Particle swarm optimization with neighborhood-based budget allocation

The standard particle swarm optimization (PSO) algorithm allocates the total available budget of function evaluations equally and concurrently among the particles of the swarm. In the present work, we propose a new variant of PSO where each particle is dynamically assigned different computational budget based on the quality of its neighborhood. The main goal is to favor particles with high-quality neighborhoods by asynchronously providing them with more function evaluations than the rest. For this purpose, we define quality criteria to assess a neighborhood with respect to the information it possesses in terms of solutions’ quality and diversity. Established stochastic techniques are employed for the final selection among the particles. Different variants are proposed by combining various quality criteria in a single- or multi-objective manner. The proposed approach is assessed on widely used test suites as well as on a set of real-world problems. Experimental evidence reveals the efficiency of the proposed approach and its competitiveness against other PSO-based variants as well as different established algorithms.