A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

Strąk, Łukasz; Skinderowicz, Rafał; Boryczka, Urszula; Nowakowski, Arkadiusz

doi:10.20944/preprints201905.0187.v1

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…Every successive iteration (

, …) is generated from the previous one by modifying positions of selected vertices; about 3% of vertices randomly change their positions in the next iteration. The same DSTP benchmark instances (created by the authors of [ 34 ]) were used for all algorithms to ensure even-handed comparison. The benchmark instances as well as the results are available for download at .…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Stodola

Michenka

Nohel

et al. 2020

Entropy

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The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

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“…Every successive iteration (

Section: Experiments and Resultsmentioning

confidence: 99%

“…The discrete particle swarm optimization approach was adapted for the DTSP by Strak et al [ 34 ]. The authors proposed their PSO algorithm with heterogeneous (non-uniform) parameter values; the parameters are set automatically for the critical PSO parameters based on discrete probability distributions.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Stodola

Michenka

Nohel

et al. 2020

Entropy

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“…According to the different stages of the path planning algorithm development, the algorithms can be divided into two categories: fast-exploring random tree method [ 8 ], artificial potential field method [ 9 ], the visible method [ 10 ], A* algorithm [ 11 ] as representative traditional algorithms. Intelligent algorithms represented by genetic algorithm [ 12 ], ant colony algorithm [ 13 ], particle swarm algorithm [ 14 ], immune cloning algorithm [ 15 ]. In [ 8 ], Janson theoretically proved that the use of deterministic low-dispersion sampling plan usually makes the RRT algorithm display superior performance.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 13 ], Beschi used an ant colony algorithm to solve complex motion planning problems after discretizing the task space. In [ 14 ], Strąk proposed a discrete particle swarm optimization algorithm to solve the dynamic traveling salesman problem. The algorithm can automatically set the parameter values of the discrete particle swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm

Hao

Zhao

et al. 2020

Sensors

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In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such as premature maturity, low convergence path quality, poor population diversity, and difficulty in breaking the local optimal solution, this paper proposes a multi-population migration genetic algorithm. The multi-population migration genetic algorithm randomly divides a large population into several small with an identical population number. The migration mechanism among the populations is used to replace the screening mechanism of the selection operator. Operations such as the crossover operator and the mutation operator also are improved. Simulation results show that the multi-population migration genetic algorithm (MPMGA) is not only suitable for simulation maps of various scales and various obstacle distributions, but also has superior performance and effectively solves the problems of the standard genetic algorithm.

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“…This modern optimization technique was first proposed by Kennedy in 1995 and it was inspired by the behavior of organisms [ 30 ]. This optimization tuner is characterized by fast convergence, the efficiency of computation and it has the capability to find local and global solutions [ 31 , 32 ]. Other modern and generalized optimization techniques can be employed either to improve the optimization process or to make a comparison in performance among each other [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

Humaidi

Ibraheem

Azar

et al. 2020

Entropy

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This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

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A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

Cited by 10 publications

References 7 publications

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

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