Solving Economic Machining Models using Quantum-Pattern Search-Particle Swarm Hybrid Optimization Algorithm

F.S. Alfares (Kuwait), H. Alenezi (UK), and M.S. Alajmi (Kuwait)


Optimization, Evolution Algorithms, Quantum Theory, Pattern Search Algorithm, and Particle Swarm Optimization.


This paper introduce novel hybrid optimization algorithm consists from elements of three optimization algorithms. The first algorithm is the particle swarm optimization, in which the element "velocity" is used. This element provides the distance to jump from the current position to the new position. The second element acquired from the pattern search algorithm in which the movement of the solution candidate in the neighborhood performed. This movement called compass movement where the direction of movement is to the up, down, right, or left. The last element obtained from the quantum-inspired algorithm. This element says that if no best solution found after performing the compass movement then the solution candidate will move to one of these positions that have the highest probability. The formula of the probabilities based on quantum computing theory. To investigate the performance of the proposed algorithm, machining economic models are used. The results have showed promising application of this hybrid algorithm to the field of mechanical engineering.

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