Hameed, Asaad Shakir (2021) An enhanced metaheuristic approach to solve quadratic assignment problem using hybrid technique. Doctoral thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
The Combinatorial Optimization Problems (COPs) are very important in the branch of optimization or in the field of Operations Research (OR) in mathematics. The Quadratic Assignment Problem (QAP) is considered as one of the complex problems in COPs, and it has many of the applications in the real-life that can be modelled as QAP such as hospital facilities, campus layout, etc. The objectives of this study are to employ the best crossover operator in the Discrete Differential Evolution (DDE) algorithm. Next, handling premature convergence and stagnation issues in the DDE algorithm by enhancing it through applying Fitness Proportionate Selection (FPS) in the crossover operator stage of the DDE algorithm. Finally, the hybrid between Discrete Differential Evolution and Tabu Search (HDDETS) algorithm has been proposed in this research work to enhance the exploitation mechanism in the DDE algorithm to get a balanced hybrid algorithm in exploitation and exploration mechanisms and capacity to optimization the solutions of QAP model with a larger size within a reasonable time. The valuate of performance HDDETS algorithm comparison to existing hybrid-based algorithms, namely: Biogeography-Based Optimization Tabu Search (BBOTS), Whale Algorithm with Tabu Search (WAITS), Hybrid Ant System (HAS), Lexisearch and Genetic Algorithms (LSGA), and Golden Ball Simulated Annealing (GBSA) algorithms. Similarly, an evaluation of the optimum solution (OPT) and the best-known solution (BKS) is given. The results of these evaluations have proven that the proposed HDDETS s better than WAITS by the rates of 4.546% in the optimum solutions and 40.625% in the best-known solutions, and better than BBOTS by the rate of 60.44% in the optimum solutions and by rate 51.72% in the best- known solutions respectively. Also is better than HAS by a rate of 10 % in the optimum solutions and 72.728 % in the best-known solutions. Finally, it is better than GBSA by a rate of 7.143% in optimum solutions and by a rate of 5% in best - known solutions.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Mathematical optimization, Combinatorial optimization, Computer algorithms, Metaheuristics |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
| Divisions: | Library > Tesis > FTMK |
| Depositing User: | F Haslinda Harun |
| Date Deposited: | 29 Sep 2022 12:50 |
| Last Modified: | 29 Sep 2022 12:50 |
| URI: | http://eprints.utem.edu.my/id/eprint/26046 |
| Statistic Details: | View Download Statistic |
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