Aerospace and Electronic Systems - October 2018 - 44
Performance Comparison of Particle Swarm Optimization and Cuckoo Search for Online Route Planning
Figure 5.
Dijkstra algorithm.
n
n
Equations (17) and (18) present the speed v j and position x j
respectively for the jth particle and n represents the iteration so far.
(
)
(
v nj = ϕ1v nj −1 + α1ϕ 2 ψ g − x nj −1 + α 2ϕ3 ψ j − x nj −1
x nj = x nj −1 + v nj
)
(17)
(18)
where φ1 is the inertia coefficient in the range [0, 1], φ2 and φ3 is the
trust coefficient for self and group experience, ψg is the best group
known position, ψj is the best-known particle position, and α1 and
α2 ∼ N(0, 1) is a random number with Gaussian distribution. Note
that in (18), it is assumed a time interval of 1 iteration between
successive updates of the position vector xj.
In the context of online route planning, each particle corresponds to a geographic coordinate and the fitness of each particle is evaluated with the objective function defined in (3). The
algorithm starts with a cloud of particles that gradually converge
to an optimal solution [4]. Figure 4 shows the evolution in the
personal and social behaviour of the particles. In a first stage
in Figure 4a, particles explore the search space guided more
by their self-experience than the group experience. In a second
stage in Figure 4b, when global optimal and local optimal solutions start to be discovered by the particles, the group converges
towards these solutions. Finally, in a third stage in Figure 4c,
all particles have converged to a small region where in some
point the stop condition will be fulfilled. Stop condition can be
reached through a maximum number of iteration or by reaching the reference error. It is important to note that PSO, being
a heuristic method, will not have a predictable that allows to
determine a given number of iterations to converge.
DIJKSTRA ALGORITHM
Figure 4.
Particles social behavior.
44
The Dijkstra algorithm is an offline route planning method that
explores the shortest paths between the departure node and all
the other paths leading all the vertexes to reach the destination
node. When the shortest path from departure to destination is
reached, the algorithm stops. Dijkstra is defined as a Si = (V,
A) graph where V is a set of elements called vertexes, A is a set
of edge pairs (a, b) corresponding to each one of the vertexes
IEEE A&E SYSTEMS MAGAZINE
OCTOBER 2018
Aerospace and Electronic Systems - October 2018
Table of Contents for the Digital Edition of Aerospace and Electronic Systems - October 2018
Contents
Aerospace and Electronic Systems - October 2018 - Cover1
Aerospace and Electronic Systems - October 2018 - Cover2
Aerospace and Electronic Systems - October 2018 - Contents
Aerospace and Electronic Systems - October 2018 - 2
Aerospace and Electronic Systems - October 2018 - 3
Aerospace and Electronic Systems - October 2018 - 4
Aerospace and Electronic Systems - October 2018 - 5
Aerospace and Electronic Systems - October 2018 - 6
Aerospace and Electronic Systems - October 2018 - 7
Aerospace and Electronic Systems - October 2018 - 8
Aerospace and Electronic Systems - October 2018 - 9
Aerospace and Electronic Systems - October 2018 - 10
Aerospace and Electronic Systems - October 2018 - 11
Aerospace and Electronic Systems - October 2018 - 12
Aerospace and Electronic Systems - October 2018 - 13
Aerospace and Electronic Systems - October 2018 - 14
Aerospace and Electronic Systems - October 2018 - 15
Aerospace and Electronic Systems - October 2018 - 16
Aerospace and Electronic Systems - October 2018 - 17
Aerospace and Electronic Systems - October 2018 - 18
Aerospace and Electronic Systems - October 2018 - 19
Aerospace and Electronic Systems - October 2018 - 20
Aerospace and Electronic Systems - October 2018 - 21
Aerospace and Electronic Systems - October 2018 - 22
Aerospace and Electronic Systems - October 2018 - 23
Aerospace and Electronic Systems - October 2018 - 24
Aerospace and Electronic Systems - October 2018 - 25
Aerospace and Electronic Systems - October 2018 - 26
Aerospace and Electronic Systems - October 2018 - 27
Aerospace and Electronic Systems - October 2018 - 28
Aerospace and Electronic Systems - October 2018 - 29
Aerospace and Electronic Systems - October 2018 - 30
Aerospace and Electronic Systems - October 2018 - 31
Aerospace and Electronic Systems - October 2018 - 32
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Aerospace and Electronic Systems - October 2018 - 64
Aerospace and Electronic Systems - October 2018 - Cover3
Aerospace and Electronic Systems - October 2018 - Cover4
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