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A Class of Novel Evolutionary Algorithms for Multi-modal Functions

Author: YiMeiXiang
Tutor: QuanHuiYun
School: Hunan Normal University
Course: Basic mathematics
Keywords: Guo Tao algorithm Particle Swarm Optimization Chaos Optimization Population divided Multimodal function
CLC: O224
Type: Master's thesis
Year: 2006
Downloads: 88
Quote: 1
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Many real problem ultimately boils down to optimization problems , optimization problems in various fields . The optimization problems predecessors has come up with a lot of classic numerical methods can really get good results on certain issues . But there are some shortcomings of these traditional optimization methods : strong restrictions on the objective function , such as continuous and differentiable , etc., the strong dependence of the problem , the algorithm results with the choice of the initial value , and is easy to fall into the local the disadvantage of the minimum value . Flourished in recent years evolutionary algorithm used to optimize the field , its global optimality parallelism efficiency in function optimization of a wide range of applications . More clues to the evolutionary algorithm to overcome the disadvantages of traditional numerical methods , with the evolution of the nature , rather than a single clue to the global optimization methods , populations and random search mechanism . Evolutionary computation in the field of optimization applications caused widespread concern , various forms of evolutionary algorithm endless . Guo Tao algorithm and particle swarm algorithm is a typical representative of which , they have the advantages of high efficiency and fast . Existing function optimization studies mostly for unimodal function optimization problems , but in real life , a lot of math , engineering problems are multimodal function optimization problems, such as neural network structure and weights optimization problem, the complexity of the system parameters and structural identification problem . This problem , of course, can be used several optimization calculation until the discovery of all the peaks , but this is not only a waste of time , and with no guarantee of convergence to a number of different peak . We first chaotic mechanism introduced Guo Tao algorithm to further improve its local search capability . Then the particle swarm algorithm to transform , making it from being able to quickly solving a single optimal solution to multiple global optimal solution and achieve better results . Finally, we combine the GT and the PSO algorithm , subregional solving complex problems all global optimal solution , the effect is very good . For each improvement strategy , we are given examples and analysis .

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CLC: > Mathematical sciences and chemical > Mathematics > Operations Research > Optimization of the mathematical theory
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