A hybrid optimizer of an improved GA and an adaptive tabu search for cable ampacity optimizations in cable clusters

It is demanding to maximize the power cable ampacity to make full usage of the cluster. Since the maximization of the cable ampacity in a cable cluster is a hard constrained global optimization problem, and the final solution may be located on the constraint boundary; the existing optimization methodology will incur deficiencies in pinpointing the global optimal solution efficiently due to the underuse of the infeasible solutions. To use infeasible solutions to pinpoint exactly the global optimal solution, a hybrid optimizer based on an improved genetic algorithm and a local search adaptive tabu search (IGA-ATS) is proposed. In the proposed optimizer, the population is divided into two subpopulations: a feasible one to drive the search toward promising region while an infeasible one to stimulate to pinpoint the potential solutions on the constraint boundary. Consequently, two different criterions are designed to criticize individuals in the two subpopulations, and the corresponding selection strategies are proposed. Moreover, a boundary intensity searching strategy is proposed to drive promising infeasible individuals clustering to the potential constraint boundary efficiently. The superiorities of the proposed method are confirmed by the numerical results on a test function and a case study.