Browsing by Author "Oladele, O.T."

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    Memetic Algorithm with Multi-parent Crossover (MA-MPC) for Multi-objective Network Design.
    (The Nigerian Association of Mathematical Physics, Nigeria., 2014) Oladele, R.O.; Oladele, O.T.
    In many Evolutionary Algorithms (EAs), a crossover with two parents is commonly used to produce offsprings. Interestingly, we need not restrict ourselves to two-parent crossover since EA allows us to emulate natural evolution in a more flexible fashion. There are experimental results in the literature which show that multi-parent crossover operators can achieve better performance than traditional two-parent versions. However, most of these experimental results are based on common test functions. Experimental studies involving real-life, NP-hard problems such as network design problem are very rare. This paper presents Memetic Algorithm with Multi-Parent Crossover (MA-MPC) with a view to providing a case study of multi-parent crossover within the framework of MA for network topology design problem. Results show that MA-MPC does not always outperform MA. It depends on the size of the problem and the number parents (be it 3, 5, 7, or any other).
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    Memetic Algorithm with Population Management (MA|PM) for Multi-objective Network Design.
    (The Computer Chapter of the Institute of Electrical & Electronics Engineers (IEEE), Nigeria., 2014) Oladele, R.O.; Oladele, O.T.
    A Memetic Algorithm with Population Management (MA|PM) is employed to solve Multi-objective Network Design Problem. The algorithm was tested with three randomly generated networks of varying sizes. Results obtained were compared with the results obtained when MA was used. Overall, it was observed that MA|PM outperformed MA in terms of efficiency (computation time) for the three test problems. In addition, the results’ quality of MA|PM is superior to that of MA for 10-node network problem while it is inferior to that of MA for 36-node network problem. The results’ qualities of MA|PM and MA rank the same for 21-node network problem. The implication of these results is that MA|PM is always more efficient that MA regardless of problem size. However, the impact of population management on the effectiveness of MA is inversely proportional to the size of the problem.