An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

Wu, Changyou; Fu, Xisong

doi:10.1109/access.2020.3035899

Cited by 24 publications

(4 citation statements)

References 71 publications

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“…To demonstrate the performance of CPA-HDM, 28 instances with cities from 29 up to 1084, have been selected and compared with six algorithms in the literature, namely: D-GWO [23], DSFLA [54], DBAL [38], agglomerative greedy brain storm optimization algorithm (AGBSO3) [55],…”

Section: ) Cpa-hdm Compared With Other Participating Algorithms and A...mentioning

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: ) Cpa-hdm Compared With Other Participating Algorithms and A...mentioning

confidence: 99%

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

et al. 2022

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“…The main issue in the TSP case is how the seller can design his itinerary to visit multiple cities, where the distance from one city to another is known to reach a minimum total distance, and the seller can only visit that city once. There are various methods to solve the TSP problem, including the greedy algorithm (Wu & Fu, 2020), brute force algorithm (Violina, 2021), hillclimbing method (Fronita, Gernowo, & Gunawan, 2018), ant algorithm (Chen, Tan, Qian, & Chen, 2018), and genetic algorithm. The problem faced in TSP is how to find the minimum total distance.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Adaptive Genetic Algorithm Parameters in Traveling Salesman Problem

Herdiana

Candiasa

Indrawan

2022

CNAHPC

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The TSP problem is one where a seller visits multiple destinations at the same time and they are only allowed to visit once. The purpose of this TSP is to shorten the shortest distance, thereby minimizing time and cost. There are various methods to solve the TSP problem, including greedy algorithm, brute force algorithm, hill climbing method, ant algorithm, and genetic algorithm. Each process in a genetic algorithm is affected by several parameters, including population size, maximum generation, crossover rate, and mutation rate. The purpose of this study is to apply genetic algorithms to the traveling salesman problem optimization, calculate the maximum influence of generation, chromosome number, crossover rate and mutation rate on the optimal genetic algorithm, calculate the range of chromosome number, population size, crossover rate and mutation rate for genetic algorithm in the traveling salesman problem and calculate the effect of adaptive genetic algorithm parameters on genetic algorithm results. Based on the results obtained from research and testing, the four parameters of the genetic algorithm are positively correlated with fitness results while negatively correlated with execution time performance where each adaptive parameter applied provides more optimal fitness results than static parameters. The four adaptive parameters that are applied together give optimal results, both fitness which reaches 1.0% and time reaches 38.7%.

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“…A.C. Cinar et al presented the initial solution of the tree during the initialization phase using the nearest neighbor and a random approach for balancing the speed and quality of its solution [6]. C. Wu et al [28] and P. Guo et al [29] adopted a greedy strategy for generating initialized populations to improve the optimization speed and avoid falling into a local optimum.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Discrete Group Teaching Optimization Algorithm for TSP Path Planning with Unmanned Surface Vehicles

Yang

Huang

et al. 2022

JMSE

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A growing number of researchers are interested in deploying unmanned surface vehicles (USVs) in support of ocean environmental monitoring. To accomplish these missions efficiently, multiple-waypoint path planning strategies for survey USVs are still a key challenge. The multiple-waypoint path planning problem, mathematically equivalent to the traveling salesman problem (TSP), is addressed in this paper using a discrete group teaching optimization algorithm (DGTOA). Generally, the algorithm consists of three phases. In the initialization phase, the DGTOA generates the initial sequence for students through greedy initialization. In the crossover phase, a new greedy crossover algorithm is introduced to increase diversity. In the mutation phase, to balance the exploration and exploitation, this paper proposes a dynamic adaptive neighborhood radius based on triangular probability selection to apply in the shift mutation algorithm, the inversion mutation algorithm, and the 3-opt mutation algorithm. To verify the performance of the DGTOA, fifteen benchmark cases from TSPLIB are implemented to compare the DGTOA with the discrete tree seed algorithm, discrete Jaya algorithm, artificial bee colony optimization, particle swarm optimization-ant colony optimization, and discrete shuffled frog-leaping algorithm. The results demonstrate that the DGTOA is a robust and competitive algorithm, especially for large-scale TSP problems. Meanwhile, the USV simulation results indicate that the DGTOA performs well in terms of exploration and exploitation.

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An Agglomerative Greedy Brain Storm Optimization Algorithm for Solving the TSP

Cited by 24 publications

References 71 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

Optimization of Adaptive Genetic Algorithm Parameters in Traveling Salesman Problem

A Novel Discrete Group Teaching Optimization Algorithm for TSP Path Planning with Unmanned Surface Vehicles

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