Lens law based optimization algorithm: a novel approach - Nayak, Byamakesh; Roy Choudhury, Tanmoy (Soft computing (Berlin, Germany), 2023)
The application of three different categories such as swarm-based, physics-based, and evolutionary-based techniques to various optimization problems related to different fields gained importance due to accuracy, speed and least chance to fall in local minima. Although there are number of physics based optimization techniques available to solve the optimization problems, however, they suffer from their complexity and may require multiple runs to achieve the optimum value. This paper focuses on a simple physics based problem which outperforms few of the existing algorithms. This work investigates the physics-based Lens formula integration using a population-based approach. The contender for a population-level solution to picture obstructions created by a convex, concave, or combination of the two lenses is the item with multiple dimensional locations. The object’s location is updated using the image position. The fundamental goals of population-based optimization algorithms are highlighted by the concave, convex, or combination of both lenses, which enhances the speed of exploration, exploitation, and local optimum avoidance of the search space. The focal length equation, controls how the object’s picture is produced, may be altered by specifying a number of random and adaptive factors to highlight the exploitation and exploration of the search space. The capacity of the proposed algorithm for exploitation, exploration, utilisation of the search space, avoidance of local minima, and attainment of global maxima is validated using unimodal, multimodal, and composite benchmark functions. In addition, a variety of real-world engineering issues taken into account to verify the performance of the proposed algorithm. The observations compared with few fundamental optimization techniques of the same category and found the proposed solution better performing in some context than the existing ones.