A multiobjective state transition algorithm based on modified decomposition method

Aggregation functions largely determine the convergence and diversity performance of multi-objective algorithms in decomposition methods. Nevertheless, traditional Tchebycheff function does not consider matching relationship between weight vectors candidate solutions. To deal with this issue, a new multiobjective state transition algorithm based on modified method (MOSTA/D) is proposed. According to analysis solutions under scheme, concept degree introduced which employs vectorial angles Based degree, aggregation proposed MOSTA/D. It can adaptively select are better matched vectors. This MOSTA/D decomposes optimization problem into number scalar subproblems optimizes them collaborative manner. Each individual solution population associated subproblem. Four mutation operators STA adopted generating maintaining diversity. Relevant experimental results show that highly competitive comparison other state-of-the-art evolutionary tackling set benchmark problems complicated Pareto fronts typical engineering problem.