Numerical optimization using organizational evolutionary algorithm
In this paper, a new algorithm, organizational evolutionary algorithm (OEA), is proposed to deal with both unconstrained and constrained optimization problems. In OEA, the evolutionary operations do not act on individuals directly, but on organization. Therefore, three evolutionary operators are designed for organizations. In experiments, OEA is tested on 3 unconstrained and 6 constrained benchmark problems, and compared with three recent algorithms. The results show that OEA outperforms the three other algorithms both in the quality of solution and the computational cost.
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