optimization means the best possible solution to a problem given a constraint set, which is also called the feasible set, in mathematical optimization there are two common approaches to attain optimality, one, starting from the first principles and knowledge of the system behavior construct a deterministic objective function that relate your output response to the input factors and generate the constraint set based on the restrictions on your input variables, now you can look for the optimal solution to your problem based on suitable linear and non linear programming techniques, in case your optimization problem is a convex programming problem ( both the objective function and constraints are convex functions), the Karush-Kuhn-Tucker (KKT) conditions are the necessary and sufficient conditions for optimality.

the second approach, one that do not require much system knowledge a priori is the Statistical optimization methods, in particular the Response Surface Methodology (RSM), here the objective is to obtain a data driven approximate model of the true system response surface , usually a low order polynomial model, whose optimal point is a close approximation to the true system optimum point, however, this method involves a lot of sequential experimentation and data analysis but is extremely useful when your system is extremely complex and construction of a deterministic model of output behavior is extremely difficult, i would recommend you take into consideration RSM based analysis in your problem before coming to any conclusion about optimality.

coming to your question , optimization, as the name suggest, refers to the best possibility, so, a point in experimental space can be called optimal if and only if it satisfies the optimality conditions, now, this can be a local or global optima, in case of a local optima, the optimality is ensured only over a neighborhood of the experimental space encircling the point while a global optima satisfies the optimality condition over the entire feasible space, so, the situation you are referring to can be at best be regarded as a local optima, but may or may not not be the global optima of the system behavior, for that a detailed study of the system behavior is absolutely essential , RSM is a very useful tool in this regard.

the book on Response Surface Methodology by Myers, Montogomery and Cook contains a consolidated collection of such methods that you may find useful in solving your problem, please look into it for further clarification of your doubts in this regard.

lastly, optimization is not restricted to multi-variable situation only, there exist a myriad of uni-variate optimization tools as well, for example, the Newton's method in Unconstrained optimization situation, you can get more information about this and related topics in the appropriate subject literature

I try to give you quick answers to your questions:

- Now can I call the act of determining the right volume fraction as OPTIMIZATION?

R.: Yes, if you must find the right "solution", you have an optimization problem to solve (in short: you have an objective function to "optimize" and, probably some constraints to satisfy).

- Or Does OPTIMIZATION mean using sophisticated optimization techniques?

R.: You need a solution method. Sometime the problem is difficult and you need a sophisticated optimization technique to find an optimal or near optimal solution (the best solution according the objective function). But sometime the problem is easy to solve or you just need a feasible solution; in this situations it could be enough a simple algorithm.

- or do we use the word only when we have multiple input factors?

R.: It is not necessary this requirement to have an "optimization" problem.

Optimization means the best utilization of the available resources to get the maximum benefits.

Optimisation means getting benefits whether it is maximum or minimum functions. Definitely for the optimisation one need to have multiple input factors also called as Decision variables, these variables will be varied in Certain range.

In the experiments which we conduct will have a discrete set, there also we can do optimisation by techniques such as ANOVA for single objevtive, Grey relational analysis TOPSIS etc. If we need continuous values optimisation we need to go for algorithms like Genetic algorithm etc.

Optimization is the selection of process controls and operating parameters that add the largest value that is available with existing resources.

Optimization means the best possible solution for the given problem under defined constraint set and existing resources.

I thank each one of you for having helped me with your valuable answers.

Optimisation means acquiring best possible solution for the defined problem with in defined boundary conditions (constraints)

Optimisation is nothing but minimizing losses and maximizing the value.

Optimization is the process of finding the greatest or least value of a function for some constraint, which must be true regardless of the solution. Alternatively, it means the best possible solution for a given problem under defined set of constraints

Optimization word have different meaning in different situation:

http://vrand.com/education.html

http://www.businessdictionary.com/definition/optimization.html

http://www.jsu.edu/dept/psychology/sebac/fac-sch/rm/Ch5-1.html

I think the optimization refer to used a suitable tools or parameters to get the best solution and results

Optimization has finding an alternative with the most cost effective or highest achievable performance under the given constraints and minimizing the losses

Maximize or Minimize given objective function(s) (single or multi) with given subject to constraints (if any)

Obtaining a optimum (best) result that to reflect in product quality and production economy positively is Optimization.