A comprehensive study on modern optimization techniques for engineering applications Shitharth Selvarajan $ ^{1,2} $ Accepted: 11 June 2024 / Published online: 4 July 2024 © The Author(s) 2024 Abstract Rapid industrialization has fueled the need for effective optimization solutions, leading to the widespread use of meta-heuristic algorithms. Over 300 new methodologies have been developed in the last ten years, highlighting the need for a sophisticated understanding of these novel methods. The use of biological and natural phenomena to inform meta-heuristic optimization strategies has seen a paradigm shift in recent years. The observed trend indicates an increasing recognition of the effectiveness of bio-inspired methodologies in tackling complex engineering problems, providing solutions that exhibit rapid convergence rates and unmatched fitness scores. This study thoroughly examines the latest advancements in bio-inspired optimization techniques. It investigates each method's unique characteristics, optimization properties, and operational paradigms to determine how revolutionary these approaches could be for problem-solving paradigms. Additionally, extensive comparative analyses against conventional benchmarks, such as search history, trajectory plots, and fitness functions, are conducted to elucidate the superiority of these new approaches. Our findings demonstrate the revolutionary potential of bio-inspired optimizers and provide new directions for future research to refine and expand upon these intriguing methodologies. Our survey could be a lighthouse, guiding scientists towards innovative solutions rooted in various natural mechanisms. Keywords Bio-inspired meta-heuristic technique · Feature selection · Benchmark test problems · Optimization · Engineering problems 1 Introduction The term "meta" means "deeper" or "notably greater level." The ongoing improvement of heuristic algorithms is often referred to as "metaheuristics," despite the lack of a unified scientific definition. A heuristic algorithm is a technique that uses trial and error methods to generate feasible solutions for optimization problems. Several meta-heuristics, often known as nature-inspired methods, draw motivation from nature. Intelligent algorithms have been developed to address real-world issues and are modelled after biological and natural laws. They possess the characteristics of a simple idea and practical execution and offer complex tasks for people to do in a group for collaborative execution. Intelligent algorithms have grown to be a prominent topic for research because of their widespread use in computing, data discovery, communications networks, and time forecasting, as well as their propagation, brevity, adaptability, and endurance. Optimization is essential for solving complex engineering problems by providing suitable and intelligent solutions—optimization methodologies. Optimization methodologies are extensively used in all application systems, offering the best solutions to specific issues. According to recent reviews, more than 150 different types of optimization techniques are used in real-time application systems. Typically, feature analysis is mainly performed to solve the given problem by determining the minimum and maximum values, and the obtained solution is termed the objective function. The meta-heuristics optimization techniques are categorized into the following types: • EvolutionA comprehensive study on modern optimization techniques for engineering applications Shitharth Selvarajan $ ^{1,2} $ Accepted: 11 June 2024 / Published online: 4 July 2024 © The Author(s) 2024 Abstract Rapid industrialization has fueled the need for effective optimization solutions, leading to the widespread use of meta-heuristic algorithms. Over 300 new methodologies have been developed in the last ten years, highlighting the need for a sophisticated understanding of these novel methods. The use of biological and natural phenomena to inform meta-heuristic optimization strategies has seen a paradigm shift in recent years. The observed trend indicates an increasing recognition of the effectiveness of bio-inspired methodologies in tackling complex engineering problems, providing solutions that exhibit rapid convergence rates and unmatched fitness scores. This study thoroughly examines the latest advancements in bio-inspired optimization techniques. It investigates each method's unique characteristics, optimization properties, and operational paradigms to determine how revolutionary these approaches could be for problem-solving paradigms. Additionally, extensive comparative analyses against conventional benchmarks, such as search history, trajectory plots, and fitness functions, are conducted to elucidate the superiority of these new approaches. Our findings demonstrate the revolutionary potential of bio-inspired optimizers and provide new directions for future research to refine and expand upon these intriguing methodologies. Our survey could be a lighthouse, guiding scientists towards innovative solutions rooted in various natural mechanisms. Keywords Bio-inspired meta-heuristic technique · Feature selection · Benchmark test problems · Optimization · Engineering problems 1 Introduction The term "meta" means "deeper" or "notably greater level." The ongoing improvement of heuristic algorithms is often referred to as "metaheuristics," despite the lack of a unified scientific definition. A heuristic algorithm is a technique that uses trial and error methods to generate feasible solutions for optimization problems. Several meta-heuristics, often known as nature-inspired methods, draw motivation from nature. Intelligent algorithms have been developed to address real-world issues and are modelled after biological and natural laws. They possess the characteristics of a simple idea and practical execution and offer complex tasks for people to do in a group for collaborative execution. Intelligent algorithms have grown to be a prominent topic for research because of their widespread use in computing, data discovery, communications networks, and time forecasting, as well as their propagation, brevity, adaptability, and endurance. Optimization is essential for solving complex engineering problems by providing suitable and intelligent solutions—optimization methodologies. Optimization methodologies are extensively used in all application systems, offering the best solutions to specific issues. According to recent reviews, more than 150 different types of optimization techniques are used in real-time application systems. Typically, feature analysis is mainly performed to solve the given problem by determining the minimum and maximum values, and the obtained solution is termed the objective function. The meta-heuristics optimization techniques are categorized into the following types: • Evolution