Niche-based and angle-based selection strategies for many-objective evolutionary optimization

It is well known that balancing population diversity and convergence plays a crucial role in evolutionary many-objective optimization. However, most existing multiobjective algorithms encounter difficulties solving optimization problems. Thus, this paper suggests niche-based angle-based selection strategies for In the proposed algorithm, two are included: density estimation strategy strategy. Both employed environmental to eliminate worst individual from an iterative way. To be specific, former estimates of each finds crowded area population. The latter removes individuals with weak same niche. Experimental studies on several well-known benchmark problems show algorithm competitive compared six state-of-the-art algorithms. Moreover, has also been verified scalable deal constrained