Improved and Discrete Cuckoo Search for Solving the Travelling Salesman Problem

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

doi:10.1007/978-3-319-02141-6_4

Cited by 44 publications

(21 citation statements)

References 38 publications

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“…Some of the existing hybrid heuristic optimization based approaches used for searching near optimal solution for TSPS outside those aforementioned in the previous section include: a hybrid of genetic algorithm particle swarm optimization ant colony optimization (GA-PSO-ACO) (Deng et al, 2012), adaptive simulated annealing algorithm with greedy search (ASA-GA) (Geng et al, 2011), multi-agent simulated annealing algorithm with instance-based sampling (MSA-IBS) (Wang et al, 2015), list-based simulated annealing (LBSA) (Zhan et al, 2016), invasive weed colony optimization (IWO) (Zhou et al, 2015), mosquito host-seeking algorithm (MHSA) (Feng et al, 2009), an improved discrete bat (IBA) algorithm (Osaba et al, 2016), Discrete Cuckoo Search (DCS) algorithm (Ouaarab et al, 2014), genetic simulated annealing ant colony system with particle swarm optimization (Chen and Chien, 2011) and the symbiotic organisms search (SOS) algorithm (Yu et al, 2016;Eki et al, 2015). These algorithms are problem independent and have strong global search capability, while the hybrid features allows them to easily escape from falling into local optimum.…”

Section: Related Workmentioning

confidence: 99%

Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem

Ezugwu

Adediran

Frncu

2017

Expert Systems with Applications

163

111

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Symbiotic Organisms Search (SOS) algorithm is an effective new metaheuristic search algorithm, which has recently recorded wider application in solving complex optimization problems. SOS mimics the symbiotic relationship strategies adopted by organisms in the ecosystem for survival. This paper, presents a study on the application of SOS with Simulated Annealing (SA) to solve the well-known traveling salesman problems (TSPs). The TSP is known to be NP-hard, which consist of a set of (− 1)!/2 feasible solutions. The intent of the proposed hybrid method is to evaluate the convergence behaviour and scalability of the symbiotic organism's search with simulated annealing to solve both small and large-scale travelling salesman problems. The implementation of the SA based SOS (SOS-SA) algorithm was done in the MATLAB environment. To inspect the performance of the proposed hybrid optimization method, experiments on the solution convergence, average execution time, and percentage deviations of both the best and average solutions to the best known solution were conducted. Similarly, in order to obtain unbiased and comprehensive comparisons, descriptive statistics such as mean, standard deviation, minimum, maximum and range were used to describe each of the algorithms, in the analysis section. The oneway ANOVA and Kruskal-Wallis test were further used to compare the significant difference in performance between SOS-SA and the other selected state-of-the-art algorithms. The performances of SOS-SA and SOS are evaluated on different sets of TSP benchmarks obtained from TSPLIB (a library containing samples of TSP instances). The empirical analysis' results show that the quality of the final results as well as the convergence rate of the new algorithm in some cases produced even more superior solutions than the best known TSP benchmarked results.

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Section: Related Workmentioning

confidence: 99%

Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem

Ezugwu

Adediran

Frncu

2017

Expert Systems with Applications

163

111

View full text Add to dashboard Cite

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“…To enhance the quality of search, the step length will be connected to the value given by Lévy flights as stated in the basic CS. The search space (solution space) must have an idea/knowledge of steps, and precise and constant as considered in [46].…”

Section: ) Lévy Flightsmentioning

confidence: 99%

Enhanced cuckoo search algorithm for virtual machine placement in cloud data centres

Barlaskar¹,

Singh²,

Issac³

2018

IJGUC

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In order to enhance resource utilization and power efficiency in cloud data centers it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various metaheuristic algorithms for VM placement considering the optimized energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption.. . The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool.. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA,) Optimized Firefly Search Algorithm (OFS), and Ant Colony (AC) algorithm. The comparison results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC.

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“…Some of them are cuckoo search algorithm [17][18][19][20][21], firefly algorithm [22][23][24] and harmony search (HS) algorithm [25][26][27][28]. In Table 1, the main literature is chronologically summarized.…”

Section: Introductionmentioning

confidence: 99%

Using 2-Opt based evolution strategy for travelling salesman problem

Karagül¹,

Aydemir²,

Tokat³

2016

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Abstract. Harmony search algorithm that matches the (µ+1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions.

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Improved and Discrete Cuckoo Search for Solving the Travelling Salesman Problem

Cited by 44 publications

References 38 publications

Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem

Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem

Enhanced cuckoo search algorithm for virtual machine placement in cloud data centres

Using 2-Opt based evolution strategy for travelling salesman problem

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