Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiagent based state transition algorithm for global optimization   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.

15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas