Adaptive modification of the particle swarm method based on dynamic correction of the trajectory of movement of individuals in the population

Evolutionary search methods are successfully used for deferent modeling and optimization tasks due to their universality and the relative simplicity of realization in practice. However, a significant problem of using them is related with premature convergence of the computational algorithm due to incomplete exploration of the search space. This happens when all particles come into space of the first found, perhaps local optimum and cannot get out of it. To solve the problem, it is necessary to develop control procedures correcting movements of the individuals in the population. This paper proposes a particle swarm optimization adaptive modification, permitting dynamic changes to the particles’ trajectory to find more promising locations. The method is based on the opportunity to change the displacement vector individually for each particle depending on previous iteration effectiveness. For this purpose, procedures of direction choice and dynamic change of particle movement free parameters are added in the proposed modification. As opposed to the canonic swarm algorithm version, where all individuals converge on one particle with the best value found, in the new modification each particle chooses its displacement direction independently and can change it if the direction will be identified as ineffective. This approach makes it possible to reduce the probability of premature convergence of the algorithm and to explore given search space better, all of which is especially important for the multimodal function with complex landscape. The proposed method was tested on the standard set of test functions for continuous optimization, and it showed high reliability with relatively small use of time and computer resources.