Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach

Table 9

Summary of parameter variation optimization results.

Variation	Percent coverage	Average revisit time	Computational load	Pareto front
Increasing number of populations	Better up to 29%	Better up to 54%	Worse up to 31%	Increasing number of solutions in Pareto front
Increasing number of generations	Better up to 48%	Better up to 60%	Worse up to 206%	N/A
Increasing number of populations & generations	Better up to 71%	Better up to 69%	Worse up to 293%	Increasing number of solutions in Pareto front
Using a variation of , , , , and	Better up to 33%	Better up to 82%	N/A	N/A
Using a variation of , , and	Better up to 5%	Better up to 61%	N/A	N/A
Using a variation of and	Better up to 29%	Better up to 70%	N/A	N/A
Fuel penalty used	Better up to 38%	Better up to 54%	N/A	N/A
Semisparse constellation (Cases & )	Better up to 25%	Better up to 61%	N/A	N/A
Increasing number of satellites in constellation	Same value	Better up to 54%	Worse up to 102%	N/A