Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions
In this thesis, in order to solve single objective optimization problem and bi-objective objective optimization problem in non-linear functions, two methods are created during the course of the present work. Firstly, a new strategy based on a combined method (i.e. single-objective Gravitational S...
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/83687/1/FS%202019%2027%20-%20ir.pdf http://psasir.upm.edu.my/id/eprint/83687/ |
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| Summary: | In this thesis, in order to solve single objective optimization problem and bi-objective
objective optimization problem in non-linear functions, two methods are created
during the course of the present work. Firstly, a new strategy based on a combined
method (i.e. single-objective Gravitational Search (GSA) with Bat Algorithm (BAT)
(SOGS-BAT)) algorithm is proposed in which relies on the closed interval between 0
and 1 to avoid falling into local search. The lack of local optimum mechanism
decreases the intensification of the search space, whereas diversity remains high.
Secondly, two meta-heuristics, namely, Bi-Objective Gravitational Search Algorithm
(BOGSA) and Bi-Objective Bat Algorithm (BOBAT), were combined to form a
(BOGS-BAT) algorithm. Later, this algorithm was used to solve bi-objective
Production Planning (PP) and Scheduling Problem (Sch.P).
The BOGS-BAT algorithm is based on three techniques. The first technique is to move
or switch solution from single function to functions that contain more than one
objective functions. The use of the BOGSA algorithm aims to create a new equation
for the calculation of the masses of population individuals, as found in the theoretical
work in the Strength Pareto Evolutionary Algorithm two (SPEAII) algorithm. The
second technique is to solve bi-objective functions by using the BOBAT algorithm.
The third technique is an integration of BOGSA with BOBAT to produce a BOGSBAT
algorithm. The gravitational search with BAT algorithm is used to balance
exploitation and exploration, thereby resulting in efficient and effective (speed and
accuracy) solution for the production planning model.
Finally, to verify the efficiency of the SOGS-BAT and BOGS-BAT and to
demonstrate the effectiveness and robustness of the proposed algorithms, the
numerical experiments based on benchmark test functions were performed. In
addition, the simulation random data for were used to solve single and bi-objective
optimization PP and Sch.P to improve the validation and verify the performance of the
proposed algorithms. The results reveal that the proposed algorithms are promising
and efficient. |
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