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Research on Multi-objective Optimization with Co-evolutionary Algorithms

Author: LiuGuoXing
Tutor: LiMinQiang
School: Tianjin University
Course: Systems Engineering
Keywords: Multi-objective optimization Genetic Algorithms Coevolution Pareto solution
CLC: TP18
Type: Master's thesis
Year: 2008
Downloads: 440
Quote: 6
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Abstract


The multi-objective optimization problem has been a difficult and hot issues in the decision sciences , has produced many of the traditional classic method , genetic algorithm is applied to multi-objective optimization problem previously , these traditional methods to solve the multi- objective optimization problem , there are many problems . The genetic algorithm has the ability to handle large problem space , and can get more than one optimal solution , does not require prior knowledge of the problem , these traditional algorithms are not available . Genetic algorithm to solve the problem , however , premature convergence and slow convergence rate has been irreconcilable contradictions , so solve high-dimensional , multi- modal complex multi-objective optimization problem . Coevolution algorithm is an evolutionary algorithm emerged in the 1990s , can well solve the contradiction of premature convergence and slow convergence , therefore the application of co-evolutionary algorithm to solve the multi-objective optimization problem , the trend of the development of this area . Coevolution and multi-objective optimization problem based on extensive and in-depth literature in-depth research and analysis , the following major elements : a simple review of the multi-objective optimization problem , and a brief introduction to traditional solve multi-objective optimization problems , and also pointed out the problems of these traditional methods , a brief review of the emergence and development of the genetic algorithm , and genetic algorithm based knowledge and theory is described in detail . Review the traditional genetic algorithm to solve the multi-objective optimization problem , simple evaluation and comparison made ??of these algorithms , and proposed the revelation of these traditional genetic algorithm to other algorithms . Analysis of the emergence and development of the co-evolutionary algorithm , and the type of cooperation and competition type to the idea of co - evolutionary algorithm . Cooperative and competitive two co-evolutionary algorithm to solve the multi- objective optimization problem , and according to the algorithm running , the improvement strategies . Using two co-evolutionary algorithms and MOGA algorithm , tested on six test functions from two indicators of the performance of the algorithm , the experimental results show that the co-evolution algorithm is better than traditional genetic algorithm search capabilities .

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