Enhanced discrete bacterial memetic evolutionary algorithm - An efficacious metaheuristic for the traveling salesman optimization

Kóczy, László T.; Földesi, Péter; Tüű-Szabó, Boldizsár

doi:10.1016/j.ins.2017.09.069

Cited by 32 publications

(19 citation statements)

References 18 publications

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“…The 3opt operator is used to further improve the best solution produced by ACO. Kóczy et al [12] presented a discrete bacterial memetic evolutionary algorithm (DBMEA), which is based on the combination of the bacterial evolutionary algorithm and local search techniques. Ouenniche et al [14] proposed a dual local search framework, that is, a search method that starts with an infeasible solution, explores the dual space, reduces infeasibility iteratively, and lands in the primal space to deliver a feasible solution.…”

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

confidence: 99%

“…In order to incorporate the information entropy matrix, the bird {3 2 1 4} is converted into a Boolean matrix representing the relation of edges. The matrix is shown in (12), where 1 denotes the edge between the two cities which is selected; 0 stands for the edge which is not selected. Figure 1 depicts the conversion steps from the TSP path to the Boolean matrix.…”

Section: Discrete Bird Swarm Algorithmmentioning

confidence: 99%

“…For combinatorial optimization problems such as TSP, these equations should be redesigned to be consistent with the characteristics of problem at hand as well as retain the good features of original algorithm. Firstly, each TSP solution is converted to an edge matrix according to (12), and then a new minus function is introduced to evaluate the difference between two solutions in the DBSA algorithm.…”

Section: Discrete Bird Swarm Algorithmmentioning

confidence: 99%

“…Implementation of DBSA. Algorithm 2 describes the implementation steps of the DBSA algorithm and Figure 2 draws the flow chart of the DBSA algorithm, where the purpose of perform TSP operators (code (12) and code (22)) in Algorithm 2 is to produce new solution according to the updated information entropy matrix. Algorithm 3 gives the pseudocode of perform TSP operators.…”

Section: Discrete Bird Swarm Algorithmmentioning

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%

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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%

Section: Discrete Bird Swarm Algorithmmentioning

confidence: 99%

Section: Discrete Bird Swarm Algorithmmentioning

confidence: 99%

Section: Discrete Bird Swarm Algorithmmentioning

confidence: 99%