Multi-level Cooperative Search: A New Paradigm for Combinatorial Optimization and an Application to Graph Partitioning

1999 ◽  
pp. 533-542 ◽  
Author(s):  
Michel Toulouse ◽  
Krishnaiyan Thulasiraman ◽  
Fred Glover
Keyword(s):  
Combinatorial Optimization ◽  
Graph Partitioning ◽  
New Paradigm ◽  
Cooperative Search ◽  
Multi Level

Topology-Aware Load-Balance Schemes for Heterogeneous Graph Processing

2018 ◽  
pp. 113-143
Keyword(s):  
Parallel Processing ◽  
Complex Networks ◽  
Configuration Space ◽  
Real World ◽  
Graph Partitioning ◽  
Load Balance ◽  
Heterogeneous Computing ◽  
Graph Processing ◽  
Multi Level ◽  
Computing Approach

Inspired by the insights presented in Chapters 2, 3, and 4, in this chapter the authors present the KCMAX (K-Core MAX) and the KCML (K-Core Multi-Level) frameworks: novel k-core-based graph partitioning approaches that produce unbalanced partitions of complex networks that are suitable for heterogeneous parallel processing. Then they use KCMAX and KCML to explore the configuration space for accelerating BFSs on large complex networks in the context of TOTEM, a BSP heterogeneous GPU + CPU HPC platform. They study the feasibility of the heterogeneous computing approach by systematically studying different graph partitioning strategies, including the KCMAX and KCML algorithms, while processing synthetic and real-world complex networks.

An integrative cooperative search framework for multi-decision-attribute combinatorial optimization: Application to the MDPVRP

2015 ◽  
Vol 246 (2) ◽  
pp. 400-412 ◽  
Author(s):  
Nadia Lahrichi ◽  
Teodor Gabriel Crainic ◽  
Michel Gendreau ◽  
Walter Rei ◽  
Gloria Cerasela Crişan ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Cooperative Search

Improving quality of graph partitioning using multi-level optimization

2015 ◽  
Vol 41 (5) ◽  
pp. 302-306 ◽  
Author(s):  
R. K. Pastukhov ◽  
A. V. Korshunov ◽  
D. Yu. Turdakov ◽  
S. D. Kuznetsov
Keyword(s):  
Graph Partitioning ◽  
Multi Level

scholarly journals Graph Coloring: A Novel Heuristic Based on Trailing Path; Properties, Perspective and Applications in Structured Networks

2018 ◽  
Author(s):  
Abhirup Bandyopadhyay ◽  
Amit Kumar Dhar ◽  
Sankar Basu
Keyword(s):  
Approximate Solution ◽  
Combinatorial Optimization ◽  
Chromatic Number ◽  
Polynomial Time ◽  
Graph Coloring ◽  
Graph Partitioning ◽  
Evolutionary Biology ◽  
Optimization Algorithms ◽  
Current Approach ◽  
Vertex Coloring

Graph coloring is a manifestation of graph partitioning, wherein, a graph is partitioned based on the adjacency of its elements. Partitioning serves potentially as a compartmentalization for any structural problem. Vertex coloring is the heart of the problem which is to find the chromatic number of a graph. The fact that there is no general efficient solution to this problem that may work unequivocally for all graphs opens up the realistic scope for combinatorial optimization algorithms to be invoked. The algorithmic complexity of graph coloring is non-deterministic in polynomial time (NP) and hard. To the best of our knowledge, there is no algorithm as yet that procures an exact solution of the chromatic number comprehensively for any and all graphs within the polynomial (P) time domain. However, several heuristics as well as some approximation algorithms have been attempted to obtain an approximate solution for the same. Here, we present a novel heuristic, namely, the 'trailing path', which returns an approximate solution of the chromatic number within polynomial time, and, with a better accuracy than most existing algorithms. The ‘trailing path’ algorithm is effectively a subtle combination of the search patterns of two existing heuristics (DSATUR and Largest First), and, operates along a trailing path of consecutively connected nodes (and thereby effectively maps to the problem of finding spanning tree(s) of the graph) during the entire course of coloring, where essentially lies both the novelty and the apt of the current approach. The study also suggests that the judicious implementation of randomness is one of the keys towards rendering an improved accuracy in such combinatorial optimization algorithms. Apart from the algorithmic attributes, essential properties of graph partitioning in random and different structured networks have also been surveyed, followed by a comparative study. The study reveals the remarkable stability and absorptive property of chromatic number across a wide array of graphs. Finally, a case study is presented to demonstrate the potential use of graph coloring in protein design – yet another hard problem in structural and evolutionary biology.

Solution integration in combinatorial optimization with applications to cooperative search and rich vehicle routing

2015 ◽  
Vol 21 (5) ◽  
pp. 663-685 ◽  
Author(s):  
Nizar El Hachemi ◽  
Teodor Gabriel Crainic ◽  
Nadia Lahrichi ◽  
Walter Rei ◽  
Thibaut Vidal
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Cooperative Search

scholarly journals From inert matter to the global society life as multi-level networks of processes

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190329 ◽  
Author(s):  
David Chavalarias
Keyword(s):  
Complex Systems ◽  
Biological Networks ◽  
Theoretical Biology ◽  
Life Forms ◽  
New Paradigm ◽  
Philosophical Foundations ◽  
Process Networks ◽  
The Face ◽  
Multi Level ◽  
To Come

A few billion years have passed since the first life forms appeared. Since then, life has continued to forge complex associations between the different emergent levels of interconnection it forms. The advances of recent decades in molecular chemistry and theoretical biology, which have embraced complex systems approaches, now make it possible to conceptualize the questions of the origins of life and its increasing complexity from three complementary notions of closure: processes closure, autocatalytic closure and constraints closure. Developed in the wake of the second-order cybernetics, this triple closure approach, that relies on graph theory and complex networks science, sketch a paradigm where it is possible to go up the physical levels of organization of matter, from physics to biology and society, without resorting to strong reductionism. The phenomenon of life is conceived as the contingent complexification of the organization of matter, until the emergence of life forms, defined as a network of auto-catalytic process networks, organized in a multi-level manner. This approach of living systems, initiated by Maturana & Varela and Kauffman, inevitably leads to a reflection on the nature of cognition; and in the face of the deep changes that affected humanity as a complex systems, on the nature of cultural evolution. Faced with the major challenges that humanity will have to address in the decades to come, this new paradigm invites us to change our conception of causality by shifting our attention from state change to process change and to abandon a widespread notion of 'local' causality in favour of complex systems thinking. It also highlights the importance of a better understanding of the influence of social networks, recommendation systems and artificial intelligence on our future collective dynamics and social cognition processes. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

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