Structural Optimization Using Multi Evolutionary System Co-Exist Genetic Algorithm

Dong Mei Cheng; Chang Hua Qiu; Cheng Yang Liu

doi:10.4028/www.scientific.net/KEM.450.556

Paper Titles

Genetic Algorithm Based Methodology for Optimal Maintenance Scheduling of Multi-Unit Systems
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A Data-Driven Neural Network Approach for Remaining Useful Life Prediction
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Sliding Mode Control with Fuzzy Tuning to Pneumatic Driving 6-DOF Parallel Robot
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Wavelet Filter Approach for GPS Attitude Tracking System on Ship
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Structural Optimization Using Multi Evolutionary System Co-Exist Genetic Algorithm
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Dynamic Sub-Population Genetic Algorithm Combined with Dynamic Penalty Function to Solve Constrained Optimization Problems
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A Skill-, Rule-, and Knowledge-Based Interaction Design Framework for Web-Based Virtual Reality Training Systems
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Application and Benefits of the Virtual Reality Technique for Product Conceptual Design
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Kinematics Simulation of the Disc-Rod Blade Variable Vector Propeller
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Structural Optimization Using Multi Evolutionary System Co-Exist Genetic Algorithm

Abstract:

Traditional genetic algorithms put all the individuals in one population to cross and adopt the same set of evolutionary parameters and genetic operators to guide the evolution, which will easily lead to local convergence and poor searching efficiency. A multi evolutionary system co-exist genetic algorithm is developed to overcome the fluctuations of the whole evolution process through dividing individuals into several sub-populations according to the fitness value. Moreover, the improved algorithm prevents the early convergent and increases the diversity of individuals by supplying these sub-populations different evolutionary systems. The effectiveness and feasibility of the algorithm are verified by typical genetic algorithm test functions and an engineering case. The results show that the genetic algorithm has a good versatility, high convergence rate and solution precision.