  Multi-Objective Optimization (MOO) techniques have become increasingly
popular in recent years due to their potential for solving real-world problems
in various fields, such as logistics, finance, environmental management, and
engineering. These techniques offer comprehensive solutions that traditional
single-objective approaches fail to provide. Due to the many innovative
algorithms, it has been challenging for researchers to choose the optimal
algorithms for solving their problems. This paper examines recently developed
MOO-based algorithms. MOO is introduced along with Pareto optimality and
trade-off analysis. In real-world case studies, MOO algorithms address
complicated decision-making challenges. This paper examines algorithmic
methods, applications, trends, and issues in multi-objective optimization
research. This exhaustive review explains MOO algorithms, their methods, and
their applications to real-world problems. This paper aims to contribute
further advancements in MOO research. No singular strategy is superior;
instead, selecting a particular method depends on the natural optimization
problem, the computational resources available, and the specific objectives of
the optimization tasks.
