Abstract
AbstractThe use of meta-heuristic methods for solving nonlinear engineering and optimization problems is one of the paramount topics that attracted the attention of the researchers. Particle swarm ...
Highlights
In recent years, various optimization algorithms had been used to solve engineering problems and provide optimal solutions to them
Results and discussion the Halton-Particle swarm optimization (PSO) technique is applied for solving the different benchmark functions
It is clear that the results of optimal gear ratio obtained by the Halton-PSO algorithm are similar to the other algorithms (CS (Gandomi et al, 2013), ALO (Mirjalili, 2015a), MFO (Mirjalili, 2015b), MVO (Mirjalili et al, 2016), ISA (Gandomi, 2014), and MBA (Sadollah et al, 2013). These results show the efficiency of the Halton-PSO algorithm for solving the engineering discrete problems
Summary
Various optimization algorithms had been used to solve engineering problems and provide optimal solutions to them. In contrary to classical algorithms, the new optimization techniques are implemented to evaluate the non-differentiable and non-continues optimization problems. The exploration and exploitation capabilities are two major features that make the metaheuristic algorithms powerful to find the global optimum. The exploration phase of an algorithm helps to search for the feasible space for new results and finding the global optimum. In the exploitation phase, for choosing the best solution, the algorithm searches the neighborhood of the highest quality solution. The balance of these two phases is very important, and an efficient algorithm provides a good exploration phase in the inception of the process and a good exploitation ability in the final stages (Yang, 2010b)
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