Comparing The Utilization of Shuffle Frog Leaping Algorithm, Ants Colony Algorithm and Genetic Algorithm in Geometry Optimization of Morning Glory Spillway
Reza Farshad, Roozbeh Aghamajidi, Masoud Haghbin
Simulating the natural evolutionary process of living organisms and its result in solving real complex problems and engineering issues has already brought very positive results. In this study, it has been tried to suggest the most appropriate algorithm in solving a single problem that is optimizing the flow volume of morning glory spillway by investigating the efficiency of Shuffle frog leaping algorithm, (based on the theory of Lamarck), Genetics (based on Darwin's theory) and Ants colony algorithm (based on swarm intelligence). The results obtained from ant colony algorithm, genetic algorithm and Shuffle frog leaping algorithm are respectively 0.6093m3, 0.59285m3 and 0.59334m3. There is a little difference between each of the three numbers gained from the test. However, by investigating the performances of these three algorithms in a number of different repetitions and comparing the standard deviation of the objective function values in each run, it was showed that genetic algorithm is sharply sensitive to the local optimum point in small numbers of repetitions. But two other algorithms do not have this problem in small numbers of repetitions; however, as the number of repetitions and the rate of population members in search space increases, genetic algorithm seems to be more efficient than the other two algorithms and acts with a more acceptable speed. Ant's colony algorithm is much better and more efficient than Shuffle frog leaping algorithm.