A tabu search heuristic for the Equitable Coloring Problem

The Equitable Coloring Problem is a variant of the Graph Coloring Problem where the sizes of two arbitrary color classes differ in at most one unit. This additional condition, called equity constraints, arises naturally in several applications. Due to the hardness of the problem, current exact algorithms can not solve large-sized instances. Such instances must be addressed only via heuristic methods. In this paper we present a tabu search heuristic for the Equitable Coloring Problem. This algorithm is an adaptation of the dynamic TabuCol version of Galinier and Hao. In order to satisfy equity constraints, new local search criteria are given. Computational experiments are carried out in order to find the best combination of parameters involved in the dynamic tenure of the heuristic. Finally, we show the good performance of our heuristic over known benchmark instances

Variations on Memetic Algorithms for Graph Coloring Problems

11 pages, 8 figures, 3 tables, 2 algorithmsInternational audienceGraph vertex coloring with a given number of colors is a well-known and much-studied NP-complete problem.The most effective methods to solve this problem are proved to be hybrid algorithms such as memetic algorithms or quantum annealing. Those hybrid algorithms use a powerful local search inside a population-based algorithm.This paper presents a new memetic algorithm based on one of the most effective algorithms: the Hybrid Evolutionary Algorithm HEA from Galinier and Hao (1999).The proposed algorithm, denoted HEAD - for HEA in Duet - works with a population of only two individuals.Moreover, a new way of managing diversity is brought by HEAD.These two main differences greatly improve the results, both in terms of solution quality and computational time.HEAD has produced several good results for the popular DIMACS benchmark graphs, such as 222-colorings for , 81-colorings for and even 47-colorings for and 82-colorings for

Computing trisections of 4-manifolds

Algorithms that decompose a manifold into simple pieces reveal the geometric and topological structure of the manifold, showing how complicated structures are constructed from simple building blocks. This note describes a way to algorithmically construct a trisection, which describes a $4$-dimensional manifold as a union of three $4$-dimensional handlebodies. The complexity of the $4$-manifold is captured in a collection of curves on a surface, which guide the gluing of the handelbodies. The algorithm begins with a description of a manifold as a union of pentachora, or $4$-dimensional simplices. It transforms this description into a trisection. This results in the first explicit complexity bounds for the trisection genus of a $4$-manifold in terms of the number of pentachora ($4$-simplices) in a triangulation.Comment: 15 pages, 9 figure

Ordering heuristics for parallel graph coloring

This paper introduces the largest-log-degree-first (LLF) and smallest-log-degree-last (SLL) ordering heuristics for paral-lel greedy graph-coloring algorithms, which are inspired by the largest-degree-first (LF) and smallest-degree-last (SL) serial heuristics, respectively. We show that although LF and SL, in prac-tice, generate colorings with relatively small numbers of colors, they are vulnerable to adversarial inputs for which any paralleliza-tion yields a poor parallel speedup. In contrast, LLF and SLL allow for provably good speedups on arbitrary inputs while, in practice, producing colorings of competitive quality to their serial analogs. We applied LLF and SLL to the parallel greedy coloring algo-rithm introduced by Jones and Plassmann, referred to here as JP. Jones and Plassman analyze the variant of JP that processes the ver-tices of a graph in a random order, and show that on an O(1)-degree graph G = (V,E), this JP-R variant has an expected parallel run-ning time of O(lgV / lg lgV) in a PRAM model. We improve this bound to show, using work-span analysis, that JP-R, augmented to handle arbitrary-degree graphs, colors a graph G = (V,E) with degree ∆ using Θ(V +E) work and O(lgV + lg ∆ ·min{√E,∆+ lg ∆ lgV / lg lgV}) expected span. We prove that JP-LLF and JP-SLL — JP using the LLF and SLL heuristics, respectively — execute with the same asymptotic work as JP-R and only logarith-mically more span while producing higher-quality colorings than JP-R in practice. We engineered an efficient implementation of JP for modern shared-memory multicore computers and evaluated its performance on a machine with 12 Intel Core-i7 (Nehalem) processor cores. Our implementation of JP-LLF achieves a geometric-mean speedup of 7.83 on eight real-world graphs and a geometric-mean speedup of 8.08 on ten synthetic graphs, while our implementation using SLL achieves a geometric-mean speedup of 5.36 on these real-world graphs and a geometric-mean speedup of 7.02 on these synthetic graphs. Furthermore, on one processor, JP-LLF is slightly faster than a well-engineered serial greedy algorithm using LF, and like-wise, JP-SLL is slightly faster than the greedy algorithm using SL

Contemporary Methods for Graph Coloring as an Example of Discrete Optimization

This paper provides an insight into graph coloringapplication of the contemporary heuristic methods. It discusses avariety of algorithmic solutions for The Graph Coloring Problem(GCP) and makes recommendations for implementation. TheGCP is the NP-hard problem, which aims at finding the minimumnumber of colors for vertices in such a way, that none of twoadjacent vertices are marked with the same color.With the adventof multicore processing technology, the metaheuristic approachto solving GCP reemerged as means of discrete optimization. Toexplain the phenomenon of these methods, the author makes athorough survey of AI-based algorithms for GCP, while pointingout the main differences between all these techniques

Contemporary Methods for Graph Coloring as an Example of Discrete Optimization

This paper provides an insight into graph coloringapplication of the contemporary heuristic methods. It discusses avariety of algorithmic solutions for The Graph Coloring Problem(GCP) and makes recommendations for implementation. TheGCP is the NP-hard problem, which aims at finding the minimumnumber of colors for vertices in such a way, that none of twoadjacent vertices are marked with the same color.With the adventof multicore processing technology, the metaheuristic approachto solving GCP reemerged as means of discrete optimization. Toexplain the phenomenon of these methods, the author makes athorough survey of AI-based algorithms for GCP, while pointingout the main differences between all these techniques