The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version created by the researchers from the University of Tehran. The authors of the present paper have extended the exIWO algorithm introducing a set of both deterministic and nondeterministic strategies of individuals’ selection. The goal of the project was to evaluate the e...

The traveling salesman problem (TSP) has attracted many researchers’ attention in the past few decades, and amounts of algorithms based on heuristic algorithms, genetic algorithms, particle swarm optimization, tabu search and memetic algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. This paper is devoted to the presentatio...

The traveling salesman problem (TSP) describes a salesman who needs to travel through a list of given cities exactly once by taking the shortest possible tour. The problem is NP-Complete since there are no known polynomial time algorithms to solve it. The main motivation to solve this problem is that the applications of Traveling Salesman are immense. It can be used by banks to find an optimal ...

This paper shows the application of Wang’s Recurrent Neural Network with the 'Winner Takes All' (WTA) principle in a soft version to solve the Traveling Salesman Problem. In soft WTA principle the winner neuron is updated at each iteration with part of the value of each competing neuron and some comparisons with the hard WTA are made in this work with instances of the TSPLIB (Traveling Salesman...

The traveling salesman problem and the quadratic assignment problem are the two of the most commonly studied optimization problems in Operations Research because of their wide applicability. Due to their NP-hard nature, the individual problems are already complex and difficult to solve. In this paper, the two hard problems are integrated together first, that is called the integrated problem of ...

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...

The edges of a complete graph on n vertices are assigned i. i. d. random costs from a distribution for which the interval [0, t] has probability asymptotic to t as t → 0 through positive values. In this so called pseudo-dimension 1 mean field model, we study several optimization problems, of which the traveling salesman is the best known. We prove that as n → ∞, the cost of the minimum travelin...

A method for dynamic scheduling on a network computing system and an approximation algorithm for solving the asymmetric traveling salesman problem (ATSP) are presented in this paper. Dynamic scheduling was implemented to minimize the application program execution time. Our method decomposes the program workload into com-putationally homogeneous subtasks, which may be of diierent size, depending...

In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dua...

The graphical traveling salesman problem (GTSP) has been studied as a variant of the classical symmetric traveling salesman problem (STSP) suited particularly for sparse graphs. In addition, it can be viewed as a relaxation of the STSP and employed for solving the latter to optimality as originally proposed by Naddef and Rinaldi. There is a close natural connection between the two associated po...