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Composite interfaces are commonly simulated by cohesive zone models with thekey challenge being the calibration of interfacial parameters. A new frameworkis presented in this paper to derive the characteristic of any cohesive zonemodel. This approach employs the multi-island genetic algorithm to obtain theinterface parameters aligning closely with the experimental observations. Theintroduced framework innovatively formulates an objective function, consideringboth the congruence of the load-displacement curve and the alignment with thefailure mode of model. The framework combines machine learning andmulti-objective optimization. A method using the interface debonding length toquantify the failure mode of the model is proposed. To demonstrate thefeasibility of the proposed framework, the newly strength-based cohesive zonemodel is taken as an example, and key parameters and damage evolution areidentified accurately. The inverse algorithm is used to identify the interfaceparameters of both the double cantilever beam experiment and the four-pointbending test. The robustness and accuracy of the framework are validatedthrough the double cantilever beam test. The findings indicate that thenumerical results align closely with the experimental data, confirming that theinterface parameters identified by the proposed framework can reproduce theperformance of the adhesive joints.

Inverse identification framework for cohesive zone model incorporating failure mode based on multi-island genetic algorithm