International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Multi-Objective Optimization for Real-World Problems: A Comprehensive Survey, Algorithmic Framework, and Smart Grid Case Study

Author(s) Ms. Prakruti Devang Dave, Dr. Hiren Satishbhai Lekhadiya
Country India
Abstract Multi-objective optimization is used to solve problems where there is an optimization of more than two objectives simultaneously. MOO yields a solution that consists of a Pareto-optimal set of decisions rather than a unique optimum solution. This survey presents a review of advanced algorithms developed in MOO including evolutionary algorithms such as NSGA-II, NSGA-III, MOEA/D, SPEA2, and SMS-EMOA as well as other conventional algorithms like weighted sum method and epsilon constraint. Convergence behaviour and diversity preservation mechanism are included in the theoretical background analysis. The taxonomy includes various fields of applications such as engineering design, energy, logistics, healthcare, water distribution network design, and financial portfolio. Comparative studies among algorithms using benchmark functions and metrics for multi-objective optimal power flow problem in renewable integrated smart grid microgrid are presented. Benchmark problems and metrics include hypervolume, generational distance, and inverted generational distance measures. Recent trends in many objective optimization, large scale multi-objective optimization, dynamic environment and machine learning algorithms integration are discussed.
Keywords Multi-objective optimization, Pareto optimality, evolutionary algorithms, many-objective optimization, real-world applications, metaheuristics.
Field Mathematics
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-06-12
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.81226
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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