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

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

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

10.20944/preprints201905.0187.v1 ◽

2019 ◽

Author(s):

Łukasz Strąk ◽

Rafał Skinderowicz ◽

Urszula Boryczka ◽

Arkadiusz Nowakowski

Keyword(s):

Travelling Salesman Problem ◽

Particle Swarm ◽

Pso Algorithm ◽

Discrete Particle Swarm Optimization ◽

Travelling Salesman ◽

Discrete Pso ◽

Parameter Values ◽

Positive Effect ◽

Uniform Parameter

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic travelling salesman problem (DTSP). The DTSP can be modelled as a sequence of static sub-problems, each of which is an instance of the TSP. We present a method for automatically setting the values of the DPSO parameters without three parameters, which can be defined based on the size of the problem, the size of the particle swarm, the number of iterations, and the particle neighbourhood size. We show that the diversity of parameter values has a positive effect on the quality of the generated results. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

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Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

The Scientific World JOURNAL ◽

10.1155/2015/791058 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

M. Christobel ◽

S. Tamil Selvi ◽

Shajulin Benedict

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Computation Time ◽

Voltage Scaling ◽

Dynamic Voltage Scaling ◽

Energy Reduction ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle ◽

Dynamic Voltage

One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle’s best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm.

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