|
Kaishav Gupta
Keywords:
Mathematical optimization, Engineering applications, Linear programming, Genetic algorithm, Particle swarm optimization, Machine learning, Artificial intelligence.
Abstract:
Mathematical optimization has become one of the most significant analytical approaches in engineering due to its ability to identify the most efficient solution from a set of feasible alternatives while satisfying predefined constraints. The rapid growth of computational power, artificial intelligence, and data-driven technologies has considerably expanded the scope of optimization techniques across engineering disciplines. Mathematical optimization assists engineers in reducing operational costs, improving system efficiency, minimizing resource consumption, enhancing reliability, and achieving sustainable development goals. Modern engineering systems are characterized by increasing complexity, uncertainty, and multidimensional decision-making, making optimization an indispensable component of engineering design and management. This review systematically evaluates classical optimization methods, nonlinear optimization, dynamic programming, stochastic optimization, evolutionary algorithms, swarm intelligence techniques, and hybrid optimization models. The study further examines applications in civil, mechanical, electrical, chemical, industrial, aerospace, transportation, environmental, biomedical, and manufacturing engineering. Advantages, limitations, emerging challenges, and future research opportunities are comprehensively discussed. The review concludes that hybrid optimization techniques integrated with artificial intelligence and machine learning represent the future direction of engineering optimization research.
|
|
International Journal of Recent Research and Review
ISSN: 2277-8322
Vol. XIX, Issue 3
August 2026
|
PDF View
PUBLISHED
August 2026
ISSUE
Vol. XIX, Issue 3
SECTION
Articles
|
