A Novel Sparrow Search Algorithm for the Traveling Salesman Problem

The sparrow search algorithm (SSA) tends to fall into local optima and to have insufficient stagnation when applied to the traveling salesman problem (TSP). To address this issue, we propose a novel greedy genetic sparrow search algorithm based on a sine and cosine search strategy (GGSC-SSA). First,...

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Autores principales:	Changyou Wu, Xisong Fu, Junke Pei, Zhigui Dong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Sparrow search algorithm traveling salesman problem greedy algorithm genetic operators sin-cosine search strategy combinatorial optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/4b882aeb627f4f0ba0a045965be1a7b6
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id	oai:doaj.org-article:4b882aeb627f4f0ba0a045965be1a7b6
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spelling	oai:doaj.org-article:4b882aeb627f4f0ba0a045965be1a7b62021-11-24T00:03:03ZA Novel Sparrow Search Algorithm for the Traveling Salesman Problem2169-353610.1109/ACCESS.2021.3128433https://doaj.org/article/4b882aeb627f4f0ba0a045965be1a7b62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615156/https://doaj.org/toc/2169-3536The sparrow search algorithm (SSA) tends to fall into local optima and to have insufficient stagnation when applied to the traveling salesman problem (TSP). To address this issue, we propose a novel greedy genetic sparrow search algorithm based on a sine and cosine search strategy (GGSC-SSA). First, the greedy algorithm is introduced to initialize the population and to increase the diversity of the population. Second, genetic operators are used to update the population, balancing global search and local development capabilities. Finally, the adaptive weight is introduced in the producer update to increase the adaptability of the algorithm and to optimize the quality of the solution, and a sin-cosine search strategy is introduced to update the scroungers. In addition, the GGSC-SSA is compared with the genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), grey wolf optimization (GWO), ant colony optimization (ACO) and the artificial fish (AF) algorithm on TSP datasets for performance testing. We also compare it with some recently improved algorithms. The results of the simulations are encouraging; the GGSC-SSA significantly enhances the solution precision, optimization speed and robustness.Changyou WuXisong FuJunke PeiZhigui DongIEEEarticleSparrow search algorithmtraveling salesman problemgreedy algorithmgenetic operatorssin-cosine search strategycombinatorial optimizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153456-153471 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sparrow search algorithm traveling salesman problem greedy algorithm genetic operators sin-cosine search strategy combinatorial optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Sparrow search algorithm traveling salesman problem greedy algorithm genetic operators sin-cosine search strategy combinatorial optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Changyou Wu Xisong Fu Junke Pei Zhigui Dong A Novel Sparrow Search Algorithm for the Traveling Salesman Problem
description	The sparrow search algorithm (SSA) tends to fall into local optima and to have insufficient stagnation when applied to the traveling salesman problem (TSP). To address this issue, we propose a novel greedy genetic sparrow search algorithm based on a sine and cosine search strategy (GGSC-SSA). First, the greedy algorithm is introduced to initialize the population and to increase the diversity of the population. Second, genetic operators are used to update the population, balancing global search and local development capabilities. Finally, the adaptive weight is introduced in the producer update to increase the adaptability of the algorithm and to optimize the quality of the solution, and a sin-cosine search strategy is introduced to update the scroungers. In addition, the GGSC-SSA is compared with the genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), grey wolf optimization (GWO), ant colony optimization (ACO) and the artificial fish (AF) algorithm on TSP datasets for performance testing. We also compare it with some recently improved algorithms. The results of the simulations are encouraging; the GGSC-SSA significantly enhances the solution precision, optimization speed and robustness.
format	article
author	Changyou Wu Xisong Fu Junke Pei Zhigui Dong
author_facet	Changyou Wu Xisong Fu Junke Pei Zhigui Dong
author_sort	Changyou Wu
title	A Novel Sparrow Search Algorithm for the Traveling Salesman Problem
title_short	A Novel Sparrow Search Algorithm for the Traveling Salesman Problem
title_full	A Novel Sparrow Search Algorithm for the Traveling Salesman Problem
title_fullStr	A Novel Sparrow Search Algorithm for the Traveling Salesman Problem
title_full_unstemmed	A Novel Sparrow Search Algorithm for the Traveling Salesman Problem
title_sort	novel sparrow search algorithm for the traveling salesman problem
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/4b882aeb627f4f0ba0a045965be1a7b6
work_keys_str_mv	AT changyouwu anovelsparrowsearchalgorithmforthetravelingsalesmanproblem AT xisongfu anovelsparrowsearchalgorithmforthetravelingsalesmanproblem AT junkepei anovelsparrowsearchalgorithmforthetravelingsalesmanproblem AT zhiguidong anovelsparrowsearchalgorithmforthetravelingsalesmanproblem AT changyouwu novelsparrowsearchalgorithmforthetravelingsalesmanproblem AT xisongfu novelsparrowsearchalgorithmforthetravelingsalesmanproblem AT junkepei novelsparrowsearchalgorithmforthetravelingsalesmanproblem AT zhiguidong novelsparrowsearchalgorithmforthetravelingsalesmanproblem
_version_	1718416090582745088

A Novel Sparrow Search Algorithm for the Traveling Salesman Problem

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