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Ant colony optimization with parameter update using a genetic algorithm for travelling salesman problem

E. A. Blagoveshchenskaya, Il'ya Igorevich Mikulik, Lutz Strüngmann

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Abstract

It is widely known that the ant colony algorithm is sensitive to changes in parameters. We present a novel kind of the algorithm which uses a genetic algorithm for solving this problem. Travelling salesman problem, known as NP-complete problem, is one of the most popular and important tasks in combinatorial optimization. This is a typical task for testing combinatorial optimization algorithms. In this paper, we present a comparative analysis of the algorithm with a simple ant colony optimization (SACO) on the example of this problem.

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What this paper is about

It is widely known that the ant colony algorithm is sensitive to changes in parameters. We present a novel kind of the algorithm which uses a genetic algorithm for solving this problem. Travelling salesman problem, known as NP-complete problem, is one of the most popular and important tasks in combinatorial optimization. This is a typical task for testing combinatorial optimization algorithms. In this paper, we present a comparative analysis of the algorithm with a simple ant colony optimization (SACO) on the example of this problem.

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Available abstract

It is widely known that the ant colony algorithm is sensitive to changes in parameters. We present a novel kind of the algorithm which uses a genetic algorithm for solving this problem. Travelling salesman problem, known as NP-complete problem, is one of the most popular and important tasks in combinatorial optimization. This is a typical task for testing combinatorial optimization algorithms. In this paper, we present a comparative analysis of the algorithm with a simple ant colony optimization (SACO) on the example of this problem.

Key concepts: Travelling salesman problem, Ant colony optimization algorithms, Genetic algorithm, Computer science, Mathematical optimization, Algorithm, Mathematics

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