Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

Abstract

Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of difficulty, which are amenable to the use of alternative search techniques such as metaheuristics, since they require little domain information to operate. From the several metaheuristics available, multiobjective evolutionary algorithms (MOEAs) have become particularly popular, mainly because of their availability, ease of use, and flexibility. This paper presents a taxonomy and a comprehensive review of applications of MOEAs in aeronautical and aerospace design problems. The review includes both the characteristics of the specific MOEA adopted in each case, as well as the features of the problems being solved with them. The advantages and disadvantages of each type of approach are also briefly addressed. We also provide a set of general guidelines for using and designing MOEAs for aeronautical and aerospace engineering problems. In the final part of the paper, we provide some potential paths for future research, which we consider promising within this area.

From evolutionary computation to the evolution of things

“…Population-based methods such as EAs have proven effective at meeting the challenges of this combination. In particular, so-called multi-objective EAs, change the selection function to explicitly reward diversity, so that they discover and maintain high-quality solutions representing different trade-offs between objectives -technically they approximate diverse segments of the Pareto front 28 . Many examples can also been found in bio-informatics.…”

Hybrid adaptive evolutionary algorithm based on decomposition

“…They arise in various applications including in air traffic routing, the design of telephone networks, electrical and hydraulic applications, cable TV and computer systems, road networks and other. Continuous optimization is widely used in mechanical design, chemical engineering, economics, finance, agriculture and the food industry, to name a few, [57,35,8,1,6].…”

“…, λ N be a set of N weight vectors and z * j = min{ f j (x)|x ∈ Ω} be the reference point. We use the Tchebycheff Aggregation Function [49] to transform the approximated PF of problem (1) into N scalar optimization subproblems whose j th subproblem is as follows:…”

A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite

“…The second objective, which is added in order to ensure the fairness, amounts to minimize the maximum profit difference between users. Some researchers studied multi-objective optimization problems for space applications [1][10] [30]. Furthermore, some literature considered as an objective the fairness among users [20].…”