DJAYA: A discrete Jaya algorithm for solving traveling salesman problem

Gündüz, Mesut; ASLAN, Murat

doi:10.1016/j.asoc.2021.107275

Cited by 51 publications

(36 citation statements)

References 45 publications

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“…To avoid trapping into a local optimal and enhance the population diversity, the improved uniform crossover operator and neighborhood search are employed to solve TSP. Gunduz et al [39] reconstructed a discrete Jaya algorithm (DJAYA) with different swap, shift, and symmetry exchange operator combinations, and finally, the 2-Opt algorithm is employed to enhance the quality of the optimal individual in the population. Huang et al [40] proposed a discrete shuffled frog-leaping algorithm (DSFLA).…”

Section: ) Algorithms Without Decoding Methods For Tspmentioning

confidence: 99%

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

et al. 2022

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The traveling salesman problem (TSP) is one of the most extensively studied problems in the combinatorial optimization area and still presents unsolved challenges due to its NP-hard attribute. Although many real-coded algorithms are available for TSP, they still have some performance challenges in the switch from continuous space to discrete space and perform at low convergence speed. This paper proposes a real-coded carnivorous plant algorithm with a heuristic decoding method (CPA-HDM) to solve the traveling salesman problem (TSP), which exhibits good convergence speed and solution accuracy. In this improved method, a new heuristic decoding method (HDM) is designed, which helps to map continuous variables to discrete ones without losing information, maintain population diversity, and enhance the solution quality after decoding. To balance the algorithm's search capability and enhance the probability of preferable individuals generated, an adaptive attraction probability (AAP), an improved growth model of carnivorous plants (IGMOCP), and a position update method of prey (IPUMOP) are developed. Aiming to reduce the probability of premature and prevent search stagnation, an improved reproduction strategy (IRS) and an adaptive combination perturbation are reconstructed. Finally, a local search algorithm is employed to improve the exploitation capability. To verify its validation, CPA-HDM is compared with six algorithms, for solving 28 TSP instances. The simulation results and statistical analyses demonstrate the superior performance of the proposed algorithm.

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Section: ) Algorithms Without Decoding Methods For Tspmentioning

confidence: 99%

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

et al. 2022

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“…Recently, [14] resolves the cost-balanced TSP using a variable neighborhood search algorithm. [15] defines a comprehensive survey on the multiple TSP and [16] solves the TSP using discrete Jaya algorithm.…”

Section: Related Workmentioning

confidence: 99%

Optimizing the Trapezoidal Fuzzy Travelling Salesman Problem Through Dhouib-Matrix-TSP1 Method Based on Magnitude Technique

Dhouib¹,

Dhouib²

2021

IJSRMSS

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“…This algorithm and its variants have been highly successful in gradient-free global optimization, both constrained and unconstrained, of nonconvex problems in continuous domains and have seen wide applicability in diverse areas, including engineering [3]- [6], manufacturing [7], energy [8], fuel cells [9], healthcare [10] and finance [11]. Discrete (e.g., [12], [13]) and multiobjective (e.g., [14]) versions of Jaya have also been developed. A recent survey can be found in [15].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Models of Jaya and Semi-Steady-State Jaya Algorithms

Chakraborty

2022

IEEE Access

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The Jaya algorithm and its variants have enjoyed great success in diverse application areas, but no theoretical analysis of the algorithm, to our knowledge, is available in the literature. In this paper we build stochastic models for analyzing Jaya and semi-steady-state Jaya algorithms. For these algorithms, the computational cost depends on how, at each iteration, the new individual fares against the existing individual. Costs must be incurred for any replacement of individuals and the subsequent update of the population-worst individual's (and/or the population-best individual's) index. We use the following two quantities as the main metrics for analysis: the expected number of updates in a generation of the worst individual's index, and the corresponding expectation for updating the best individual's index. Clearly, the higher these expectations, the costlier the algorithm. The analysis shows that for semi-steady-state Jaya (a) the maximum expected number of worst-index updates per generation is 1.7 regardless of the population size; (b) regardless of the population size, the expectation of the number of best-index updates per generation decreases monotonically with generations; (c) exact upper bounds as well as asymptotics of the expected best-update counts can be obtained for specific distributions; the upper bound is 0.5 for normal and logistic distributions, ln 2 for the uniform distribution, and e −γ ln 2 for the exponential distribution, where γ is the Euler-Mascheroni constant; the asymptotic is e −γ ln 2 for logistic and exponential distributions and ln 2 for the uniform distribution (the asymptotic cannot be obtained analytically for the normal distribution). The models lead to the derivation of computational complexities of Jaya and semi-steady-state Jaya. The theoretical analysis is supported with empirical results on a benchmark suite.

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DJAYA: A discrete Jaya algorithm for solving traveling salesman problem

Cited by 51 publications

References 45 publications

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

Optimizing the Trapezoidal Fuzzy Travelling Salesman Problem Through Dhouib-Matrix-TSP1 Method Based on Magnitude Technique

Stochastic Models of Jaya and Semi-Steady-State Jaya Algorithms

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