Hybrid evolutionary algorithms for the Multiobjective Traveling Salesman Problem

Psychas, Iraklis-Dimitrios; Delimpasi, Eleni; Marinakis, Yannis

doi:10.1016/j.eswa.2015.07.051

Cited by 37 publications

(14 citation statements)

References 49 publications

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“…Various meta-heuristics and heuristics have been developed for the multicriteria STSP, such as Pareto local search algorithms, MOEAs, multi-objective ant colony optimization methods, memetic algorithms and others (see, e.g., [9,10,13,15,22]). However, we have not found in the literature any multi-objective metaheuristic proposed specifically to the multicriteria ATSP and experimentally tested on instances with non-symmetric weights of arcs.…”

Section: Computational Experimentsmentioning

confidence: 99%

Reduction of the Pareto Set in Bicriteria Asymmetric Traveling Salesman Problem

Zakharov

Kovalenko

2018

Communications in Computer and Information Science

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0000−0003−2469−023X] and Yulia V. Kovalenko 2,3[0000−0003−4791−7011]Abstract. We consider the bicriteria asymmetric traveling salesman problem (bi-ATSP). Optimal solution to a multicriteria problem is usually supposed to be the Pareto set, which is rather wide in real-world problems. We apply to the bi-ATSP the axiomatic approach of the Pareto set reduction proposed by V. Noghin. We identify series of "quanta of information" that guarantee the reduction of the Pareto set for particular cases of the bi-ATSP. An approximation of the Pareto set to the bi-ATSP is constructed by a new multi-objective genetic algorithm. The experimental evaluation carried out in this paper shows the degree of reduction of the Pareto set approximation for various "quanta of information" and various structures of the bi-ATSP instances generated randomly.Keywords: Reduction of the Pareto set · Multi-objective genetic algorithm · Computational experiment 2 Problem Statement An instance of the traveling salesman problem [2] (TSP) is given by a complete graph G = (V, E), where V = {v 1 , . . . , v n } is the set of vertices and set E

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Section: Computational Experimentsmentioning

confidence: 99%

Reduction of the Pareto Set in Bicriteria Asymmetric Traveling Salesman Problem

Zakharov

Kovalenko

2018

Communications in Computer and Information Science

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“…Although, a variety of methods for the tour improvement heuristics have been proposed [36,47], the best methods are based on the following two main optimization approaches: (a) systematic exchange of edge tuples (the k-opt optimization methods use this approach); and, (b) hierarchical decomposition of the original problem into smaller problems.…”

Section: Motivations For the Development Of A Novel Algorithmmentioning

confidence: 99%

IntraClusTSP—An Incremental Intra-Cluster Refinement Heuristic Algorithm for Symmetric Travelling Salesman Problem

2018

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The Symmetric Traveling Salesman Problem (sTSP) is an intensively studied NP-hard problem. It has many important real-life applications such as logistics, planning, manufacturing of microchips and DNA sequencing. In this paper we propose a cluster level incremental tour construction method called Intra-cluster Refinement Heuristic (IntraClusTSP). The proposed method can be used both to extend the tour with a new node and to improve the existing tour. The refinement step generates a local optimal tour for a cluster of neighbouring nodes and this local optimal tour is then merged into the global optimal tour. Based on the performed evaluation tests the proposed IntraClusTSP method provides an efficient incremental tour generation and it can improve the tour efficiency for every tested state-of-the-art methods including the most efficient Chained Lin-Kernighan refinement algorithm. As an application example, we apply IntraClusTSP to automatically determine the optimal number of clusters in a cluster analysis problem. The standard methods like Silhouette index, Elbow method or Gap statistic method, to estimate the number of clusters support only partitional (single level) clustering, while in many application areas, the hierarchical (multi-level) clustering provides a better clustering model. Our proposed method can discover hierarchical clustering structure and provides an outstanding performance both in accuracy and execution time.

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“…Seperti pemilihan parents, pemilihan survivor juga berperan berperan untuk membedakan individual berdasarkan kualitasnya, perbedaannya hanya pada proses keduanya dilakukan pada tahap yang berbeda. Pemilihan survivor dilakukan setelah proses penciptaan offspring dari parents terpilih [7].…”

Section: Seleksiunclassified

“…Tidak semua generasi terbaik bisa dilakukan mutasi tetapi selalu disesuaikan dalam probabilitas yang ditentukan melalui mutasi setiap jalur (probalitas mutasi) antara not Pm. Salah satu pemutasi yang sering digunakan dalam algortima evolusi adalah Mutasi terarah, dalam penggunaannya melalui hasil yang diinformasikan genenerasi pertama [7].…”

Section: Mutasiunclassified

Pengaruh Inisialisasi Populasi Random Search Pada Algoritma Berevolusi Dalam Optimasi Travelling Salesman Problem (Tsp)

Fitriyani¹,

Away²,

Gani³

2018

JNKTI

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<p>Traveling Salesman Problem (TSP) is a problem where each initial route of departure and return path between regions remains the same. The problem with TSP is how to get the optimum results to get the shortest path that will be passed, to solve TSP problems, one way can be by using evolved algorithms. Evolution Algorithm (AE) is a method that uses natural selection as the main idea in solving a particular problem. This algorithm is implemented through computer simulations starting from the individual population that will be randomly generated and then evaluated to achieve the best solution. Random search is included in part of stochastic optimization and global optimization. Random search is a direct search method that does not require derivatives to search for continue domains. The purpose of this study was to see the effect of population initialization with random search on algorithms evolving in TSP optimization. Results of initialization strategy implementation Random search has shown more optimal results compared to pure initialization without using random search. This is due to the initialization searching for space for random initialization.</p>

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Hybrid evolutionary algorithms for the Multiobjective Traveling Salesman Problem

Cited by 37 publications

References 49 publications

Reduction of the Pareto Set in Bicriteria Asymmetric Traveling Salesman Problem

Reduction of the Pareto Set in Bicriteria Asymmetric Traveling Salesman Problem

IntraClusTSP—An Incremental Intra-Cluster Refinement Heuristic Algorithm for Symmetric Travelling Salesman Problem

Pengaruh Inisialisasi Populasi Random Search Pada Algoritma Berevolusi Dalam Optimasi Travelling Salesman Problem (Tsp)

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