  Cooperative coevolution (CC) has shown great potential in solving large scale
optimization problems (LSOPs). However, traditional CC algorithms often waste
part of computation resource (CR) as they equally allocate CR among all the
subproblems. The recently developed contribution-based CC (CBCC) algorithms
improve the traditional ones to a certain extent by adaptively allocating CR
according to some heuristic rules. Different from existing works, this study
explicitly constructs a mathematical model for the CR allocation (CRA) problem
in CC and proposes a novel fine-grained CRA (FCRA) strategy by fully
considering both the theoretically optimal solution of the CRA model and the
evolution characteristics of CC. FCRA takes a single iteration as a basic CRA
unit and always selects the subproblem which is most likely to make the largest
contribution to the total fitness improvement to undergo a new iteration, where
the contribution of a subproblem at a new iteration is estimated according to
its current contribution, current evolution status as well as the estimation
for its current contribution. We verified the efficiency of FCRA by combining
it with SHADE which is an excellent differential evolution variant but has
never been employed in the CC framework. Experimental results on two benchmark
suites for LSOPs demonstrate that FCRA significantly outperforms existing CRA
strategies and the resultant CC algorithm is highly competitive in solving
LSOPs.
