Performance Analysis of Chaos Optimization Algorithm

The main objective of modern optimization algorithm is to deal with the problem of global optimum, which is essentially probabilistic. Many new global algorithms have been put forward such as TABU, simulated annealing algorithm (SAA), genetic algorithm (GA), evolutionary strategy (ES) and ant colony algorithm (ACA), which benefit from many natural phenomena. The ergodicity of chaos is a new and interesting property for optimization, of which the main idea is to search according to a series of successive fields: first search in the total field, then in a smaller one, and so on. The most famous phenomena of chaos is described by Logistic equation, which have been studied and applied to solve practical problems by many scholars. However, it has some obvious drawbacks in comparison with the ordinary random searching: chaos mainly searches on the edge of searching field, it is not as well as random in homogeneity, so there are apparent differences between chaos optimization and random optimization. The conclusion is confirmed by simulation. Therefore it is of great importance to notice the difference when applying Logistic equation in global optimization.