An Enhancement of Artificial Bee Colony Algorithm applied in numerical function optimization Fajardo, Salvador V. and Serrano, Blesseda Maria Leah P. 6
By: Fajardo, Salvador V. and Serrano, Blesseda Maria Leah P. 4 0 16 [, ] | [, ] |
Contributor(s): 5 6 [] |
Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; March 2015.46Edition: Description: 28 cm. 121 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:| Item type | Current location | Home library | Collection | Call number | Status | Date due | Barcode | Item holds |
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| Book | PLM | PLM Archives | Filipiniana-Thesis | QA76.9 F34 2015 (507 } (Browse shelf) | Available | FT6098 |
Undergraduate Thesis (BSCS major in Computer Science) Pamantasan ng Lungsod ng Maynila, 2015.;ABSTRACT: Selection of best element from set of available alternatives is what we call Numerical Function Optimization. In this case, the function undergoes process of maximizing and minimizing by systematically choosing input values from within an allowed set and computing the value of the function. In this study, Artificial Bee Colony (ABC) Algorithm was enhanced and applied in Numerical Function Optimization. In the enhanced ABC Algorithm, three modifications are used to progress its search ability in finding solutions. Four benchmark functions are used in the existing ABC Algorithm to obtained solutions. These benchmark functions help us to prove that the process enclosing in the existing ABC Algorithm need improvement. We aim to improve the exploitation and exploration process of the algorithm by applying the Self-Adaptive Multi-method Search. This multi-method search employs a population based search strategy to find the local optimum of a function. Thus, this helps the algorithm to find solutions that are close the optimum solution of a given function. Another is the previous solutions generated by the ABC Algorithm are again generated and by using Multi-start method those repeated solutions will be reduced. The objective of this multi-start method is to minimize the number of repetive solutions on a given population of the function. Lastly, because of randomization that was adopted in the process of ABC Algorithm, it does not use any information in comparing food sources (solutions). We applied meta-heuristic method to find a better way of comparing the obtained solutions to produce the best overall which is the objective of the ABC Algorithm. These modification process conducted by the researchers helps the algorithm to find the best overall which is the objective of the ABC Algorithm. These modification process conducted by the researchers helps the algorithm to find the best overall solution of a numerical function and producing solutions near its global optimum. 56
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