Velocity Restriction-Based Improvised Particle Swarm Optimization Algorithm

Mouna, H.; Azhagan, M. S. Mukhil; Radhika, M. N.; Mekaladevi, V.; Devi, M. Nirmala

doi:10.1007/978-981-10-6875-1_34

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…However, DPSO's limits do not change in scale over time and only restrict each particle's maximum viable search space for the next iteration relative to its current location [12]. Velocity restriction-based improvised PSO (VRIPSO) uses a dynamic method of limiting velocity and relies on an escape velocity mechanism to explore beyond its velocity limit [13]. Alternatively, DPSO warps individuals who appear to be stagnating, while VRIPSO occasionally allows particles to escape the imposed velocity limit.…”

Section: Related Workmentioning

confidence: 99%

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Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Schlauwitz

Musilek

2021

Applied Sciences

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This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm optimization semi-independently and compares it with the traditional particle swarm optimization. In addition, the comparison is extended to differential evolution and genetic algorithm. This presented comparative study provides a clear exposition of the effects introduced by the proposed algorithm. Performance of all evaluated optimizers is evaluated based on how well they perform in finding the global minima of 24 multi-dimensional benchmark functions, each having 7, 14, or 21 dimensions. Each algorithm is put through a session of self-tuning with 100 iterations to ensure convergence of their respective optimization parameters. The results confirm that the new variant is a significant improvement over the traditional algorithm. It also obtained notably better results than differential evolution when applied to problems with high-dimensional spaces relative to the number of available particles.

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Section: Related Workmentioning

confidence: 99%

“…Another feature found in VRIPSO is that it adjusts momentum over time [13]. Continuous PSO has a more in-depth version of this momentum feature in that it determines a gradient by which to adjust the momentum factors, encouraging movement in directions with greater improvements and not just in the current direction [14].…”

Section: Related Workmentioning

confidence: 99%

Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Schlauwitz

Musilek

2021

Applied Sciences

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“…Vaez [47] proposed a new method based on GA combined with PSO for detection of damage in thin plates based on modal data. Besides its advantages, PSO also has some limitations as it may fall into local optimum and the search precision is not too high [48][49][50]. Furthermore, the PSO algorithm is easily stuck at the boundary conditions of the mathematics objective function, and once this problem has occurred during PSO implementation, the convergence is significantly reduced.…”

Section: Introductionmentioning

confidence: 99%