Perpertual Coupled Simulated Annealing for Continuous Optimization
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2018
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| _version_ | 1866916623132131328 |
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| author | Gonçalves-e-Silva, Kayo Xavier-de-Souza, Samuel |
| author_facet | Gonçalves-e-Silva, Kayo Xavier-de-Souza, Samuel |
| contents | Global optimization heuristics are popular to optimize hard non-convex problems. Despite their irrefutably large cost-to-solution, in the lack of other working greedy or convex approaches, global optimization algorithms remain the no-brainer choice. Nevertheless, successful use often requires tedious adjustments to initial parameters to avoid premature convergence. The Coupled Simulated Annealing approach proposed a method based on the coupling of multiple optimizers to escape premature convergence, having achieved success in optimizing hyperparameters of many applications of machine learning; however, a careful choice of the generation temperature is still required. In this paper we propose the Perpetual Orbit technique as a solution to control the generation temperature and avoid search stagnation. In principle, this technique can also be applied to other ensemble- and population-based algorithms that have a dispersion variable. The results of our experiments show superior performance when using the proposed technique because it makes the Couple Simulated Annealing totally parameter-free and capable of reaching equal or better solutions in more than 85\% cases across all functions and competitor methods analyzed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1803_01059 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Perpertual Coupled Simulated Annealing for Continuous Optimization Gonçalves-e-Silva, Kayo Xavier-de-Souza, Samuel Optimization and Control 90C30, 90C56, 90C59 Global optimization heuristics are popular to optimize hard non-convex problems. Despite their irrefutably large cost-to-solution, in the lack of other working greedy or convex approaches, global optimization algorithms remain the no-brainer choice. Nevertheless, successful use often requires tedious adjustments to initial parameters to avoid premature convergence. The Coupled Simulated Annealing approach proposed a method based on the coupling of multiple optimizers to escape premature convergence, having achieved success in optimizing hyperparameters of many applications of machine learning; however, a careful choice of the generation temperature is still required. In this paper we propose the Perpetual Orbit technique as a solution to control the generation temperature and avoid search stagnation. In principle, this technique can also be applied to other ensemble- and population-based algorithms that have a dispersion variable. The results of our experiments show superior performance when using the proposed technique because it makes the Couple Simulated Annealing totally parameter-free and capable of reaching equal or better solutions in more than 85\% cases across all functions and competitor methods analyzed. |
| title | Perpertual Coupled Simulated Annealing for Continuous Optimization |
| topic | Optimization and Control 90C30, 90C56, 90C59 |
| url | https://arxiv.org/abs/1803.01059 |