An introduction to synchronous self-learning Pareto strategy

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.

A natural-inspired optimization machine based on the annual migration of salmons in nature

Bio inspiration is a branch of artificial simulation science that shows pervasive contributions to variety of engineering fields such as automate pattern recognition, systematic fault detection and applied optimization. In this paper, a new metaheuristic optimizing algorithm that is the simulation of The Great Salmon Run(TGSR) is developed. The obtained results imply on the acceptable performance of implemented method in optimization of complex non convex, multi dimensional and multi-modal problems. To prove the superiority of TGSR in both robustness and quality, it is also compared with most of the well known proposed optimizing techniques such as Simulated Annealing (SA), Parallel Migrating Genetic Algorithm (PMGA), Differential Evolutionary Algorithm (DEA), Particle Swarm Optimization (PSO), Bee Algorithm (BA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Cuckoo Search (CS). The obtained results confirm the acceptable performance of the proposed method in both robustness and quality for different bench-mark optimizing problems and also prove the authors claim.