Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectory Optimization

Abdelkhalik, Ossama; Gad, Ahmed

doi:10.2514/1.54330

Cited by 45 publications

(45 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For the Earth-Mars mission, the SCEA found the known optimal planet sequence (EVM) and the DSM structure. Several studies reported the same solution found in this paper [8,9]. For the JEO mission, the SCEA automatically found the gravitational-assist sequence (EVEEJ) and the DSM structure.…”

Section: Comparisons and Discussionsupporting

confidence: 60%

“…The normalized pericenter altitudes h are normalized with respect to the mean radius of the associated gravitational-assist planet R p . Using h as the design variables limits the resulting pericenter altitudes to feasible values only when dealing with different gravitational-assist planets with varied radii [9]. The ε i variable determines the epoch of a DSM i as a fraction of the transfer time of the leg.…”

Section: Cost Functionmentioning

confidence: 99%

“…In three case studies, the DSMPGA found the known optimal solutions, with improvements in some cases. The DSMPGA was demonstrated to have an advantage, as compared to parallel processing of fixed-size populations, in terms of computational cost [9]. Englander et al [10,11] presented an approach that uses nested loops composed of an outer-loop solver (that handles the solution sequence in terms of the categorical variables) and an inner-loop solver (that finds the optimal trajectory for a given sequence).…”

mentioning

confidence: 98%

“…In three case studies, the HGGA found the known optimal solutions, with improvements in some cases [8]. The second method is a dynamic-size multiple populations genetic algorithm (DSMPGA) [9]. In the DSMPGA, subpopulations of fixed-size design spaces are randomly initialized and maintained in parallel.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Structured-Chromosome Evolutionary Algorithms for Variable-Size Autonomous Interplanetary Trajectory Planning Optimization

Nyew

Abdelkhalik

Onder

2015

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

In interplanetary trajectory optimization, events such as planetary gravitational-assist maneuvers (swingbys) and deep-space maneuvers can be added/removed from the trajectory plan to reduce the cost or the flight time. This renders the number of design variables in the optimization problem variable. Global optimization methods that optimize this type of multimodal objective function can only handle problems with a fixed number of design variables. This paper presents the structured-chromosome evolutionary algorithm framework that is developed to handle variable-size design space optimization problems. In this framework, a solution (chromosome) is represented by a hierarchical data structure where the genes in the chromosome are classified as dependent and nondependent genes. This structure provides the capability to apply genetic operations between solutions of different lengths, and thus to automatically determine the number of swingbys, the planets to swingby, launch and arrival dates, and the number of deep-space maneuvers, as well as their locations, magnitudes, and directions, in an optimal sense. This new method is applied to several interplanetary trajectory design problems. Results show that solutions obtained using this tool match known solutions for some complex problems. A comparison between genetic algorithms and differential evolution in the structured-chromosome framework is presented. Nomenclature a = first selected chromosome a = thrust acceleration vector b = second selected chromosome c = third selected chromosome F = fitness (cost function) F 0 = modified fitness (cost function) f = flight direction G = derating function h = pericenter altitude, km h = normalized swingby pericenter altitude iter = iteration m = number of swingby maneuvers NR = number of runs n = number of deep-space maneuvers in a single leg R = mean radius, km r = heliocentric position vector in inertial frame, km r = spacecraft position vector r = spacecraft acceleration vector SC = success counter SR = success rate sol = solution T = time of flight, days t = Julian date tmp = temporary chromosome u = solution feature v ∞ = hyperbolic velocity vector relative to the planet, km∕s w = differential weight ΔV x = x component of impulsive maneuver velocity ΔV y = y component of impulsive maneuver velocity ΔV z = z component of impulsive maneuver velocity Δv = impulsive maneuver velocity vector, km∕s Δv T = total mission cost, km∕s ε = epoch of a deep-space maneuver as a fraction of transfer time η = swingby plane rotation angle, rad μ = gravitational constant, km 3 ∕s 2 Subscripts a = arrival d = departure l = leg number p = swingby's planet ps = powered swingby req = required s∕c = spacecraft T = total Superscripts − = incoming = outgoing

show abstract

Section: Comparisons and Discussionsupporting

confidence: 60%

Section: Cost Functionmentioning

confidence: 99%

mentioning

confidence: 98%

mentioning

confidence: 99%

See 2 more Smart Citations

Structured-Chromosome Evolutionary Algorithms for Variable-Size Autonomous Interplanetary Trajectory Planning Optimization

Nyew

Abdelkhalik

Onder

2015

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…La dimensión de la población ha sido un tema clave a considerar en la computación evolutiva [1] [41]. Un tamaño de población "pequeño" podría guiar al algoritmo a soluciones deficientes [119], y un tamaño de población "grande" podría hacer que el algoritmo gastara más tiempo de cálculo en encontrar una solución [97].…”

Section: Selección Del Tamaño De La Poblaciónunclassified