Dissertation > Excellent graduate degree dissertation topics show

A Multi-objective Genetic Algorithm Based on a New Model

Author: MaGuangJuan
Tutor: WangYuPing
School: Xi'an University of Electronic Science and Technology
Course: Operational Research and Cybernetics
Keywords: Evolutionary algorithm Multi-objective optimization Pareto-optimal solutions
CLC: TP18
Type: Master's thesis
Year: 2008
Downloads: 183
Quote: 4
Read: Download Dissertation

Abstract


Many real world problems are usually composed of multi-objective problems. Generally, these objects are conflict and the number of Pareto optimal solutions is usually infinite. How to find a sufficient number of uniformly and widely distributed representative Pareto optimal solutions for the decision maker is very important.Many problems which often depend on the mathematical characteristics of the objective functions can not be solved satisfactorily by the traditional approaches. Genetic algorithms have been proved efficient to many hard engineering optimization problems due to its global searching ability. Over the last 10 years, there has been an increasing interest in applying genetic algorithm to multi-objective optimization problems, and many work have been done in this field.In this thesis, the basic concepts, theories and frames of the evolutionary algorithm and the multi-objective optimization are systematically introduced firstly. Then a new measure called S-measure for the broadness of the non-dominated solution range in the objective space based on the orthogonal design is proposed. Through the introduction of the concepts of the rank variance, density variance and S-measure variance based on the rank, density and distribution of the solutions, the multi-objective optimization problem is converted into a three-objective optimization problem. For the transformed problem, a two-phase multi-objective genetic algorithm (TPMOGA) is proposed. See wheather the maximum archive number tends to M and whether the density variance and S-measure variance tends to zero as the end conditions, the algorithm tends to find good quality non-dominated solutions. In the end the convergence of TPMOGA is proved. The computer simulations demonstrate that TPMOGA can find a large number of uniformly and widely distributed Pareto-optimal solutions.

Related Dissertations

  1. Research on Subsea Pipeline Repair Coupling,TE973
  2. Evolutionary Clustering Algorithm and Its Application,TP311.13
  3. Mining resources based on genetic algorithm optimization model of,O224
  4. Study on Emergency Logistics Vehicle Routing Mode Based on the Clonal Immune Algorithm,U116.2
  5. Physiological detection for wearable wireless sensor network QoS Routing,TP212.9
  6. Wind power system with optimal operation of the unit,TM73
  7. The Study of the Scheduling Approaches in Hydrogen-network System of Refinery,TE624
  8. The Application of Intelligent Algorithm in the Network Item Bank,TP311.52
  9. Finite Element Analysis of Optimal Tunnel Cross-Section Shape,U452.2
  10. Study on Signal Control Model and Algorithm at Isolated Intersection Based on Dynamic Time-of-Day,U491.54
  11. Research on Multi-Objective Optimization for Power Dispatch Considering Energy Saving and Emission Reduction,TM73
  12. Optimality Conditions of the αk-major Efficient Solutions for Multiobjective Programming under B-invariant Convexity,O221.6
  13. The Optimization Design and Finite Element Analysis of Cycloid Ball Planetary Reducer,TH132.46
  14. Research on the Methods of Power Plant Load Dispatch,TM714
  15. Multi-objective Optimization for Rollover Safety of A Fully Integral Bus Based on Rsm,U461.91
  16. Multi-objectives Genetic Algorithm and Its Application in the Process of Electrolytic Copper,TP18
  17. SAR Image Segmentation Based on Evolutionary Computation,TN957.52
  18. Multi-objective Optimization Design of the Injection Molding Process Parameters,TP391.7
  19. The Natures of Strong Quasiconcave Function and Its Apply in Utility Function,O221.6
  20. Research on Project Logistic Alliance Management Model Based on Negotiation,F252
  21. Research and Application of Flow-Shop Scheduling Methods Based on Hybrid Quantum-Inspired Evolutionary Algorithm,TP301.6

CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
© 2012 www.DissertationTopic.Net  Mobile