Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation

• We propose a cooperative approach for numerical optimization.
• A fast operator is proposed to capture the interdependencies among variables.
• Problem decomposition is performed based on the interdependencies.
• A cross-cluster mutation is proposed to optimize the subproblems.
• This approach is effective for large scale optimization problems.

Cooperative optimization algorithms have been applied with success to solve many optimization problems. However, many of them often lose their effectiveness and advantages when solving large scale and complex problems, e.g., those with interacted variables. A key issue involved in cooperative optimization is the task of problem decomposition. In this paper, a fast search operator is proposed to capture the interdependencies among variables. Problem decomposition is performed based on the obtained interdependencies. Another key issue involved is the optimization of the subproblems. A cross-cluster mutation strategy is proposed to further enhance exploitation and exploration. More specifically, each operator is identified as exploitation-biased or exploration-biased. The population is divided into several clusters. For the individuals within each cluster, the exploitation-biased operators are applied. For the individuals among different clusters, the exploration-biased operators are applied. The proposed operators are incorporated into the original differential evolution algorithm. The experiments were carried out on CEC2008, CEC2010, and CEC2013 benchmarks. For comparison, six algorithms that yield top ranked results in CEC competition are selected. The comparison results demonstrated that the proposed algorithm is robust and comprehensive for large scale optimization problems.

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