Design, Evaluation and Analysis of Combinatorial Optimization Heuristic Algorithms
Daniel Karapetyan

TL;DR
This paper explores heuristic algorithms for two NP-hard combinatorial problems, focusing on local search and memetic algorithms, resulting in improved heuristics and state-of-the-art metaheuristics.
Contribution
It systematically advances local search neighborhoods and memetic algorithms for the Generalized Traveling Salesman and Multidimensional Assignment Problems.
Findings
Proposed heuristics outperform existing algorithms in various time/quality scenarios.
Enhanced local search methods significantly improve solution quality.
New memetic algorithms set a state-of-the-art benchmark for these problems.
Abstract
Combinatorial optimization is widely applied in a number of areas nowadays. Unfortunately, many combinatorial optimization problems are NP-hard which usually means that they are unsolvable in practice. However, it is often unnecessary to have an exact solution. In this case one may use heuristic approach to obtain a near-optimal solution in some reasonable time. We focus on two combinatorial optimization problems, namely the Generalized Traveling Salesman Problem and the Multidimensional Assignment Problem. The first problem is an important generalization of the Traveling Salesman Problem; the second one is a generalization of the Assignment Problem for an arbitrary number of dimensions. Both problems are NP-hard and have hosts of applications. In this work, we discuss different aspects of heuristics design and evaluation. A broad spectrum of related subjects, covered in this…
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Taxonomy
TopicsVehicle Routing Optimization Methods · Data Management and Algorithms · Metaheuristic Optimization Algorithms Research
