Oriented multi-mutation strategy in a many-objective evolutionary algorithm

Abstract Reference-point-based many objective optimisation is recognised to be a promising method with various applications. To mitigate the loss of selection pressure, most existing works try to discover a new preference relation and promote active diversity in its decisive space. However, with regard to breeding their off-springs, maintaining a good balance between convergence and diversity remains a dilemma. This paper suggests a novel θ dominance-based evolutionary algorithm (abbreviated as NUM-θ-DEA), which uses non-uniform mutation (NUM) instead of polynomial mutation. Its hybrid variant with a dual-stage model is also proposed. The technique focuses on rational exploitation and makes comprehensive use of the merits of non-uniform mutation, simulating binary crossover and differential evolution strategy. An extensive comparison with other many-objective optimisers was conducted in all the test benchmark problems with 3, 5, 8, 10, or 15 objectives. Experimental results and their relevant analyses illustrate that a very encouraging target can be achieved by NUM-θ-DEA with a multi-strategy switching mechanism.