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Original Article
Musca Optimizer: A Perturbation-Based Escape Algorithm for Gradient Descent
Milind K. Patil1
1 Syncaissa Systems Inc. USA.
Published Online: November-December 2025
Pages: 59-65
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506010
References
1. S. Ruder, An overview of gradient descent optimization algorithms, arXiv preprint arXiv: 1609.04747, 2016.
2. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by simulated annealing, Science, vol. 220, no. 4598, pp. 671 680, 1983.
3. J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95, vol. 4, pp. 1942 1948, IEEE, 1995.
4. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison- Wesley, 1989.
5. L. Bottou, Large-scale machine learning with stochastic gradient descent, in Proceedings of COMPSTAT'2010, pp. 177 186, Springer, 2010.
2. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by simulated annealing, Science, vol. 220, no. 4598, pp. 671 680, 1983.
3. J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95, vol. 4, pp. 1942 1948, IEEE, 1995.
4. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison- Wesley, 1989.
5. L. Bottou, Large-scale machine learning with stochastic gradient descent, in Proceedings of COMPSTAT'2010, pp. 177 186, Springer, 2010.
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