3,154 research outputs found
Genetic Algorithm with Optimal Recombination for the Asymmetric Travelling Salesman Problem
We propose a new genetic algorithm with optimal recombination for the
asymmetric instances of travelling salesman problem. The algorithm incorporates
several new features that contribute to its effectiveness: (i) Optimal
recombination problem is solved within crossover operator. (ii) A new mutation
operator performs a random jump within 3-opt or 4-opt neighborhood. (iii)
Greedy constructive heuristic of W.Zhang and 3-opt local search heuristic are
used to generate the initial population. A computational experiment on TSPLIB
instances shows that the proposed algorithm yields competitive results to other
well-known memetic algorithms for asymmetric travelling salesman problem.Comment: Proc. of The 11th International Conference on Large-Scale Scientific
Computations (LSSC-17), June 5 - 9, 2017, Sozopol, Bulgari
A hybrid heuristic solving the traveling salesman problem
This paper presents a new hybrid heuristic for solving the Traveling Salesman Problem, The
algorithm is designed on the frame of a general optimization procedure which acts upon two steps,
iteratively. In first step of the global search, a feasible tour is constructed based on insertion approach.
In the second step the feasible tour found at the first step, is improved by a local search optimization
procedure. The second part of the paper presents the performances of the proposed heuristic algorithm, on
several test instances. The statistical analysis shows the effectiveness of the local search optimization
procedure, in the graphical representation.peer-reviewe
An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem
The Generalized Traveling Salesman Problem (GTSP) is an extension of the
well-known Traveling Salesman Problem (TSP), where the node set is partitioned
into clusters, and the objective is to find the shortest cycle visiting each
cluster exactly once. In this paper, we present a new hybrid Ant Colony System
(ACS) algorithm for the symmetric GTSP. The proposed algorithm is a
modification of a simple ACS for the TSP improved by an efficient GTSP-specific
local search procedure. Our extensive computational experiments show that the
use of the local search procedure dramatically improves the performance of the
ACS algorithm, making it one of the most successful GTSP metaheuristics to
date.Comment: 7 page
An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem
In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO).
The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops
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