Purdue University Graduate School
Final_Thesis_saif.pdf (2.16 MB)

Dynamic Electronic Asset Allocation Comparing Genetic Algorithm with Particle Swarm Optimization

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posted on 2019-01-17, 15:00 authored by Md Saiful IslamMd Saiful Islam
The contribution of this research work can be divided into two main tasks: 1) implementing this Electronic Warfare Asset Allocation Problem (EWAAP) with the Genetic Algorithm (GA); 2) Comparing performance of Genetic Algorithm to Particle Swarm Optimization (PSO) algorithm. This research problem implemented Genetic Algorithm in C++ and used QT Data Visualization for displaying three-dimensional space, pheromone, and Terrain. The Genetic algorithm implementation maintained and preserved the coding style, data structure, and visualization from the PSO implementation. Although the Genetic Algorithm has higher fitness values and better global solutions for 3 or more receivers, it increases the running time. The Genetic Algorithm is around (15-30%) more accurate for asset counts from 3 to 6 but requires (26-82%) more computational time. When the allocation problem complexity increases by adding 3D space, pheromones and complex terrains, the accuracy of GA is 3.71% better but the speed of GA is 121% slower than PSO. In summary, the Genetic Algorithm gives a better global solution in some cases but the computational time is higher for the Genetic Algorithm with than Particle Swarm Optimization.


Degree Type

  • Master of Science in Electrical and Computer Engineering


  • Electrical and Computer Engineering

Campus location

  • Indianapolis

Advisor/Supervisor/Committee Chair

Dr. Lauren A. Christopher

Additional Committee Member 2

Dr. Brian S. King

Additional Committee Member 3

Dr. Mohamed El-Sharkawy