A discrete invasive weed optimization algorithm for solving traveling salesman problem

Zhou, Yongquan; Luo, Qifang; Chen, Huan; He, Anping; Wu, Jinzhao

doi:10.1016/j.neucom.2014.01.078

Cited by 118 publications

(55 citation statements)

References 36 publications

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“…We decided to use an improved complete 3-0pt method inspired from the complete 2-0pt method [16] that consists of the following: where i = 1 , j = i + 2, k = j + 2 • Select an edge of the tour -< Si , Si+ 1 >-; i < n -2…”

Section: Hybrid Local Searchmentioning

confidence: 99%

A new Hybrid Discrete Bat Algorithm for Traveling Salesman Problem using ordered crossover and 3-Opt operators for Bat's local search

Amara

Hamdani

Alimi³

2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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In this paper we propose a new Hybrid Bat Algo rithm to solve the traveling salesman problem (TSP) that has attracted many researchers applying exact and metaheuristic methods trying to solve it. The new proposed method is based on the basics of Bat Algorithm (BA) recently proposed as a new bio-inspired meta-heuristic algorithm. Accordingly, we use the concepts of Swap Operator (SO) and Swap Sequence (SS) to redefine respectively BA position and velocity operators for TSP.Additionally, based on ordered crossover and 3-0pt algorithm, we propose to redefine the Bat's local search method. We compare our algorithm to other state of the art methods from the literature by using benchmark datasets of symmetric TSP from TSPLIB library in order to test its effectiveness. Based on the recorded experiments our method outperforms most of the compared methods.

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Section: Hybrid Local Searchmentioning

confidence: 99%

A new Hybrid Discrete Bat Algorithm for Traveling Salesman Problem using ordered crossover and 3-Opt operators for Bat's local search

Amara

Hamdani

Alimi³

2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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“…For example, one might divide metaheuristics into two categories depending on whether they are pure or hybrid. Examples of pure metaheuristics for the TSP include Simulated Annealing (Kirkpatrick et al, 1983;Malek et al, 1989), Tabu Search (Malek, 1988;Malek et al, 1989;Tsubakitani and Evans, 1998a), Guided Local Search (Voudouris and Tsang, 1999), Jump Search (Tsubakitani and Evans, 1998b), Randomized Priority Search (DePuy, Moraga and Whitehouse, 2005), Greedy Heuristic with Regret (Hassin and Keinan, 2008), Genetic Algorithms (Jayalakshmi et al, 2001;Tsai et al, 2003;Albayrak and Allahverdi, 2011;Nagata and Soler, 2012), Evolutionary Algorithms (Liao et al, 2012), Ant Colony Optimization (Dorigo and Gambardella, 1997), Artificial Neural Networks (Leung et al, 2004;Li et al, 2009), Water Drops Algorithm (Alijla et al, 2014), Discrete Firefly Algorithm (Jati et al, 2013), Invasive Weed Optimization (Zhou et al, 2015), Gravitational Search (Dowlatshahi et al, 2014), and Membrane Algorithms (He et al, 2014). Examples of hybrid metaheuristics include Simulated Annealing with Learning (Lo and Hsu, 1998), Genetic Algorithm with Learning (Liu and Zeng, 2009), SelfOrganizing Neural Networks and Immune System (Masutti and de Castro, 2009), Genetic Algorithm and Local Search (Albayrak and Allahverdi, 2011), Genetic Algorithm and Ant Colony Optimization (Dong at al., 2012), Honey Bees Mating and GRASP (Marinakis et al, 2011), and Particle Swarm Optimization and Ant Colony Optimization (Elloumi et al, 2014).…”

Section: Heuristic Approaches and Methodsmentioning

confidence: 99%

A dual local search framework for combinatorial optimization problems with TSP application

Ouenniche

Ramaswamy

Gendreau

2017

Journal of the Operational Research Society

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Abstract:In practice, solving realistically sized combinatorial optimization problems (COPs) to optimality is often too time-consuming to be affordable; therefore, heuristics are typically implemented within most applications software. A specific category of heuristics has attracted considerable attention, namely, local search methods. Most local search methods are primal in nature; that is, they start the search with a feasible solution and explore the feasible space for better feasible solutions. In this research, we propose a dual local search method and customise it to solve the traveling salesman problem (TSP); that is, a search method that starts with an infeasible solution, explores the dual space -each time reducing infeasibility, and lands in the primal space to deliver a feasible solution. The proposed design aims to replicate the designs of optimal solution methodologies in a heuristic way. To be more specific, we solve a combinatorial relaxation of a TSP formulation, design a neighborhood structure to repair such an infeasible starting solution, and improve components of intermediate dual solutions locally. Sample-based evidence along with statistically significant t-tests support the superiority of this dual design compared to its primal design counterpart.

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“…The most commonly used heuristic and meta-heuristic algorithms such as Greedy algorithms, 2-opt algorithms, Simulated Annealing (cf., [3,4]), Tabu Search, Ant Colony Optimization [5,6], Genetic Algorithms (cf., [7][8][9][10]), Neural Networks [11], Weed optimization [12] and Memetic Algorithm (MA) (cf., [11,13]) etc. TSP algorithms are categorized into two classes such as exact and approximate algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Algorithm with Modified Inver-Over Operator and Genetic Algorithm Search for Traveling Salesman Problem

Singh

2016

Advances in Intelligent Systems and Computing

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In this article, we develop a novel hybrid approach to solve the traveling salesman problem (TSP). In this approach, we first initialize suboptimal solution using Nearest Neighbor (NN) tour construction method, followed modified Inver-over operator and then proposed crossover with 2-opt mutation applied to improve for optimal solution. We use 14 TSP data sets from TSPLIB to evaluate the performance of proposed hybrid method. The proposed hybrid method gives better results in terms of best and average error. In experimental results of the tests we show that the proposed hybrid method is superior to available algorithm in literature.Keywords Traveling salesman problem Á Basic inver-over operator Á Modified inver-over operator Á 2-opt mutation Á Crossover operator

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A discrete invasive weed optimization algorithm for solving traveling salesman problem

Cited by 118 publications

References 36 publications

A new Hybrid Discrete Bat Algorithm for Traveling Salesman Problem using ordered crossover and 3-Opt operators for Bat's local search

A new Hybrid Discrete Bat Algorithm for Traveling Salesman Problem using ordered crossover and 3-Opt operators for Bat's local search

A dual local search framework for combinatorial optimization problems with TSP application

A Hybrid Algorithm with Modified Inver-Over Operator and Genetic Algorithm Search for Traveling Salesman Problem

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