Solving Optimization Problems

Test Your Understanding About Optimization

In this video, you can take a test with multiple choice questions to check your understanding about optimization.

Optimisation is generally referred to as finding the best solution to an optimisation problem. Optimisation is always desirable in many fields such as engineering design, computer science, operations research, economics, computational chemistry, biomedicine, etc. (Coelho, Ayala & Mariani 2014; Ng & Li 2014; Wang et al. 2013).

Optimisation methods can be broadly classified into two categories: deterministic methods and stochastic methods (Hanagandi & Nikolaou 1998). Each category has advantages and disadvantages.

Deterministic methods are capable of guaranteeing the global optimal solutions for certain problems thanks to exploiting some helpful features of the problem structure. However, they may fail when tackling black-box problems, extremely ill-behaved functions or complex large-scale problems, due to the combinatorial explosion issue.

Stochastic methods can work with any kind of optimisation problems but they are of weak capability of guaranteeing the global optimal solutions. Stochastic methods only provide the global optimal solutions with probabilistic guarantee and this probability will become 1 in infinite computing time (Liberti & Kucherenko 2005; Moles, Mendes & Banga 2003).

Nevertheless, there is no algorithm capable of solving general optimisation problem with certainty in finite computing time (Boender & Romeijn 1995).

Let’s see how many correct answers you get.

If you have any question or problem, please contact Dr. Panda by email: learnwithpanda2018@gmail.com

Thank you very much and good luck with your research!

Dr.Panda

View Comments

  • Sir how can I use Particle Swarm Optimization to solve the traveling Salesman Problem. I have the source code but it is not relating to the TSP. Secondly, do you have the source code of the PSO for python?

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