Jelena Ragno
In the rapidly evolving fields of engineering and technology, the quest for efficiency and innovation is constant. Engineers and researchers are often faced with the challenge of simultaneously optimizing multiple conflicting objectives. Traditional single-objective optimization approaches fall short in addressing the complexity of real-world problems where multiple criteria need to be considered. This is where Multi-Objective Optimization (MOO) comes into play, offering a powerful framework to tackle such challenges. This article delves into the significance of multi-objective optimization in engineering and technology and explores its applications, benefits and future prospects. Multi-objective optimization refers to the process of finding the best possible solutions that optimize multiple objectives simultaneously. These objectives are typically conflicting, meaning that an improvement in one objective may lead to deterioration in another. The aim of MOO is to identify a set of solutions, known as the Pareto front, which represents the trade-offs between different objectives, enabling decision-makers to make informed choices based on their preferences.
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