Characterizing the performance of adaptive swarm-based simulated annealing algorithm
Date of Publication
2009
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Chemical Engineering
College
Gokongwei College of Engineering
Department/Unit
Chemical Engineering
Thesis Adviser
Raymond Girard Roca Tan
Defense Panel Member
Cesar A. Llorente
Florinda Tiangco Bacani
Abstract/Summary
Swarm-based simulated annealing (SBSA) is a hybrid algorithm based from simulated annealing with the incorporation of swarm intelligence to resolve the problem of slow convergence in obtaining the optimal solutions. This study focuses on the characterization of algorithm by adjusting the parameters which affect its performance in terms of accuracy, consistency, and computational efficiency. The parameters which affect the performance of the algorithm were determined by solving eleven (11) test problems with different number of variables, constraints, and mathematical operators. The parameters that were varied were the swarm size, outer loop iteration, adaptive cooling coefficient, and step size, which were further characterized by assigning low and high level values in a two-level factorial experimental design. A configuration which has a higher parameter value of swarm size and number of iterations and a lower parameter value of step size and adaptive cooling coefficient was found to produce near-optimal and consistent solutions in test problems with small number of variables, constraints, and mathematical operators. The solutions of test problems were obtained at the low level value of outer loop iteration. However, increasing the number of outer loop iterations can increase the chance of finding the global optima. Increasing the penalty function and swarm size can increase the probability of exhibiting near-optimal solution for large-scale test problems. SBSA program is not recommended to be used for large-scale test problems because it produced inaccurate and inconsistent data based from the results of the study.
Abstract Format
html
Language
English
Format
Accession Number
TU15947
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
113 leaves : ill. ; 28 cm.
Keywords
Swarm intelligence; Simulated annealing (Mathematics)
Recommended Citation
Ligon, P. C., Luna, E. B., & Siccion, R. A. (2009). Characterizing the performance of adaptive swarm-based simulated annealing algorithm. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/9125