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Intro; Preface; Contents; A Parallel Algorithm for the Constrained Shortest Path Problem on Lattice Graphs; 1 Definition of the Problem; 2 Related Problems in the Literature; 3 Example; 4 Pseudo-code of the Algorithm; 5 Memory Management and Initialization; 5.1 Labels for Vertices and Edges; 5.2 Termination; 5.3 Sequences Accessible to All Processing Elements; 6 Graph Update; 6.1 Step 1: Initial Triggering of Vertices; 6.2 Step 2: Analyzing Triggered Vertices; 6.3 Step 3: Gathering Input from Phantoms; 6.4 Step 4: Triggering Edges; 6.5 Step 5: Treatment of Triggered Edges.
6.6 Step 6: Checking Terminal Conditions6.7 Step 7: Final Treatment of Phantoms; 6.8 Step 8: Final Treatment of Vertices; 6.9 Step 9: Final Treatment of Active Edges; 7 Large Sets of Active Vertices; 8 Performance Analysis; 9 Conclusion; References; Gathering a Swarm of Robots Through Shortest Paths; 1 Introduction; 2 Gathering in Different Environments; 3 Optimization Problems for Robot-Based Computing Systems; 4 Optimal Gathering for gmp+; 4.1 Configuration View; 4.2 Configuration Automorphisms and Symmetries; 4.3 Ungatherability Results; 4.4 Weber Points for gmp; 4.5 The Algorithm.
5 Optimal Gathering for GG+5.1 Configuration Automorphisms, Symmetries and Ungatherabilty Results; 5.2 Weber Points for GG; 5.3 Optimal Gathering on Trees; 5.4 Optimal Gathering on Rings; 5.5 Optimal Gathering on Infinite Grids; 6 Conclusion; References; The MinSum-MinHop and the MaxMin-MinHop Bicriteria Path Problems; 1 Introduction; 2 Preliminaries; 3 MinHop-MinSum Path Problem; 4 MinHop-MaxMin Path Problem; 5 Computational Results; 5.1 MinHop-MinSum Path Problem; 5.2 MinHop-MaxMin Path Problem; 6 Conclusions; References.
Distance-Vector Algorithms for Distributed Shortest Paths Computation in Dynamic Networks1 Introduction; 2 Background; 2.1 Asynchronous System; 2.2 Graph Notation; 2.3 Dynamic Networks; 2.4 Complexity Measures; 2.5 Distance-Vector Algorithms; 3 Distributed Bellmann-Ford; 4 Diffuse Update Algorithm; 4.1 Data Structures; 4.2 Algorithm; 4.3 Example of Execution; 5 Loop Free Routing; 5.1 Data Structures; 5.2 Algorithm; 5.3 Example of Execution; 6 Distributed Computation Pruning; 6.1 Power-Law Networks; 6.2 The Technique; 6.3 Data Structures; 6.4 Description; 6.5 Combining DCP with DUAL.
6.6 Combining DCP with LFR6.7 Practical Effectiveness of DCP; 7 Conclusions; References; Influenza Virus Algorithm for Multiobjective Energy Reduction Open Vehicle Routing Problem; 1 Introduction; 2 Multiobjective Energy Reduction Open Vehicle Routing Problem; 3 Parallel Multi-Start Multiobjective Influenza Virus Algorithm (PMS-MOIVA); 3.1 Basic Parts of the Algorithm; 3.2 Multiobjective Influenza Virus Algorithm; 4 Computational Results; 5 Conclusions and Future Research; References; Practical Algorithms for the All-Pairs Shortest Path Problem; 1 Introduction; 2 The Hourglass Algorithm.
6.6 Step 6: Checking Terminal Conditions6.7 Step 7: Final Treatment of Phantoms; 6.8 Step 8: Final Treatment of Vertices; 6.9 Step 9: Final Treatment of Active Edges; 7 Large Sets of Active Vertices; 8 Performance Analysis; 9 Conclusion; References; Gathering a Swarm of Robots Through Shortest Paths; 1 Introduction; 2 Gathering in Different Environments; 3 Optimization Problems for Robot-Based Computing Systems; 4 Optimal Gathering for gmp+; 4.1 Configuration View; 4.2 Configuration Automorphisms and Symmetries; 4.3 Ungatherability Results; 4.4 Weber Points for gmp; 4.5 The Algorithm.
5 Optimal Gathering for GG+5.1 Configuration Automorphisms, Symmetries and Ungatherabilty Results; 5.2 Weber Points for GG; 5.3 Optimal Gathering on Trees; 5.4 Optimal Gathering on Rings; 5.5 Optimal Gathering on Infinite Grids; 6 Conclusion; References; The MinSum-MinHop and the MaxMin-MinHop Bicriteria Path Problems; 1 Introduction; 2 Preliminaries; 3 MinHop-MinSum Path Problem; 4 MinHop-MaxMin Path Problem; 5 Computational Results; 5.1 MinHop-MinSum Path Problem; 5.2 MinHop-MaxMin Path Problem; 6 Conclusions; References.
Distance-Vector Algorithms for Distributed Shortest Paths Computation in Dynamic Networks1 Introduction; 2 Background; 2.1 Asynchronous System; 2.2 Graph Notation; 2.3 Dynamic Networks; 2.4 Complexity Measures; 2.5 Distance-Vector Algorithms; 3 Distributed Bellmann-Ford; 4 Diffuse Update Algorithm; 4.1 Data Structures; 4.2 Algorithm; 4.3 Example of Execution; 5 Loop Free Routing; 5.1 Data Structures; 5.2 Algorithm; 5.3 Example of Execution; 6 Distributed Computation Pruning; 6.1 Power-Law Networks; 6.2 The Technique; 6.3 Data Structures; 6.4 Description; 6.5 Combining DCP with DUAL.
6.6 Combining DCP with LFR6.7 Practical Effectiveness of DCP; 7 Conclusions; References; Influenza Virus Algorithm for Multiobjective Energy Reduction Open Vehicle Routing Problem; 1 Introduction; 2 Multiobjective Energy Reduction Open Vehicle Routing Problem; 3 Parallel Multi-Start Multiobjective Influenza Virus Algorithm (PMS-MOIVA); 3.1 Basic Parts of the Algorithm; 3.2 Multiobjective Influenza Virus Algorithm; 4 Computational Results; 5 Conclusions and Future Research; References; Practical Algorithms for the All-Pairs Shortest Path Problem; 1 Introduction; 2 The Hourglass Algorithm.