Heuristics for combinatorial optimization are often controlled by discrete and continuous parameters that define its behavior. The number of possible configurations of the heuristic can be large, resulting in a difficult analysis. Manual tuning can be time-consuming, and usually considers a very limited number of configurations. An alternative to manual tuning is automatic tuning. In this paper...

among various heuristics techniques, genetic algorithm (ga) is one of the most widely used techniques which has successfully been applied on a variety of complex combinatorial problems. the performance of ga largely depends on the proper selection of its parameters values; including crossover mechanism, probability of crossover, population size and mutation rate and selection percent. in this p...

Genetic Algorithms (GAs) have shown themselves to be very powerful tools for a wide variety of combinatorial optimization problems. Through this project I hope to implement a GA to solve the Minimum Labeling Spanning Tree (MLST) problem (a combinatorial optimization problem). If time permits, I may attempt to modify the code to solve another combinatorial optimization problem. Additionally, I w...

Multiple Travelling Salesman Problem (mTSP) is one of the most popular and widely used combinatorial optimization problems in the operational research. Many complex problems can be modeled and solved by the mTSP. To solve the mTSP, deterministic algorithms cannot be used as the mTSP is an NP-hard optimization problem. Hence, heuristics approaches are usually applied. In this paper, the Gravitat...

Classical approaches to multi-constrained routing problems generally require construction of trees and the use of heuristics to prevent combinatorial explosion. Introduced here is the notion of constrained path algebras and their application to multi-constrained path problems. The inherent combinatorial properties of these algebras make them useful for routing problems by implicitly pruning the...

The maximum clique problem (MCP) is to determine a sub graph of maximum cardinality. A clique is a The corresponding optimization problem i.e. finding the maximum Maximum Clique Problem. in Handbook of Combinatorial. Common theme of every optimization problem: of semidefinite programming, A module on combinatorial optimization, Selected topics: The largest clique (i.e., complete subgraph)! In “...

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. In this paper, we describe a GRASP for the graph planarization problem, extending the heuristic of Goldschmidt and Takvorian [Networks, v. 24, pp. 69–73, 1994]. We review basic concepts of GRASP: construction and local search algorithms. The implementation of GRASP for graph planarization is...