.1 Test Instances -- 3.2 Results -- 4 Conclusion -- References -- A Biased Random Key Genetic Algorithm for Solving the -Neighbor p-Center Problem -- 1 Introduction -- 2 Biased Random Key Genetic Algorithm -- 2.1 Decoder -- 2.2 Local Improvement -- 3 Experiments and Results -- 4 Conclusions and Future Work -- References -- A Continuous-GRASP Random-Key Optimizer -- 1 Introduction -- 2 Random-Key Optimizer -- 3 Random-Key GRASP -- 3.1 GRASP -- 3.2 Continuous GRASP -- 3.3 Random-Key-GRASP -- 4 Experimental Results -- References -- Adaptive Ant Colony Optimization Using Node Clustering with Simulated Annealing -- 1 Introduction -- 2 Adaptive Ant Colony Optimization -- 3 Simulated Annealing -- 4 Numerical Experiment -- 5 Conclusion -- References -- Job-Shop Scheduling with Robot Synchronization for Transport Operations -- 1 Introduction -- 2 Problem Description -- 3 Linear Formulation of the Problem -- 3.1 Data -- 3.2 Decision Variables -- 3.3 Objective Function -- 3.4 Constraints -- 4 Metaheuristic Based Resolution -- 4.1 Solution Modeling Based on Disjunctive Graph -- 4.2 Indirect Representation of a Solution Using Bierwith's Vector -- 4.3 Local Search -- 4.4 Metaheuristic -- 5 Numerical Experiments -- 6 Conclusion -- References -- AI and Metaheuristics for Routing -- SIRO: A Deep Learning-Based Next-Generation Optimizer for Solving Global Optimization Problems -- 1 Introduction -- 2 Model Description -- 2.1 SIRO Algorithm Modelling -- 2.2 SIRO Model.
2.3 Basic and Neural Network-Based Initialization Methods -- 2.4 SIRO Neural Network-Based Parameter Selection -- 2.5 SIRO Algorithms and Computational Complexity -- 3 System and Parameter Configuration -- 3.1 Results and Discussion -- 3.2 Analysis of Statistical Results -- 4 Conclusion and Future Work -- References -- Investigation of the Benefit of Extracting Patterns from Local Optima to Solve a Bi-objective VRPTW -- 1 Introduction -- 2 Multi-objective Optimization -- 3 Learning and Multi-objective Optimization -- 4 Hybridization Between Learning and MOEA/D -- 4.1 MOEA/D -- 4.2 Learning Within A and Variants -- 5 Problem and Related Knowledge -- 5.1 Vehicle Routing Problems with Time Windows (VRPTW) -- 5.2 Pattern Injection Local Search -- 6 Experimental Setup -- 6.1 The Solomon's Benchmark -- 6.2 Setup and Tuning -- 7 Experimental Design -- 8 Experimental Results -- 9 Conclusion -- References -- A Memetic Algorithm for Large-Scale Real-World Vehicle Routing Problems with Simultaneous Pickup and Delivery with Time Windows -- 1 Introduction -- 2 Related Works -- 3 VRPSPDTW Problem Formulation -- 4 Memetic Algorithm for the VRPSPDTW -- 4.1 Solution (Chromosome) Representation and Initialization -- 4.2 Crossover -- 4.3 Local Search -- 5 Computational Study and Experimental Analysis -- 5.1 Problem Instances from JD Logistics -- 5.2 Experimental Setup -- 5.3 Comparing BCRCD with Other Crossovers -- 5.4 Comparing MA-BCRCD with MATE with and Without Crossover -- 6 Conclusion -- References -- Tabu Search for Solving Covering Salesman Problem with Nodes and Segments -- 1 Introduction -- 2 Covering Salesman Problem with Nodes and Segments -- 3 Proposed Method -- 3.1 Local Search Method -- 3.2 Tabu Search -- 4 Simulations and Results -- 5 Conclusion -- References -- GRASP with Path Relinking.
VNS with Path Relinking for the Profitable Close-Enough Arc Routing Problem -- 1 Introduction -- 2 Previous GRASP Approaches -- 3 A New Heuristic Algorithm Based on VNS -- 3.1 The Path Relinking Post-processing -- 4 Computational Experiments and Conclusions -- References -- Meta-Heuristics for Preference Learning -- A Simulated Annealing Algorithm to Learn an RMP Preference Model -- 1 Introduction -- 2 Ranking Based on Multiple Reference Profiles (RMP) -- 3 A Simulated Annealing Algorithm to Learn RMP/SRMP Models -- 4 Numerical Analysis -- 5 Conclusion and Future Work -- References -- New VRP and Extensions -- Iterative Heuristic over Periods for the Inventory Routing Problem -- 1 Introduction -- 2 The Inventory Routing Problem -- 3 The Iterative Heuristic over Periods -- 4 Computational Experiments -- 4.1 Instances -- 4.2 Results -- 5 Conclusion -- References -- Combining Heuristics and Constraint Programming for the Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones -- 1 Introduction -- 2 Problem Description -- 3 Constraint Programming Models -- 4 Experimental Results -- References -- Operations Research for Health Care -- A Re-optimization Heuristic for a Dial-a-Ride Problem in the Transportation of Patients -- 1 Introduction -- 2 Literature Review -- 3 Problem Description -- 4 Re-optimization Heuristic -- 5 Numerical Experiments -- 6 Conclusions and Future Works -- References -- Solving the Integrated Patient-to-Room and Nurse-to-Patient Assignment by Simulated Annealing -- 1 Introduction -- 2 Search Method -- 3 Preliminary Results -- 4 Conclusions -- References -- Enhancing Real-World Applicability in Home Healthcare: A Metaheuristic Approach for Advanced Routing and Scheduling -- 1 Introduction -- 2 Problem Formulation -- 2.1 Basic Formulation -- 2.2 Extended Formulation -- 3 Solution Technique -- 4 Experimental Results.
5 Conclusions and Future Work -- References -- Solving the Two-Stage Robust Elective Patient Surgery Planning Under Uncertainties with Intensive Care Unit Beds Availability -- 1 Introduction and Related Works -- 2 Solving the Two-Stage Robust Elective Surgery Planning -- 3 Computational Experience -- 4 Conclusion and Perspectives -- References -- Extracting White-Box Knowledge from Word Embedding: Modeling as an Optimization Problem -- 1 Introduction -- 2 Background on Word Embedding -- 3 A Combinatorial Optimization Model to Extract White-Box Knowledge from Word Embedding -- 3.1 Solution Modeling -- 3.2 Resolution with a Local Search -- 4 Experiments and Results -- 5 Conclusion and Further Research -- References -- A Hybrid Biased-Randomized Heuristic for a Home Care Problem with Team Scheme Selection -- 1 Introduction -- 2 Solution Methodology -- 3 Results -- 4 Conclusions and Future Work -- References -- Optimization for Forecasting -- Extended Set Covering for Time Series Segmentation -- 1 Introduction -- 2 An Extended Set Covering Model -- 3 Computational Experience -- 4 Conclusions -- References -- Quantum Meta-Heuristic for Operations Research -- Indirect Flow-Shop Coding Using Rank: Application to Indirect QAOA -- 1 Introduction -- 2 Indirect Flow-Shop Coding Using Rank -- 2.1 Graph Modeling -- 2.2 Quasi-Direct Representation -- 2.3 Indirect Representation of Solutions -- 2.4 Resolution of the Carlier 7 Jobs 7 Machines Instance -- 2.5 Resolution of the Carlier 8 Jobs 8 Machines Instance -- 2.6 Resolution of the Carlier 8 Jobs 9 Machines Instance -- 3 Conclusion -- References -- Utilizing Graph Sparsification for Pre-processing in Max Cut QUBO Solver -- 1 Introduction -- 1.1 Our Contributions -- 1.2 Related Works -- 2 Preliminaries -- 2.1 Max Cut Problem -- 2.2 QUBO Formulation for the Max Cut Problem.
2.3 Graph Sparsification by Effective Resistances ch22spielman2008graph -- 3 Proposed Method -- 4 Experimental Results -- 4.1 Gap in Solutions Due to Graph Sparsification -- 4.2 Computation Time in Classical Solver -- 4.3 Experiments on Quantum-Inspired Solvers -- 4.4 Discussions on Results on Classical and Quantum-Inspired Solvers -- 5 Conclusion and Future Works -- References -- Addressing Machine Unavailability in Job Shop Scheduling: A Quantum Computing Approach -- 1 Introduction -- 2 Problem Definition -- 3 Related Works -- 4 QUBO Formulation -- 5 Non Fixed Resource Availability Constraints -- 6 Computational Experiments -- 7 Discussion -- References -- Solving Edge-Weighted Maximum Clique Problem with DCA Warm-Start Quantum Approximate Optimization Algorithm -- 1 Introduction -- 2 Introduction to QAOA and Warm-Start Method -- 2.1 Introduction to QAOA -- 2.2 Introduction to the Warm-Start Method in Quantum Optimization -- 3 Warm Start Method for QAOA with Non-convex Relaxed Quadratic Binary Optimization Problem -- 3.1 General Warm-Start Method with DCA -- 3.2 Quadratic Formulation of Edge-Weighted Max Clique Problem -- 4 Numerical Simulation -- 5 Conclusion and Feature Work -- References -- Comparing Integer Encodings in QUBO for Quantum and Digital Annealing: The Travelling Salesman Problem -- 1 Introduction -- 2 Quantum Annealing and QUBO -- 3 The Travelling Salesman Problem in QUBO -- 4 Experimental Results -- 5 Conclusion -- References -- Solving Quadratic Knapsack Problem with Biased Quantum State Optimization Algorithm -- 1 Introduction -- 2 Preliminary -- 2.1 Introduction to QAOA -- 2.2 Introduction to Quadratic Knapsack Problem and Its Reformulations for QAOA -- 3 Introduction to Biased Quantum State for Constrained Quadratic Binary Optimization -- 4 Numerical Simulation -- 5 Conclusion and Feature Work -- References.
Quantum Optimization Approach for Feature Selection in Machine Learning.
Record Nr. UNINA-9910865238203321
Sevaux Marc  
Cham : , : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Metaheuristics : 15th International Conference, MIC 2024, Lorient, France, June 4-7, 2024, Proceedings, Part II
Metaheuristics : 15th International Conference, MIC 2024, Lorient, France, June 4-7, 2024, Proceedings, Part II
Autore Sevaux Marc
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2024
Descrizione fisica 1 online resource (414 pages)
Altri autori (Persone) OlteanuAlexandru-Liviu
PardoEduardo G
SifalerasAngelo
MakboulSalma
Collana Lecture Notes in Computer Science Series
ISBN 9783031629228
9783031629211
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- General Papers -- Learning Sparse-Lets for Interpretable Classification of Event-interval Sequences -- 1 Introduction -- 2 Related Work -- 2.1 Distance-Based Embedding -- 2.2 Feature-Based Embedding -- 3 Background -- 3.1 Event Table and E-Lets -- 4 Methodology -- 4.1 Genetic Algorithm -- 5 Experimental Evaluation -- 5.1 Hyper-parameters Tuning -- 5.2 BRKGA Settings -- 5.3 Computational Results -- 6 Conclusions -- References -- Deep Reinforcement Learning for Smart Restarts in Exploration-Only Exploitation-Only Hybrid Metaheuristics -- 1 Introduction -- 2 Background -- 2.1 The UES-CMAES Exploration-Only Exploitation-Only Hybrid -- 2.2 Hybridization of Machine Learning with Metaheuristics -- 3 Restarts as a Reinforcement Learning Problem -- 3.1 The Environment -- 3.2 The Agent -- 4 Assessing the Agent's Performance -- 4.1 On Individual Functions -- 4.2 On the Entire Benchmark -- 5 Optimization Results -- 5.1 Benchmark-Wide Performance -- 5.2 Comparison Against Other Algorithms -- 6 Conclusions and Future Work -- References -- Optimization of a Last Mile Delivery Model with a Truck and a Drone Using Mathematical Formulation and a VNS Algorithm -- 1 Introduction -- 2 Model Description -- 3 Solution Method: VNS Algorithm -- 3.1 Construction of Initial Solution -- 3.2 Neighborhood Structures -- 3.3 Local Search Strategy -- 4 VNS with Cumulative Gain Approach (G-VNS) -- 5 Numerical Experiments -- 5.1 Analysis of Neighborhood Selection Strategy -- 5.2 Performance Evaluation Using Small Instances -- 5.3 Performance Evaluation Using Large Instances -- 6 Conclusion -- References -- An Empirical Analysis of Tabu Lists -- 1 Introduction -- 2 Related Work -- 3 Tabu Search -- 4 Experimental Setup -- 4.1 Tabu Search Implementation -- 4.2 Benchmark Problem -- 4.3 Parameter Tuning.
5 Experimental Analysis -- 5.1 Overall Results -- 5.2 Anytime Behavior -- 5.3 Search Trajectory Networks -- 6 Conclusion and Future Perspectives -- References -- Strategically Influencing Seat Selection in Low-Cost Carriers: A GRASP Approach for Revenue Maximization -- 1 Introduction -- 2 Problem Description -- 3 Proposed Methodology -- 4 Computational Analysis and Results -- 4.1 Case of Study -- 4.2 Numerical Results -- 5 Conclusion -- References -- Behaviour Analysis of Trajectory and Population-Based Metaheuristics on Flexible Assembly Scheduling -- 1 Introduction -- 2 Problem Description -- 3 Related Work -- 4 Meta-heuristics Approaches -- 4.1 Encoding and Decoding -- 4.2 Initialization -- 4.3 Search Operators -- 4.4 Search Strategies -- 5 Experimental Analysis -- 5.1 Problem Sets -- 5.2 Experimental Setup -- 5.3 Results and Discussion -- 6 Conclusions and Further Work -- References -- Matheuristic Variants of DSATUR for the Vertex Coloring Problem -- 1 Introduction -- 2 Problem Statement and State of the Art -- 2.1 Definitions and Notation -- 2.2 Compact ILP Formulations -- 2.3 Standard DSATUR Algorithm -- 3 DSATUR Matheuristic Variants -- 3.1 Initialization -- 3.2 Local Optimization with Larger Neighborhoods -- 3.3 General Algorithm -- 3.4 Dual Bounds -- 4 Computational Results -- 4.1 Experimental Conditions and Methodology -- 4.2 Standard DSATUR with Varied Initialization -- 4.3 DSATUR with Larger Local Optimization -- 4.4 Dual Bounds -- 5 Conclusions and Perspectives -- References -- Combining Neighborhood Search with Path Relinking: A Statistical Evaluation of Path Relinking Mechanisms -- 1 Introduction -- 2 Proposed Metaheuristic Algorithm -- 2.1 Pool of Elite Set Solutions -- 2.2 Neighborhood Search -- 2.3 Path Relinking -- 3 Computational Experiment -- 3.1 Testing Mechanism of Path Relinking -- 3.2 Parameter Setting.
3.3 Computation with XML Instances -- 4 Conclusion -- A Detailed Computational Results -- References -- A General-Purpose Neural Architecture Search Algorithm for Building Deep Neural Networks -- 1 Introduction -- 2 Methodology -- 2.1 Neural Network Modelling -- 2.2 Neural Layers Affinity Indices -- 2.3 General-Purpose Neural Architecture Search -- 3 Results -- 4 Conclusions -- References -- A Dynamic Algorithm Configuration Framework Using Combinatorial Problem Features and Reinforcement Learning -- 1 Introduction -- 2 Methodology -- 2.1 Problem and Test Instances -- 2.2 Metaheuristic Algorithm -- 2.3 Dynamic Configuration Learning -- 3 Implementation and Preliminary Results -- 3.1 LON Investigation -- 3.2 Learning Outcome -- 3.3 Outlook -- References -- Large Neighborhood Search for the Capacitated P-Median Problem -- 1 Introduction -- 2 Mathematical Formulation of the Capacitated P-Median Problem -- 3 Large Neighborhood Search Framework -- 4 LNS for the Capacitated P-Median Problem -- 4.1 Initial Solution -- 4.2 Destroy Operators -- 4.3 Repair Operator -- 4.4 Algorithm Description -- 5 Evaluation -- 5.1 Data Sets -- 5.2 Parameter Configuration -- 5.3 Ablation Analysis -- 5.4 Results -- 6 Conclusions -- A Appendix -- References -- Experiences Using Julia for Implementing Multi-objective Evolutionary Algorithms -- 1 Introduction -- 2 The jMetal Framework -- 3 Component-Based Evolutionary Algorithms in Julia -- 4 Performance Evaluation -- 5 Porting jMetal Resources to MetaJul -- 6 Discussion -- 6.1 Research Questions -- 6.2 Further Remarks -- 7 Conclusions -- References -- A Matheuristic Multi-start Algorithm for a Novel Static Repositioning Problem in Public Bike-Sharing Systems -- 1 Introduction -- 2 Literature Review -- 3 Problem Description -- 4 Algorithmic Proposal -- 5 Phase I: Randomized Multi-start Algorithm.
6 Phase II: Optimal Loading Instructions -- 7 Computational Experiments -- 8 Concluding Remarks -- References -- A Disjunctive Graph Solution Representation for the Continuous and Dynamic Berth Allocation Problem -- 1 Introduction -- 2 Problem Overview -- 2.1 Problem Description -- 2.2 Assumptions -- 3 A Disjunctive Graph Representation for the Continuous and Dynamic BAP -- 3.1 A Non-oriented Disjunctive Graph Representation of the Problem -- 3.2 Solution Representation Using an Oriented Disjunctive Graph -- 4 An Iterated Local Search for the Continuous and Dynamic BAP -- 4.1 Initial Solution -- 4.2 Local Search Heuristics -- 4.3 Perturbation -- 4.4 General Framework of the ILS -- 5 Numerical Experiments -- 5.1 Instances Generator -- 5.2 Evaluation of Algorithm ILSdisj -- 6 Conclusion -- References -- Area Coverage in Heterogeneous Multistatic Sonar Networks: A Simulated Annealing Approach -- 1 Introduction -- 2 Problem Description -- 3 Simulated Annealing (SA) -- 3.1 Problem-Specific Components -- 3.2 Algorithm-Specific Components -- 4 Numerical Experiments -- 4.1 Instances -- 4.2 Results -- 4.3 Example of Solution -- 5 Conclusions and Perspectives -- References -- The Use of Metaheuristics in the Evolution of Collaborative Filtering Recommender Systems: A Review -- 1 Introduction -- 2 Background of Recommender Systems -- 3 Collaborative Filtering (CF) Recommendations -- 4 History of Advancements in CF -- 4.1 Neighborhoods and Recommendations -- 4.2 Sparsity and Accurate Neighborhoods -- 4.3 Addressing Sparsity Through Evolutionary Techniques -- 4.4 Addressing Sparsity Through Multi-objective Optimization -- 4.5 Impacts of CF Reliance on Historical Data -- 4.6 Revamping Classical Approaches with Metaheuristics -- 5 Reviews of CF -- 6 Conclusion -- References -- Modelling and Solving a Scheduling Problem with Hazardous Products Dynamic Evolution.
1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Iterated Local Search for the RCPSP-RTD -- 5 Computational Experiments -- 5.1 Data Generation -- 5.2 Results Analysis -- 6 Concluding Remarks -- References -- Fixed Set Search Matheuristic Applied to the min-Knapsack Problem with Compactness Constraints and Penalty Values -- 1 Introduction -- 2 Related Literature -- 3 Problem Definition -- 4 A Matheuristic Based on the Fixed Set Search -- 4.1 Generating the Initial Population of Solutions -- 4.2 Fixed Sets -- 4.3 The Use of the Integer Program -- 5 Computational Experiments -- 5.1 Instances -- 5.2 Results -- 6 Conclusions and Future Work -- References -- Improved Golden Sine II in Synergy with Non-monopolized Local Search Strategy -- 1 Introduction -- 2 Related Work -- 2.1 Golden Sine Algorithm II -- 2.2 Non-monopolized Search -- 3 Proposed Approach -- 4 Experimental Results -- 4.1 Friedman Rank Test -- 4.2 Dispersion of the Resulst -- 5 Conclusions and Future Work -- References -- Population of Hyperparametric Solutions for the Design of Metaheuristic Algorithms: An Empirical Analysis of Performance in Particle Swarm Optimization -- 1 Introduction -- 2 Particle Swarm Optimization -- 3 Hyper Particle Swarm Optimization -- 4 Experimental Results -- 5 Conclusions and Future Work -- References -- A GRASP Algorithm for the Meal Delivery Routing Problem -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 3.1 Users -- 3.2 Apps/Platform -- 3.3 Couriers -- 3.4 Restaurants -- 4 Methodology -- 5 Computational Experiments and Result Analysis -- 5.1 Case of Study -- 5.2 Computational Results -- 6 Conclusions -- References -- Optimization Approaches for a General Class of Single-Machine Scheduling Problems -- 1 Introduction -- 2 Optimization Approach -- 2.1 Optimization Models -- 2.2 Tabu Search -- 3 Preliminary Results -- References.
What Characteristics Define a Good Solution in Social Influence Minimization Problems?.
Record Nr. UNINA-9910865265703321
Sevaux Marc  
Cham : , : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui