Discrete cuckoo search algorithm for the travelling salesman problem

Ouaarab, Aziz; Ahiod, Belaïd; Yang, Xin-She

doi:10.1007/s00521-013-1402-2

Cited by 404 publications

(216 citation statements)

References 28 publications

Supporting

Mentioning

209

Contrasting

Unclassified

Order By: Relevance

“…Le´vy flights, named after the French mathematician Paul Le´vy, represent a model of random walks characterized by their step lengths which obey a powerlaw distribution [6]. CS is widely applied to solve many combinatorial optimization problems such as Travelling Salesman Problem [25], [27], Flow Shop Schedul ing Problem [20], Knapsack Problem [19].…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Random Key Cuckoo Search for the Quadratic Assignment Problem

Ouaarab

Ahiod

Yang

2017

TMLAI

Self Cite

View full text Add to dashboard Cite

This paper proposes an adaptation of the Random Key Cuckoo Search (RKCS) algorithm for solving the famous Quadratic Assignment Problem (QAP). We used a simplified and efficient randomkey encoding scheme to convert a continous space (real numbers) into a combinatorial space. We also consid ered the displacement of a solution in both spaces by using Le´vy flights. The performance of the RKCS for QAP is tested against a set of benchmarks of QAP from the wellknown QAPLIB library, and the comparison with a set of other methaheuristics is also carried out.

show abstract

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

“…Randomkey encoding scheme [3], [27] is an interesting procedure that can be useful to pass from a continuous space to the combinatorial space. In general, the position in the continuous space is represented by a vector of real numbers.…”

Section: Random-key Encoding Schemementioning

confidence: 99%

Random Key Cuckoo Search for the Quadratic Assignment Problem

Ouaarab

Ahiod

Yang

2017

TMLAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, meta-heuristic algorithms have many advantages of implementation simplicity, adaptability, flexibility and robustness, and the algorithms can obtain a more better or near-optimal solution of higher quality in relatively low time. Here many meta-heuristic algorithms have been developed to solve complex TSP problems [9][10].…”

Section: Introductionmentioning

confidence: 99%

Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems

Zoua

Chen

2017

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. In this paper, a discrete variant of TLBO (DTLBO) is proposed for solving the traveling salesman problem (TSP). In the proposed method, an effective learner representation scheme is redefined based on the characteristics of TSP problem. Moreover, all learners are randomly divided into several sub-swarms with equal amounts of learners so as to increase the diversity of population and reduce the probability of being trapped in local optimum. In each subswarm, the new positions of learners in the teaching phase and the learning phase are generated by the crossover operation, the legality detection and mutation operation, and then the offspring learners are determined based on greedy selection. Finally, to verify the performance of the proposed algorithm, benchmark TSP problems are examined and the results indicate that DTLBO is effective compared with other algorithms used for TSP problems.

show abstract

“…There is also the exact methods, but it can only solve the TSP instances problem with minimal size, but when the number of city is high, its impossible to find the global solution. But after using the metaheuristic method, it's possible to find the best optimal solution in a reasonable execution time, as the simulated annealing [3], Tabu search [4], Harmony search [5], cuckoo search algorithm [6], Genetic algorithm [7], Ant colony optimization [8], Particle swarm optimization [9], Bee colony optimization [10], cat swarm optimization algorithm [11], bat swarm optimization [12]. To improve the efficiency of some methods, the researchers had proposed some hybrid methods that had proven their efficiency to solve the TSP, such as, the hybrid genetic algorithm [13], hybrid antcolony optimization [14], hybrid particle swarm optimization [15], hybrid bee colony optimization [16].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of Three Metaheuristics for Solving the Travelling Salesman Problem

Bouzidi

Riffi

Bouzidi

2017

TMLAI

View full text Add to dashboard Cite

This research paper aims to do a comparative study of three recent optimization metaheuristic approaches that had been applied to solve the NPhard optimization problem called the travelling salesman problem. The three recent metaheuristics in study are cuckoo search algorithm, cat swarm optimization algorithm and batinspired algorithm. To compare the performances of these methods, the three metaheuristics are applied to solve some benchmark instances of TSPLIB. The obtained results are collected and the error percentage is calculated. The discussion, will present which method is more efficient to solve the real application based on the travelling salesman problem.

show abstract

Discrete cuckoo search algorithm for the travelling salesman problem

Cited by 404 publications

References 28 publications

Random Key Cuckoo Search for the Quadratic Assignment Problem

Random Key Cuckoo Search for the Quadratic Assignment Problem

Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems

Comparative analysis of Three Metaheuristics for Solving the Travelling Salesman Problem

Contact Info

Product

Resources

About