In order to achieve the desired results in engineering design and applications of optimization techniques are often used. This is known as engineering optimization. The other name is the optimization of engineering design optimization. The issues addressed are the shape optimization, optimization inverse planning process, structural design, topographic optimization, product designs and many others. Section of structural design is the design of welded beams and pressure vessels, etc. aerodynamic topology optimization includes, among others.
There are generally three methods or techniques used for solving the optimization problems such. These evolutionary algorithms are also known as genetic algorithms, most popular in its short GA; traditional algorithms and metaheuristics deterministic algorithms.
In order to solve simple problems, the traditional common algorithms such as hill climbing search and Hooke-Jeeves pattern finds a wide application. For problems that are more complex, strategies and evolutionary algorithms are the most used. The most recent of these are however metaheuristics algorithms which are also very promising. Among metaheuristic algorithms are genetic algorithms, simulated annealing, seeking harmony, particle swarm optimization, differential evolution and many more of them.
The "easy problems" are the problems mentioned above, including a single minimum or a single minimum. Therefore, due to this fact, the minimum found is also the global minimum. Furthermore, the most complex problems have more than a single minimum, they are those numbers many local minima. May not be possible in this case, to find the global minimum by using the gradient technique, although it may be able to find a local minimum.
Therefore, it is best to use metaheuristics algorithms for solving the problems of these methods, using a large number of search start points such as in genetic algorithms. Metaheuristic algorithms such as particle swarm optimization along with others are more competent in finding the global minimum.
Particle Swarm Optimization is a technique that solves the problem by trying to improve a candidate solution in relation to a given quality measure, using the iterative method. This technique does not use the gradient of the problem. Therefore, it is a good solution to irregular, altering and noisy optimization problems. Simulated annealing, however, is a good approximation of the global minimum of a particular function in a large search space. The differential evolution is used for functions that are multidimensional and real values. This technique is very similar to the particle swarm optimization. The harmony search is a process, which is based on improvisational techniques musicians. It is a phenomenon that mimics algorithm.
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