Particle swarm optimization (PSO) [1] has been introduced as an optimer technique for use in real-number spaces. Later, a reworked original PSO algorithm, known as Binary PSO [2], has been developed to allow PSO algorithm to operate in discrete binary variables. Binary PSO algorithm has been employed in many fields including expert systems [3-4], artificial neural network [5], pattern recognition [6], and power system [7]. Also, a number of improved Binary PSO algorithms have been introduced in literature. For example, the original Binary PSO algorithm has been improved to avoid the generated solution from being trapped in local minima [8]. On the other hand, Franken and Engelbrecht have proposed Angle Modulated PSO (AMPSO) [9] to improve Binary PSO in terms of computational complexity and efficiency. There are 2
limitations of the original Binary PSO algorithm. The first limitation is
regarding the computational complexity, while the second limitation, which is
defined as Hence, the
objective of this research is to derive, validate, and analyse a new mechanism
of discrete PSO algorithm, which is called multi-state discrete PSO algorithm,
to solve the computational complexity and |

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