[1] This algorithm is based on the social cognitive theory, and the key point of the ergodicity is the process of individual learning of a set of agents with their own memory and their social learning with the knowledge points in the social sharing library.
It has been incorporated into the NLPSolver extension of Calc in Apache OpenOffice.
In SCO, each state is called a knowledge point, and the function
cognitive agents solving in parallel, with a social sharing library.
Each agent holds a private memory containing one knowledge point, and the social sharing library contains a set of
The algorithm runs in T iterative learning cycles.
By running as a Markov chain process, the system behavior in the tth cycle only depends on the system status in the (t − 1)th cycle.
The process flow is in follows: SCO has three main parameters, i.e., the number of agents
With the initialization process, the total number of knowledge points to be generated is
Compared to traditional swarm algorithms, e.g. particle swarm optimization, SCO can achieving high-quality solutions as
Some variants [5] were proposed to guaranteed the global convergence.
For example, SCO was hybridized with differential evolution to obtain better results than individual algorithms on a common set of benchmark problems.