Dynamic multiscale region search algorithm using vitality selection for traveling salesman problem

Zhang, Hongguang; Zhou, Jie

doi:10.1016/j.eswa.2016.05.007

Cited by 25 publications

(15 citation statements)

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“…Ismkhan et al [2] put forward a new ACO algorithm with three effective strategies including pheromone representation with linear space complexity, new next city selection, and pheromone augmented 2-opt local search. Zhang et al [13] proposed a DMRSA algorithm using vitality selection for TSP. In the DMRSA algorithm, vitality selection (VS) is proposed as a new modification scheme based on delete-oldest selection for TSP.…”

Section: (1) Initialize the Parameter Values Of N M Fq P;mentioning

confidence: 99%

“…17) (9Else (10) Birds keep vigilance using Eq. 18) (11End if (12) Perform operator; (13) End for (14) Else //flying (15) Divide the swarm into two parts: producers and scroungers. (16) For = 1 to N (17) If (i==producer) (18) Birds flight using Eq.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Compare with Some Recently Improved Classical Metaheuristics. In order to further observe the performance of DBSA algorithm and give more credibility of our improvement, DBSA algorithm was compared with several recently improved classical metaheuristic algorithms, such as DBMEA [12], DMRSA [13], ACE [1], and HGA [4] algorithms. Table 5 gives the results of comparison between DBSA and ACE algorithm on 22 TSP instances, where Ry48, Ftv70, Ftv170, and Kro124p are asymmetric TSP problems.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Therefore, regardless of the optimal solutions or the average solutions, the DBSA algorithm is superior to the DBMEA algorithm. Table 7 shows the comparison results of the DBSA algorithm and the DMRSA algorithm on 24 TSP instances [13]. The maximum number of iterations of the DBSA algorithm is 2000, but DMRSA uses a local search strategy, which requires more internal iterations.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…In recent years, many metaheuristic algorithms have been proposed to solve the TSP, such as ant colony algorithm (ACO) [1,2], artificial bee colony algorithm (ABC) [3], genetic algorithm (GA) [4], particle swarm optimization (PSO) [5], cuckoo search algorithm (CS) [6,7], bat algorithm (BA) [8,9], firefly algorithm (FA) [10], invasive weed optimization [11], bacterial evolutionary algorithm [12], dynamic multiscale region search algorithm (DMRSA) [13], a dual local search algorithm [14], immune algorithm [15], simulated annealing algorithm [16], and some hybrid algorithms [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Discrete Bird Swarm Algorithm Based on Information Entropy Matrix for Traveling Salesman Problem

Lin

Zhong

Lin

et al. 2018

Mathematical Problems in Engineering

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Although bird swarm optimization (BSA) algorithm shows excellent performance in solving continuous optimization problems, it is not an easy task to apply it solving the combination optimization problem such as traveling salesman problem (TSP). Therefore, this paper proposed a novel discrete BSA based on information entropy matrix (DBSA) for TSP. Firstly, in the DBSA algorithm, the information entropy matrix is constructed as a guide for generating new solutions. Each element of the information entropy matrix denotes the information entropy from city i to city j. The higher the information entropy, the larger the probability that a city will be visited. Secondly, each TSP path is represented as an array, and each element of the array represents a city index. Then according to the needs of the minus function proposed in this paper, each TSP path is transformed into a Boolean matrix which represents the relationship of edges. Third, the minus function is designed to evaluate the difference between two Boolean matrices. Based on the minus function and information entropy matrix, the birds’ position updating equations are redesigned to update the information entropy matrix without changing the original features. Then three TSP operators are proposed to generate new solutions according to the updated information entropy matrix. Finally, the performance of DBSA algorithm was tested on a large number of benchmark TSP instances. Experimental results show that DBSA algorithm is better or competitively outperforms many state-of-the-art metaheuristic algorithms.

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Section: (1) Initialize the Parameter Values Of N M Fq P;mentioning

confidence: 99%