  In last decades optimization and control of complex systems that possessed
various conflicted objectives simultaneously attracted an incremental interest
of scientists. This is because of the vast applications of these systems in
various fields of real life engineering phenomena that are generally multi
modal, non convex and multi criterion. Hence, many researchers utilized
versatile intelligent models such as Pareto based techniques, game theory
(cooperative and non cooperative games), neuro evolutionary systems, fuzzy
logic and advanced neural networks for handling these types of problems. In
this paper a novel method called Synchronous Self Learning Pareto Strategy
Algorithm (SSLPSA) is presented which utilizes Evolutionary Computing (EC),
Swarm Intelligence (SI) techniques and adaptive Classical Self Organizing Map
(CSOM) simultaneously incorporating with a data shuffling behavior.
Evolutionary Algorithms (EA) which attempt to simulate the phenomenon of
natural evolution are powerful numerical optimization algorithms that reach an
approximate global maximum of a complex multi variable function over a wide
search space and swarm base technique can improved the intensity and the
robustness in EA. CSOM is a neural network capable of learning and can improve
the quality of obtained optimal Pareto front. To prove the efficient
performance of proposed algorithm, authors utilized some well known benchmark
test functions. Obtained results indicate that the cited method is best suit in
the case of vector optimization.
