PSO-sono: A novel PSO variant for single-objective numerical optimization

Particle Swarm Optimization(PSO) is a well-known and powerful meta-heuristic algorithm in Swarm Intelligence (SI), and it was invented by simulating the foraging behavior of bird flock in 1995. Recently, many different PSO variants were proposed to tackle different optimization applications, however, the overall performance of these variants were not satisfactory. In this paper, a new PSO variant is advanced to tackle single-objective numerical optimization, and there are three contributions mentioned in the paper: First, a sorted particle swarm with hybrid paradigms is proposed to improve the optimization performance; Second, novel adaptation schemes both for the ratio of each paradigm and the constriction coefficients are proposed during the iteration; Third, a fully-informed search scheme based on the global optimum in each generation is proposed which helps the algorithm to jump out the local optimum and improve the overall performance. A large test suite containing benchmarks from CEC2013, CEC2014 and CEC2017 test suites on real-parameter single-objective optimization is employed in the algorithm validation, and the experiment results show the competitiveness of our algorithm with the famous or recently proposed state-of-the-art PSO variants.