Firefly-simulated annealing (F-SA) algorithm for continuous constrained optimization /
Garillos, Cinmayii Abarsolo.
Firefly-simulated annealing (F-SA) algorithm for continuous constrained optimization / Cinmayii Abarsolo Garillos. - 2011 - 112 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
It is certain that NP-hard problems frequently arise and are becoming the major concern of experts in different fields of research and industries. These problems could be in the form of continuous constrained optimization. Due to this impact, several optimization algorithms. in their pure and hybrid forms have been developed and improved to handle this kind of problems. It is in this rationale that the metaheuristic Firefly-Simulated Annealing algorithm was introduced and developed through hybridizing Firefly algorithm (FA) and Simulated Annealing algorithm (SA). The ability of Simulated Annealing algorithm to avoid a firefly from being trapped at a local minimum made it a good candidate as FA's local search. In this study, the researcher employed some parameter settings to F-SA for experimentation and four commonly used cooling schedules for reducing the randomness of FA in F-SA to improve solution quality and convergence. The researcher used some benchmarks functions which have varied characteristics to reflect wide variety of difficulties encountered when solving practical problems, to rigorously test the algorithm performance and to obtain comprehensive results. Based on the overall result of this study, F-SA algorithm, especially F-SA with a cooling schedule, is superior to FA in terms of obtaining high solution quality even in solving constrained optimization problem. However, it is recommended to improve the initialization and solution generation process of F-SA to solve multiobjective optimization problems and more constrained optimization problems with equality constraints.
Firefly-simulated Annealing (F-SA)
Algorithm.
Optimization.
Continuous constrained optimization.
Firefly algorithm.
Hybrid algorithms.
Metaheuristics.
Non-dterministic polynomial-time hard (NP-hard)
Simulated annealing.
Firefly algorithm (FA)
NP-hard problems.
Undergraduate Thesis --CMSC200,
Firefly-simulated annealing (F-SA) algorithm for continuous constrained optimization / Cinmayii Abarsolo Garillos. - 2011 - 112 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
It is certain that NP-hard problems frequently arise and are becoming the major concern of experts in different fields of research and industries. These problems could be in the form of continuous constrained optimization. Due to this impact, several optimization algorithms. in their pure and hybrid forms have been developed and improved to handle this kind of problems. It is in this rationale that the metaheuristic Firefly-Simulated Annealing algorithm was introduced and developed through hybridizing Firefly algorithm (FA) and Simulated Annealing algorithm (SA). The ability of Simulated Annealing algorithm to avoid a firefly from being trapped at a local minimum made it a good candidate as FA's local search. In this study, the researcher employed some parameter settings to F-SA for experimentation and four commonly used cooling schedules for reducing the randomness of FA in F-SA to improve solution quality and convergence. The researcher used some benchmarks functions which have varied characteristics to reflect wide variety of difficulties encountered when solving practical problems, to rigorously test the algorithm performance and to obtain comprehensive results. Based on the overall result of this study, F-SA algorithm, especially F-SA with a cooling schedule, is superior to FA in terms of obtaining high solution quality even in solving constrained optimization problem. However, it is recommended to improve the initialization and solution generation process of F-SA to solve multiobjective optimization problems and more constrained optimization problems with equality constraints.
Firefly-simulated Annealing (F-SA)
Algorithm.
Optimization.
Continuous constrained optimization.
Firefly algorithm.
Hybrid algorithms.
Metaheuristics.
Non-dterministic polynomial-time hard (NP-hard)
Simulated annealing.
Firefly algorithm (FA)
NP-hard problems.
Undergraduate Thesis --CMSC200,