  We present CGO-AS, a generalized Ant System (AS) implemented in the framework
of Cooperative Group Optimization (CGO), to show the leveraged optimization
with a mixed individual and social learning. Ant colony is a simple yet
efficient natural system for understanding the effects of primary intelligence
on optimization. However, existing AS algorithms are mostly focusing on their
capability of using social heuristic cues while ignoring their individual
learning. CGO can integrate the advantages of a cooperative group and a
low-level algorithm portfolio design, and the agents of CGO can explore both
individual and social search. In CGO-AS, each ant (agent) is added with an
individual memory, and is implemented with a novel search strategy to use
individual and social cues in a controlled proportion. The presented CGO-AS is
therefore especially useful in exposing the power of the mixed individual and
social learning for improving optimization. The optimization performance is
tested with instances of the Traveling Salesman Problem (TSP). The results
prove that a cooperative ant group using both individual and social learning
obtains a better performance than the systems solely using either individual or
social learning. The best performance is achieved under the condition when
agents use individual memory as their primary information source, and
simultaneously use social memory as their searching guidance. In comparison
with existing AS systems, CGO-AS retains a faster learning speed toward those
higher-quality solutions, especially in the later learning cycles. The leverage
in optimization by CGO-AS is highly possible due to its inherent feature of
adaptively maintaining the population diversity in the individual memory of
agents, and of accelerating the learning process with accumulated knowledge in
the social memory.
