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Adaptive Particle Swarm Optimization

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An adaptive particle swarm optimization (APSO)
that features better search efficiency than classical particle swarm
optimization (PSO) is presented. More importantly, it can per-
form a global search over the entire search space with faster
convergence speed. The APSO consists of two main steps. First,
by evaluating the population distribution and particle fitness, a
real-time evolutionary state estimation procedure is performed to
identify one of the following four defined evolutionary states, in-
cluding exploration, exploitation, convergence,and jumping out in
each generation. It enables the automatic control of inertia weight,
acceleration coefficients, and other algorithmic parameters at run
time to improve the search efficiency and convergence speed. Then,
an elitist learning strategy is performed when the evolutionary
state is classified as convergence state. The strategy will act on
the globally best particle to jump out of the likely local optima.
The APSO has comprehensively been evaluated on 12 unimodal
and multimodal benchmark functions. The effects of parameter
adaptation and elitist learning will be studied. Results show that
APSO substantially enhances the performance of the PSO par-
adigm in terms of convergence speed, global optimality, solution
accuracy, and algorithm reliability. As APSO introduces two new
parameters to the PSO paradigm only, it does not introduce an
additional design or implementation complexity.


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