Solution to industrial optimization problems through differential evolution variants

ABSTRACT

Optimization forms the core of several industrial problems and demands suitable methods of solution for determining the best possible solution. However, it is often observed that the classical optimization algorithms available in literature may not be applicable in real-life scenarios where the nature of the problems is quite complex. Under such circumstances, nature-inspired algorithms (NIA) can be beneficial because of their generic nature. This is shown in the present study by obtaining the optimal solution of two fundamental industrial problems, viz. optimizing and controlling the level of noise in an industry having multiple sources of noise; and optimizing the total production cost in a machining process through different NIA including two newly proposed differential evolution (DE) variants. Comparison is also done through classical methods. Both the problems are nonlinear in nature where the complexity increases by increasing the number of variables. The first problem taken is unconstrained in nature, while the second problem is constrained. A thorough comparison of all the methods is done through various performance measures and it is observed that the proposed DE variants form an attractive alternative for dealing with such problems.

KEYWORDS:

• Equipments
• finishing
• optimization
• roughing