A Novel Sparrow Search Algorithm for the Traveling Salesman Problem

Wu, Changyou; Fu, Xisong; Pei, Junke; Dong, Zhigui

doi:10.1109/access.2021.3128433

Cited by 53 publications

(24 citation statements)

References 94 publications

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“…Secondly, a genetic operator was utilized to update the position of each iteration, and finally the adaptive weights were employed to improve the adaptability of the algorithm. This improved algorithm outperformed other algorithms through experimental comparison in terms of accuracy, optimization speed, and stability [16]. Other similar SSA variants and related applications could be found in [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 76%

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Zhang

Zhou

et al. 2022

IEEE Access

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Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.INDEX TERMS Sparrow search algorithm, cubic chaos mapping, adaptive weighting modification, levy flight, reverse learning.

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Section: Introductionmentioning

confidence: 76%

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Zhang

Zhou

et al. 2022

IEEE Access

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“…A new greedy genetic SSA (GGSC-SSA) based on the SCA method has been suggested [ 27 ]. The greedy method is first implemented to initialize the population and boost its diversity.…”

Section: Methods Of Ssamentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

164

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Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

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“…Panwar and Deep [23] presented a discrete grey wolf optimizer (DGWO) by introducing the 2-Opt operator and hamming distance in the grey wolf optimizer. Wu et al [37] designed a new sparrow search algorithm with a greedy algorithm. In this method, several components are employed to enhance the performance of the algorithm.…”

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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A Novel Sparrow Search Algorithm for the Traveling Salesman Problem

Cited by 53 publications

References 94 publications

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Advances in Sparrow Search Algorithm: A Comprehensive Survey

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

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