**2. Overview of optimization**

Optimization methods execute and compare iteratively to find solutions for the optimum solutions to be searched. Optimization is part of all problems in all fields. Common types of optimization methods adopted to find solutions are briefed.

#### **2.1 Stochastic optimization**

Stochastic optimization (SO) computation involves more vagueness and impreciseness because of randomness in function of minimization or maximization to lend for real-life scenarios. The involved unpredictability exists in form of noise in process of search by Monte Carlo randomness [8]. Stochastic annealing, approximation, programming, swarm-based algorithms are common involved techniques of SO. They include high non-linearity system noise and dimensional models. These models are present to analyze, solve, derive, and numerical extraction of information in resolving decision-making problems. Major investment of SO is in specific applications oriented towards long and short programs. Aircrafts, missile, drug design, and network traffic control applications are getting advantage of SO. Stochastic application tool can be

applied as a powerful modelling tool in a few applications, but estimation of real-life problems is another major uncertainity where solving through SO involves practical limitations. Another problem of SO is complete dependency on data available and modelling of it [3, 9].

### **2.2 Robust optimization**

The optimization model is robust based to deal with data to regulate uncertainty. Key features are deterministic, easy computational tractability and set based. Model includes global or local or non- probabilistic or probabilistic models. Any given problem will get involve all the features of robust optimization in order to search for a solution. The technique is also known as the min-max or worst-case approach. Provide a guarantee for solutions to problem application which involves more uncertainty in data. The parameters involved in process of estimation are to resolve estimation errors. One improved model for definition and interpretation is setting more robust constraints [10]. Engineering optimization design results mainly in reliability optimization and feasible input possible values to robust solution structure. Robust optimization gives same weight and values for parametric values in collection of uncertain data. Problems will be resolved with the formulation of cost savings and stability, qualitative and quantitative. Complex problems considered for optimization may extend complexity to a more significant level [6, 7].

### **2.3 Dynamic optimization**

Dynamic programming is another name for dynamic optimization which processes optimal profile of more than one parameter of a system used to find possible solutions for a problem given. Variations of dynamic optimization with optimization discrete time, calculus variation and extended static optimization. The implementation includes a system controller to perform criteria with algorithm to execute control. Dynamic optimization involves a system controller performing optimal substructure and overlapping sub-problems [8]. Dynamic optimization characterizes structure, recursively defines value, computes value and constructs an optimal solution for computation. Dynamic programming optimizes problems and recursively divide problem into sub-problems which can solve either bottom-up or top-down approach. The logic used is general and supple. It solves computation time and storage space [9]. Classification optimization based on different factors is summarized in **Table 1**.
