Traveling Salesman Problem Solution Using Plate Tectonics Based Neighborhood Search Optimization

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

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Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization

Applied Sciences ◽

10.3390/app11114780 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4780

Author(s):

Muhammad Salman Qamar ◽

Shanshan Tu ◽

Farman Ali ◽

Ammar Armghan ◽

Muhammad Fahad Munir ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Traveling Salesman Problem ◽

Particle Swarm ◽

Standard Test ◽

Traveling Salesman ◽

Problem Solution ◽

Ant System ◽

Swarm Optimization ◽

Hybrid Particle Swarm Optimization ◽

Novel Approach

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.

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Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks

Neural Networks ◽

10.1016/s0893-6080(03)00130-8 ◽

2003 ◽

Vol 16 (5-6) ◽

pp. 827-832 ◽

Cited By ~ 55

Author(s):

Samuel A. Mulder ◽

Donald C. Wunsch

Keyword(s):

Neural Networks ◽

Traveling Salesman Problem ◽

Traveling Salesman ◽

Divide And Conquer ◽

Problem Solution ◽

Adaptive Resonance

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Unconventional GVNS for Solving the Garbage Collection Problem with Time Windows

Technologies ◽

10.3390/technologies7030061 ◽

2019 ◽

Vol 7 (3) ◽

pp. 61 ◽

Cited By ~ 2

Author(s):

Christos Papalitsas ◽

Theodore Andronikos

Keyword(s):

Traveling Salesman Problem ◽

Optimization Problems ◽

Time Windows ◽

Experimental Information ◽

Traveling Salesman ◽

Neighborhood Search ◽

Np Hard ◽

The Novel ◽

Global Positioning ◽

The Traveling Salesman Problem

GVNS, which stands for General Variable Neighborhood Search, is an established and commonly used metaheuristic for the expeditious solution of optimization problems that belong to the NP-hard class. This paper introduces an expansion of the standard GVNS that borrows principles from quantum computing during the shaking stage. The Traveling Salesman Problem with Time Windows (TSP-TW) is a characteristic NP-hard variation in the standard Traveling Salesman Problem. One can utilize TSP-TW as the basis of Global Positioning System (GPS) modeling and routing. The focus of this work is the study of the possible advantages that the proposed unconventional GVNS may offer to the case of garbage collector trucks GPS. We provide an in-depth presentation of our method accompanied with comprehensive experimental results. The experimental information gathered on a multitude of TSP-TW cases, which are contained in a series of tables, enable us to deduce that the novel GVNS approached introduced here can serve as an effective solution for this sort of geographical problems.

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