What is Optimization?

Optimization is a form of mathematical procedure for determining optimal allocation of scare resources. In recent years, the optimization area has received enormous attention primarily due to the rapid emerging science and technology in computing, communication, engineering, environment and society. Several types of optimization problems exist. Two important classes of objects for most optimization problems are limited resources and activities.

Types of optimizations

5. Stochastic optimization (SO)
5. Robust optimization (RO)
5. Dynamic optimization (DO)
5.  

Stochastic optimization (SO)

Stochastic optimization (SO) process involves randomness in the minimization or maximization of a function and lends itself to real-life phenomena which involve uncertainty and imprecision. The randomness may be present as either noise in measurements or Monte Carlo randomness in the search procedure, or both. Some common techniques of SO are: direct search methods, stochastic approximation, stochastic programming, simulated annealing, genetic algorithms, etc. These techniques can cope with the inherent system noise, and systems with high nonlinearity and high dimensional models.

Robust optimization (RO)

Robust optimization (RO) is a rather new approach that deals with data uncertainty. The two motivational factors of RO are firstly the uncertainty model is rather deterministic and set-based. This motivational concept is the most appropriate notion of parameter uncertainty in many applications. The second motivational factor is the computational tractability. For instance, for a given optimization problem, multiple robust versions exist depend on the structure of the uncertainty set, therefore maintaining tractability is important. The classification models for RO includes local vs. global and probabilistic vs. non-probabilistic. Based on the nature of the problem, this technique is also known as min-max or worst-case approach. It provides a good guaranteed solution for most possible realizations of the uncertainty in the data. It is also useful if some of the parameters belong to the estimation process and contains estimation errors.

Dynamic optimization

Dynamic optimization (DO), also known as dynamic programming is a process of finding the optimal control profile of one or more control parameters of a system. It is used to find the possible number of solutions for a given problem.

Four major steps on development of DO algorithm are:

1. Characterize the structure of an optimal solution.
2. Recursively define the value of an optimal solution.
3. Compute the value of an optimal solution in a bottom-up fashion.
4. Construct an optimal solution from computed information.

Conclusion

These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems.

The journal invites different types of articles including original research article, review articles, short note communications, case reports, Editorials, letters to the Editors and expert opinions & commentaries from different regions for publication.

A standard editorial manager system is utilized for manuscript submission, review, editorial processing and tracking which can be securely accessed by the authors, reviewers and editors for monitoring and tracking the article processing. Manuscripts can be uploaded online at Editorial Tracking System (https://www.longdom.org/editorial-tracking/publisher.php) or forwarded to the Editorial Office at https://www.longdom.org/swarm-intelligence-evolutionary-computation.html The Journals includes around 150Abstracts and 100 Keynote speakers have given their valuable words. The meet has provided a great scope for interaction of professionals including in addition to clinical experts and top-level pathologists and scientists from around the globe, on a single platform.

Media Contact:

Sarah Rose

Journal Manager

International journal of swarm intelligence and evolutionary computation

Email: evolcomput@journalres.org