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Abstract

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.

Keywords

ampacity cable cluster hard constrained optimization power cable

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References

Giglio

De Leon

. Thermal model for different size trefoil power cables with various loads in non-forced-ventilated tunnels. IEEE Trans Power Delivery 2023; 38: 1286–1296.

Tessema

Yen

. An adaptive penalty formulation for constrained evolutionary optimization. IEEE Trans Syst Man Cybern Part A Syst Hum 2009; 39: 565–578.

Deb

. An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 2000; 186: 311–338.

Gao

Yen

Liu

. A dual-population differential evolution with coevolution for constrained optimization. IEEE Trans Cybern 2015; 45: 1108–1121.

Peng

Liu

. A novel constraint-handling technique based on dynamic weights for constrained optimization problems. Soft Comput 2018; 22: 3919–3935.

Yang

Bai

, et al. A wind driven optimization-based methodology for robust optimizations of electromagnetic devices under interval uncertainty. IEEE Trans Magn 2017; 53: 1–4.

Wang

Xue

, et al. Utilizing the correlation between constraints and objective function for constrained evolutionary optimization. IEEE Trans Evol Comput 2020; 24: 29–43.

Tian

Zhang

Xiao

, et al. A coevolutionary framework for constrained multiobjective optimization problems. IEEE Trans Evol Comput 2021; 25: 102–116.

Kieffer

Danoy

Bouvry

, et al. A new co-evolutionary algorithm based on constraint decomposition. Proceedings of International Parallel and Distributed Processing Symposium Workshops; 2017 May 29-Jun 02; Lake Buena Vista (FL): IEEE Press; 2017. pp.492–500. DOI: 10.1109/IPDPSW.2017.26.

10.

Deb

Singh

. Balancing survival of feasible and infeasible solutions in constraint evolutionary optimization algorithms. Proceedings of 2018 IEEE Congress on Evolutionary Computation; 2018 Jul 08-13; Rio de Janeiro (Brazil): IEEE Press; 2018. pp.1–8. DOI: 10.1109/CEC.2018.8477976.

11.

Liang

Runarsson

Mezura-Montes

, et al. Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. Nanyang Technological University, Singapore, Technical Report. 2006 Sep.

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

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