A more useful AHMOPSO (adaptive hybrid multi-objective particle swarm optimization) algorithm

Aim. The introduction of the full paper reviews a number of papers in the open literature and then proposes AHMOPSO algorithm, which we believe is better and is explained in sections 1,2 and 3. Section 1 briefs past research. The core of section 2 consists of: "Firstly, the initial solution sets are mapped by the Sobol sequence to distribute the decision variables uniformly. And the linear descending weight is utilized to enhance the convergence of the algorithm. The adaptive mutating operator based on the diversity index SP is brought to add the variety of the chromosomes. In addition, the adaptive chaos searching operator based on the improved generation distance index GD is adopted to enhance the local search ability." Simulation results, presented in Tables 1 through 3 and Figs. 2 through 5, compare our AHMOPSO algorithm with three generally used algorithms; the comparison shows preliminarily that AHMOPSO can indeed obtain better convergence and diversity.