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Page 342 Sellers et al. Intell Robot 2022;2(4):333­54 I http://dx.doi.org/10.20517/ir.2022.21

Algorithm 1: Improved PSO (IPSO) algorithm for waypoint sequencing
Initialize a population of particles
Set the size of the swarm to , the maximum number of iteration .
for i=1 to do
Initialize within the search range of ( , ) frandomly;
Initialize within the velocity range of ( , ) randomly;
= ;
end
Evaluate each ;
Identify the best position ;
while (a stop criterion is not satisfied & t < ) do
for p=1 to do
Update 1 and 2 by Equation (8), Equation (9), and Equation (10);
= ∑ ¯ ( )

=1
end
; // The sum of particle weights
( )
∑
¯ = ˆ / ˆ ; // Average the weights together
=1

max 1≤ ≤ (F( ( )))−F( ( ))+

ˆ = max 1≤ ≤ (F( ( )))−min 1≤ ≤ (F( ( )))+ ; // Calculate the weights of each

particle

(t+1)= (t)+ 1 1 [ (t)- (t)]+ 2 2 [ (t)- (t)]; // Update the fitness

value for each particle
+1 = +1 + ; // Add the fitness value to the particle position
+1

, +1 ,
= ; // Store the initial local best position
, +1 ,
=
end
; // Store the initial global best position
Evaluate F( +1 );

, +1 +1
if F( ) < F( ) then
, +1
Update ; // Update the new local best position
end
, +1 , +1
if F( ) < F( ) then
, +1
Update ; // Update the new global best position
end
workspace through the distance of the edge list E. Nevertheless, some edge distances cannot represent the
sparseness of the entire workspace, such as the edges enclosed by the red dotted line in Figure 6B.