  In this paper, a novel multiagent based state transition optimization
algorithm with linear convergence rate named MASTA is constructed. It first
generates an initial population randomly and uniformly. Then, it applies the
basic state transition algorithm (STA) to the population and generates a new
population. After that, It computes the fitness values of all individuals and
finds the best individuals in the new population. Moreover, it performs an
effective communication operation and updates the population. With the above
iterative process, the best optimal solution is found out. Experimental results
based on some common benchmark functions and comparison with some
stat-of-the-art optimization algorithms, the proposed MASTA algorithm has shown
very superior and comparable performance.
