  A cooperative group optimization (CGO) system is presented to implement CGO
cases by integrating the advantages of the cooperative group and low-level
algorithm portfolio design. Following the nature-inspired paradigm of a
cooperative group, the agents not only explore in a parallel way with their
individual memory, but also cooperate with their peers through the group
memory. Each agent holds a portfolio of (heterogeneous) embedded search
heuristics (ESHs), in which each ESH can drive the group into a stand-alone CGO
case, and hybrid CGO cases in an algorithmic space can be defined by low-level
cooperative search among a portfolio of ESHs through customized memory sharing.
The optimization process might also be facilitated by a passive group leader
through encoding knowledge in the search landscape. Based on a concrete
framework, CGO cases are defined by a script assembling over instances of
algorithmic components in a toolbox. A multilayer design of the script, with
the support of the inherent updatable graph in the memory protocol, enables a
simple way to address the challenge of accumulating heterogeneous ESHs and
defining customized portfolios without any additional code. The CGO system is
implemented for solving the constrained optimization problem with some generic
components and only a few domain-specific components. Guided by the insights
from algorithm portfolio design, customized CGO cases based on basic search
operators can achieve competitive performance over existing algorithms as
compared on a set of commonly-used benchmark instances. This work might provide
a basic step toward a user-oriented development framework, since the
algorithmic space might be easily evolved by accumulating competent ESHs.
