This research paper presents an adaptation of the cat swarm optimization (CSO) to solve the traveling salesman problem (TSP). This evolutionary algorithm appeared in 2007 by Chu and Tsai for optimization problems in the continuous case. To solve TSP, which is a discrete problem, we will describe the various operators and operations performed in two different modes of this algorithm, which is th...

We introduce four new general optimization algorithms based on the ‘demon’ algorithm from statistical physics and the simulated annealing (SA) optimization method. These algorithms reduce the computation time per trial without significant effect on the quality of solutions found. Any SA annealing schedule or move generation function can be used. The algorithms are tested on traveling salesman p...

Abstract: Multiple traveling salesman problems (MTSP) are a typical computationally complex combinatorial optimization problem, which is an extension of the famous traveling salesman problem (TSP). The MTSP can be generalized to a wide variety of routing and scheduling problems. The paper makes the attempt to show how Genetic Algorithm (GA) can be applied to the MTSP with ability constraint. In...

The DNA fragment assembly is an important phase required to obtain complete genomes. Optimization using nature inspired algorithms has been proposed by several authors. We present another nature inspired algorithm based on Particle Swarm Optimization and Differential Evolution. These algorithms are compared using a set of common benchmarks and show that our proposed algorithm has some advantage...

Nature inspired algorithms for their powerfulness, acquire a unique place among the algorithms for optimization. This paper intends to provide a comparison of firefly algorithm (FA) with 3 other nature inspired algorithms; genetic algorithms (GA), particle swarm optimization algorithm (PSO) and ant colony systems (ACS). Traveling salesman problem (TSP) has been used as the problem to be solved ...

We introduce a new extension of Punnen’s exponential neighborhood for the traveling salesman problem (TSP). In contrast to an interesting generalization of Punnen’s neighborhood by De Franceschi, Fischetti and Toth (2005), our neighborhood is searchable in polynomial time, a feature that invites exploitation by heuristic and metaheuristic procedures for the TSP and related problems, including t...

This paper presents a novel and efficient heuristic framework for approximating the solutions to the multiple traveling salesmen problem (m-TSP) and other variants on the TSP. The approach adopted in this paper is an extension of the Maximum-Entropy-Principle (MEP) and the Deterministic Annealing (DA) algorithm. The framework is presented as a general tool that can be suitably adapted to a numb...

– In this article we propose a Quantum inspired Genetic Algorithm (GQA) for solving the Traveling Salesman Problem (TSP). The TSP is a known combinatorial optimization problem which aims to find the shortest Hamiltonian cycle linking N cities. This algorithm is an extension of a classical genetic algorithm obtained by introducing some quantum principles such as quantum interference and states s...

The Traveling Salesman Problem (TSP) is to find a Hamiltonian tour of minimal length on a fully connected graph. The TSP is a NP-Complete, and there is no polynomial algorithm to find the optimal result. Many bio-inspired algorithms has been proposed to address this problem. Generally, generic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO) are three typical ...

The Lin-Kernighan heuristic is known to be one of the most successful heuristics for the Traveling Salesman Problem (TSP). It has also proven its efficiency in application to some other problems. In this paper we discuss possible adaptations of TSP heuristics for the Generalized Traveling Salesman Problem (GTSP) and focus on the case of the Lin-Kernighan algorithm. At first, we provide an easy-...