چكيده فارسي :
This paper proposes the Real-parameter Compact
Supervision for the Particle Swarm Optimization (RCSPSO) in
order to optimize problems with continuous parameters.
RCSPSO uses the evolutionary configuration of the Real-valued
Compact Genetic Algorithm (RCGA) and the search philosophy
of the Particle Swarm Optimization (PSO). As a Compact
Evolutionary Algorithms (CEA), RCGA rather than operating on
a population of individuals processes a statistical representation
of that population. Thus, it shows a very explorative behavior. In
contrast, PSO despite having access to several solutions only uses
the best solution in order to explore the search space. Although it
hardly consumes any additional memory, it provides great
insight on the potential exploration areas to the particles.
Moreover, to improve the update operation of the probability
vector in RCGA, it uses an algorithm that prevents inaccurate
influence of particle’s fitness on its gene’s fitness by evaluating
each gene separately. Furthermore, to improve sampling the
search space, the algorithm uses a combination of Cauchy and
Gaussian distributions. To show the algorithm’s viability, we use
Differential Evolution (DE), CEAs and PSO on some well-known
benchmark functions under identical initial conditions. The
results show that the proposed method outperforms the
aforementioned algorithms in majority of simulation scenario
