A Prior Preference-Based Decision-Making Algorithm in Pareto Optimization

In the applications of real-life, the importance of having a flexible optimization algorithm is obvious. Commonly in these issues, Evolutionary Multi-Objective Optimization (EMO) algorithms and particularly Pareto optimization method as one of the most significant and useful classes have been used extensively. Often optimization algorithms that have used the EMO algorithm in their own as posteriori Decision-making (DM) algorithms, in weighted objectives problems, have suffered from the uniform prioritization of objectives. In this paper, we propose a lightweight angle-based updating Pareto front (PF) algorithm which considers the preferences of desired objectives expressed using the Favorite Region (FR). Actually, the FR has been created in the objective space according to the prior-fixed angle of priority objectives. Thus, the solutions in PF will be able to tend towards FR during the evolutionary process. Consequently, other solutions that are not in the favorite region will not go away, but the Fronts levels of solutions via an update process will change rearwardly. The updating process in Pareto method, during the evolution process, causes that the solutions in the first and second fronts lead to the exploration and exploitation of appropriate solutions in the favorite regions with uniform distribution for first Pareto Front, while the solutions’ density in the undesirable region become impaired. The experimental results on benchmark multi-objective problems show that the proposed algorithm in addition to providing preference decision-making, by providing a tradeoff between convergence performance and computational complexity, can give the best convergence performance.