We discussed how to use tools from exploratory data analysis of statistics to visualize experimental results. We used the data collected from the Task 1 of the Implementation Contest. In the comparison there were three extra participants (ROS: Random Ordered Sequence greedy coloring, DSATUR, The degree of saturation and RLF, Recursive Large First). RLF and DSATUR clearly outperformed the other ...

In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead toward an efficient, effective, and robust search improve their performance terms of solution quality, convergence rate, robustness. Since various methods with different purposes have developed,...

Different successful heuristic approaches have been proposed for solving combinatorial optimization problems. Commonly, each of them is specialized to serve a different purpose or address specific difficulties. However, most combinatorial problems that model real world applications have a priori well known measurable properties. Embedded machine learning methods may aid towards the recognition ...

The notion of a tolerance is a helpful tool for designing approximation and exact algorithms for solving combinatorial optimization problems. In this paper we suggest a tolerance-based polynomial heuristic algorithm for theweighted independent set problem. Several computational experiments show that our heuristics works very well on graphs of a small density.

Although dynamic programming could ideally solve any combinatorial optimization problem, the curse of dimensionality of the search space seriously limits its application to large optimization problems. For example, only few papers in the literature have reported the application of dynamic programming to workforce scheduling problems. This paper investigates approximate dynamic programming to ta...

The marriage in honey–bees optimization (MBO) algorithm was recently proposed and showed good results for combinatorial optimization problems. Contrary to most of the swarm intelligence algorithms (such as Ant Colony Optimization), MBO uses self-organization to mix different heuristics. This paper presents a variation of the MBO algorithm where the colony contains a single queen with multiple w...

Multiobjective combinatorial optimization problems in various disciplines remain to be the focus of extensive research due to their inherent hard nature and difficulty of finding near-optimal solutions. Iterative heuristics like Tabu Search (TS) and Simulated Evolution (SimE) have successfully been employed to solve a range of such optimization problems [1]. These heuristics are able to obtain ...

The problem of m-machine permutation flowshop scheduling is considered in this paper. The objective is to minimize the makespan. The flowshop scheduling problem is a typical combinatorial optimization problem and has been proved to be strongly NP-hard. Hence, several heuristics and meta-heuristics were addressed by the researchers. In this paper, a discrete African wild dog algorithm is applied...

This paper deals with several combinatorial optimization problems. The most challenging such problem is the quadratic assignment problem. It is considered in both two dimensions (QAP) and in three dimensions (Q3AP) and in the context of communication engineering. Semidefinite relaxations are used to derive lower bounds for the optimum while heuristics are applied to either find upper bounds or ...

Counting-based branching heuristics in CP have been very successful on a number of problems. Among the few remaining hurdles limiting their general applicability are the integration of counting information from auxiliary variables and the ability to handle combinatorial optimization problems. This paper proposes to answer these challenges by generalizing existing solution counting algorithms fo...