Hybrid Differential Evolution Algorithm for Traveling Salesman Problem

Xiang, Wang; Xu, Guoyi

doi:10.1016/j.proeng.2011.08.511

Cited by 20 publications

(5 citation statements)

References 3 publications

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“…LRT : length of the resulting tour, LOT : length of the optimal tour. Table 1 shows a comparison of the two proposed alternatives of the monarchy metaheuristic MN 1 and MN 2 and other methods in the literature that have used integer distances, the results of GA (genetic algorithm) and AMCPA (adaptive multicrossover population algorithm) were taken from [4], the results of HGA (hybrid genetic algorithm) were taken from [5], the results of HDE (hybrid differential evolution algorithm) and DPSO (discrete particle swam optimization) were taken from [6], and the results of SA (simulated annealing), ACO (ant colony optimization), and TPASHO (two-phase hybrid optimization algorithm) were taken from [7]. As seen in Table 1, the results produced by the proposed method are better than those of all the other methods for Eil101, Eil76, Eil51, and St70 test problems, and the optimum solutions are obtained for the Eil51 and St70 instances.…”

Section: Resultsmentioning

confidence: 99%

A novel metaheuristic optimization algorithm: the monarchy metaheuristic

Ahmia¹

2019

Turk J Elec Eng & Comp Sci

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In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems. To evaluate the efficiency of the proposed algorithm, we examined it on the traveling salesman problem (TSP) using some benchmark from TSPLIB online library of instances for the TSP. The experimental results indicate that the monarchy metaheuristic is competitive with the other methods that exist in the literature for finding approximate solutions.

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Section: Resultsmentioning

confidence: 99%

A novel metaheuristic optimization algorithm: the monarchy metaheuristic

Ahmia¹

2019

Turk J Elec Eng & Comp Sci

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“…In this part, we will try to compare the results of the application of the RSO with the results of other more known metaheuristics. The results of ACO (ant colony optimization) were taken from (Bao, 2015), the results of ABC (artificial bee colony) (Gündüz et al, 2015), and the results of HA (Wang et al, 2011).…”

Section: Comparison and Discussionmentioning

confidence: 99%

A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem

Mzili

Riffi

Mzili

2022

Decis. Mak. Appl. Manag. Eng.

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Metaheuristics are often used to find solutions to real and complex problems. These algorithms can solve optimization problems and provide solutions close to the global optimum in an acceptable and reasonable time. In this paper, we will present a new bio-inspired metaheuristic based on the natural chasing and attacking behaviors of rats in nature, called a Rat swarm optimizer. Which has given good results in solving several continuous optimization problems, and adapted it to solve a discrete, NP-hard, and classical optimization problem that is the traveling salesman problem (TSP) while respecting the natural behavior of rats. To test the efficiency of the adaptation of our proposal, we applied the adapted rat swarm optimization (RSO) algorithm to some reference instances of TSPLIB. The obtained results show the performance of the proposed method in solving the traveling salesman problem (TSP).

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“…X. Wang and Xu (2011)proposed hybrid DE Algorithm that uses hill climbing heuristic for solving traveling salesman problem.Gao andLiu (2011)developed a hybridization betweenthe artificial bee colony and DE algorithms.El Dor et al ( 2012) hybridized particle swarm optimization with DE for enhancing the algorithm. Cui et al(2013) proposed a hybrid differential evolution harmony search algorithm for numerical optimization problems.…”

Section: Hybridization With Other Algorithmsmentioning

confidence: 99%

Literature Review on Differential Evolution Algorithm

Oudah¹,

Zaher²,

Hassan³

et al. 2021

JUSST

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Differential evolution algorithm is one of the most efficient metaheuristic approaches. In this paper, a review and analysis is presented in order to help for future research in differential evolution algorithm. It covers an analysis of about 142 papers of the previous work in the modifications of the algorithm including the main parameters of the classical steps of the algorithm and hybridization with other algorithms. The analysis also shows the applications that optimized using the differential evolution algorithm.

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Hybrid Differential Evolution Algorithm for Traveling Salesman Problem

Cited by 20 publications

References 3 publications

A novel metaheuristic optimization algorithm: the monarchy metaheuristic

A novel metaheuristic optimization algorithm: the monarchy metaheuristic

A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem

Literature Review on Differential Evolution Algorithm

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