Swarm INTelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
stochastic search and optimization tool. Changes in the
PSO parameters, namely the inertia weight and the
cognitive and social acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO parameters.
Swarm INTelligence is an innovative computational way to solving hard problems.
This discipline is inspired by the behavior of social insects such as fish
schools and bird flocks and colonies of ants, termites, bees and wasps. In general,
this is done by mimicking the behavior of the biological creatures within
their swarms and colonies.
aiParts is a set of C++ classes that can be used to develop artificial INTelligence for multi-decision problems. It includes classes that implement the High-Hope technique and some sample programs.