A Brief Description on Optimization Techniques

Abstract

The optimization of large-scale issues is fraught with challenges. Multi-modality, dimensionality, and differentiability are the main challenges. Traditional methods often fail to tackle such large-scale issues, particularly when the goal functions are nonlinear. The primary issue is that conventional methods cannot handle non-differentiable functions since most traditional techniques need gradient information, which is not available. Furthermore, such methods often fail to handle optimization problems with a large number of local optima. To address these issues, stronger optimization methods must be developed. Modern optimization techniques are the name given to these methods. This article discusses optimization issue formulation, optimization methodologies, and solution approaches. Methods based on population are also discussed. For structures with discrete parameters, optimization utilizing constraints in terms of dependability is shown to be the optimal choice.