Angular Preference Multi-Objective Optimization Algorithms with Inverse Initialization

Since the preferred multi-objective optimization solution set is a local optimal solution with decision maker’s preferences. To improve the performance of its, this paper proposes the angular preference multi-objective optimization algorithms with inverse initialization (AP-MOA). AP-MOA proposes three new strategies. The first is the target initialization strategy. There are two types of cases. For a bi-objective optimization problem, a better initial population is generated in the specified region on the preference information. For the tri-objective optimization problem, a tent mapping is used to generate uniform individuals in the specified region on the preference information. The second is two stage mutation, which is using genetic and differential mutation to produce excellent and stable offspring. The third is the angular preference guiding strategy. Two rays are drawn from the origin of the coordinates based on preference information to delineate a preferred solution region. According to experimental comparison, AP-MOA can converge quickly and obtain a satisfactory set of preference solutions.