Combining particle swarm optimisation with angle modulation to solve binary problems

The optimisation process of a particular problem generally has many influencing factors including the parameter choices, problem constraints as well as the complexity of the optimisation algorithm and optimisation problem among others. The dimensionality of a problem influences the computational complexity in converging to a valid solution. With problems defined in larger and more abstract dimensions, complexity becomes a problem as the solutions presented by the algorithm are more likely to be sub-optimal. An interesting and unique manner to reduce the complexity of binary problems is developed in this paper: angle modulation is applied to generate a bit string to solve binary problems, using particle swarm optimisation (PSO) to evolve the function coefficients of a trigonometric model. Instead of evolving a high dimensional bit vector, angle modulation reduces the problem to a four-dimensional problem defined in continuous space. Experimental results show that the angle modulation method is faster than the standard binary PSO, and that accuracy is improved for most benchmark functions used