Many real world optimization problems are difficult to solve, therefore, when solving such problems it is wise to employ efficient optimization algorithms which are capable of handling the problem complexities and of finding optimal or near optimal solutions within a reasonable time and without using excessive computational resources. The objective of this research is to develop Differential Evolution (DE) algorithms with improved performance capable of solving difficult and challenging global constrained and unconstrained optimization problems, as well as extending the application of the these algorithms to real-world optimization problems, particularly wireless broadband network placement and deployment problems. The adaptation of DE control parameters has also been investigated and a novel method using Mann-Iteration and Tournament scoring is proposed to improve the performance of the algorithm. A novel constraint handling technique called neighborhood constraints handling (NCR) method has been also proposed. A set of experiments are conducted to comprehensively test the performance of the proposed DE algorithms for global optimization. The numerical results for well-known optimization global optimization test problems are shown to prove the performance of the proposed methods. In addition, a novel wireless network test point (TP) reduction algorithm (TPR) has been presented. The TPR algorithm and the proposed DE algorithms have been applied for solving the optimal network placement problem. In order to utilize the value of flexibility a novel value optimization problem formulation integrating the state of the art approaches of cash flow (CF) analysis and real option analysis (ROA) for network deployment has been presented, utilizing the proposed DE algorithms to obtain the optimal roll-out sequence that maximizes the value of the wireless network deployment. A numerical experimentation, based on a case study scenario of an optimal network placement and deployment for wireless broadband access network, has been conducted to confirm the efficiency of these algorithms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:602400 |
| Date | January 2013 |
| Creators | Farah, Abdulkadir |
| Publisher | University of Reading |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
Page generated in 0.0061 seconds