Application Of Particle Swarm Optimization To
The Item Packing Problem
Author(s)
Y.-B. Shin & E. Kita
Abstract
The item packing problem is a class of optimization problems which involve
attempting to pack items together inside a container, as densely as possible without
the item overlap. This research focuses on the application of Particle Swarm
Optimization (PSO) to the item packing problem in the two-dimensional region.
PSO has the potential solutions of the problem as particles. Particles in the
swarm are updated according to the update rule with the velocity and position
vectors. The position vectors of the item centers are taken as the design variables.
The total number of items is maximized when all items are included inside a
container without the item overlap. In the original PSO, the particle position vector
is updated with the best position in all particles; i.e., global best position, and the
local best position in previous positions of each particle; i.e., local best position.
The present PSO algorithm utilizes, in addition to them, the second best position
in all particles; i.e., global second-best position.
In the numerical example, the present algorithm is applied to the item packing
problem within the two-dimensional region. The region figure is not regular and
the square items are packed in the region. The comparison of the original and the
present PSOs show that the present algorithm can find a better solution than the
original PSO.
Keywords: particle swarm optimization, item packing problem, global best
position, second global best position.
1 Introduction
Evolutionary computations are techniques implementing mechanisms inspired
by biological evolution such as reproduction, mutation, recombination, natural
selection and survival of the fittest; Genetic Algorithms [1–3], Simulated
Annealing [4], Evolutionary Programming [5], Genetic Programming [6, 7],
Particle Swarm Optimization [8, 9] and so on.
Keywords
particle swarm optimization, item packing problem, global best
position, second global best position.
Related Book
Other papers in this volume
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