Multi-objective optimal power flow problem using constrained dynamic multitasking multi-objective optimization algorithm

The multi-objective optimal power flow (MOOPF) problem involves conflicting objectives and complex constraints, presenting a significant challenge for existing optimization methods. To address constrained multi-objective optimization problems (CMOPs), a recent evolutionary multi-tasking (EMT) framew...

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Bibliographic Details
Published in:Swarm and evolutionary computation Vol. 93; p. 101850
Main Authors: Zhu, Junhua, Yu, Xiaobing, Wang, Feng, Mao, Yaqi
Format: Journal Article
Language:English
Published: Elsevier B.V 01.03.2025
Subjects:
Auxiliary task
Constrained multi-objective optimization
Constraint handling technique
Knowledge transfer
Optimal power flow
Auxiliary task
MOOPF
OPF
IGD
UPF
FR
EMT
Knowledge transfer
CHT
CDP
HV
CV
Optimal power flow
CMOP
CMOEA
Constrained multi-objective optimization
CPF
MOEA
LVD
Constraint handling technique
ISSN:2210-6502
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Summary:The multi-objective optimal power flow (MOOPF) problem involves conflicting objectives and complex constraints, presenting a significant challenge for existing optimization methods. To address constrained multi-objective optimization problems (CMOPs), a recent evolutionary multi-tasking (EMT) framework has been proposed, involving a primary task and several auxiliary tasks running in parallel. The design of these auxiliary tasks is critical for supporting the solution of the primary task. This paper introduces a novel constrained dynamic multitasking multi-objective optimization algorithm (CDMTMO) to solve CMOPs. The proposed algorithm comprises three populations, each assigned a specific task: the first population focuses on solving the primary CMOP, the second population tackles a constraint-relaxed problem, and the third population gradually transitions from solving an unconstrained problem to the CMOP. To ensure effective collaboration among auxiliary tasks and the main task, CDMTMO incorporates an improved ε-constrained method and an enhanced dual ranking method. Furthermore, a pre-selection strategy for solution sets is integrated to discern promising individuals and facilitate knowledge transfer. CDMTMO has been evaluated using two IEEE standard systems, to demonstrate its capability and suitability in efficiently tackling the MOOPF problem. A thorough analysis of CDMTMO's results was performed, comparing it with seven state-of-the-art algorithms: AGEMOEA, CCMO, DSPCMDE, CMOEMT, ToP, EMCMO and DEST. After evaluating across eight test cases, CDMTMO achieved the best inverted generational distance plus (IGD+) and hypervolume (HV) values in seven cases. Furthermore, CDMTMO achieves a feasible rate (FR) of 1 in all cases, demonstrating its consistent ability to find feasible solutions.
ISSN:2210-6502
DOI:10.1016/j.swevo.2025.101850