Abstract

In this study, an efficient biologically inspired constrained multi-objective optimization algorithm called Genetic Pareto Set Identification Algorithm plus Different Sex (GPSIA+DS) was developed. A complex system comprising of mixed configuration, k-out-of-n and redundant subsystem was used to validate GPSIA+DS in comparison with Genetic Pareto Set Identification Algorithm (GPSIA) and Fast Non–Dominated Sorting Genetic Algorithm plus Constrain Domination (NSGA-II+CD). The optimization strategy based on Genetic Pareto Set Identification Algorithm (GPSIA) only considered feasible solutions. That is, solutions, which satisfies all constraints conditions. Infeasible solutions could facilitate faster convergence to the true Pareto front. GPSIA+DS, which considers feasible and infeasible solutions in it optimization strategy was shown to outperform GPSIA and NSGA-II for the test problem considered.

Keywords:

Complex system, constrain, Genetic Algorithm (GA), Multi-Objective Optimization (MOO), Pareto set,

References

Competing interests

The authors have no competing interests.

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The authors have no competing interests.