Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Abstract

The traveling salesman problem (TSP) is one of the most important issues in combinatorial optimization problems that are used in many engineering sciences and has attracted the attention of many scientists and researchers. In this issue, a salesman starts to move from a desired node called warehouse and returns to the starting place after meeting n customers provided that each customer is only met once. The aim of this issue is to determine a cycle with a minimum cost for this salesman. One of the major weaknesses of the PSO algorithm in the classical version is that it gets stuck in local optimizations. Therefore, in the proposed algorithm, called MPSO, the best solution in the current iteration is also used in the movement step. In addition, a variety of local search algorithms are provided that are used when better answers are generated than before. Also, a new method for moving the particle towards the best particle is presented, which, in addition to probably increasing the quality of the new answer, prevents the premature convergence of the algorithm due to consideration of the concept of random. The results evaluated with the results of several metaheuristic algorithms in the literature show the efficiency of the MPSO algorithm because it has been able to achieve excellent solutions in most of these instances.

Multiple Ant Colony Algorithm Combining Community Relationship Network

“…To verify the performance of the CACO algorithm, 20 independent experiments were conducted in different size city sets and compared with the latest improved particle swarm optimization algorithm MPSO [39] and wolf swarm optimization algorithm D-GWO [40] so far, and the results are shown in Tables 3 and 4.…”

Adaptive Grey Wolf Optimization Algorithm with Neighborhood Search Operations: An Application for Traveling Salesman Problem

“…Particle swarm algorithm (PSO) for a traveling salesman problem has been improved by introducing the best current solution [44]. The basic idea is to use the best current iteration in the moving steps, improving the movement of the particles toward the best regains that have the best quality of solutions in the search space.…”

Algoritma Emperor Penguin pada Efisiensi Pengiriman Produk UMKM dengan Konsep Pembagian Ongkos Kirim

“…Mayoritas penelitian terkait traveling salesman problem umumnya dibedakan menjadi dua yaitu traveling salesman problem (TSP) secara umum dan multiple traveling salesman problem (MTSP) yang kemudian dikembangkan menjadi colored traveling salesman problem (CTSP). Pada penelitian untuk permasalahan TSP umum telah dilakukan ujicoba dengan beberapa algoritma evolutionary diantaranya adalah Algoritma Genetik yang dilakukan perubahan pada operator crossovernya [7], Particle Swarm Optimization (PSO) dan perkembangannya yang disebut sebagai MPSO [8], Algoritma Ant Colony Optimization (ACO) dan perkembangannya yang disebut sebagai ACS [9], bahkan terdapat juga penelitian yang berusaha membandingkan beberapa algoritma (termasuk algoritma evolutionary) untuk mencari solusi paling optimal dari TSP [10]. Sedangkan pada CTSP telah dilakukan penelitian menggunakan algoritma genetik dan pengembangannya yang menggabungkan Algoritma Genetik dengan Hill Climbing dan Algoritma Greedy [11].…”