Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the
last decades. The need to explore this area is determined by the growing request for design …

Inverted generational distance (IGD) has been widely considered as a reliable performance
indicator to concurrently quantify the convergence and diversity of multiobjective and many …

Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …

Both convergence and diversity are crucial to evolutionary many-objective optimization,
whereas most existing dominance relations show poor performance in balancing them, thus …

Achieving balance between convergence and diversity is a key issue in evolutionary
multiobjective optimization. Most existing methodologies, which have demonstrated their …

Flexible job shop scheduling problem (FJSP) is one of the challenging issues in industrial
systems. In this study, we propose a bi-population balancing multi-objective evolutionary …

In this paper, we propose the use of modified distance calculation in generational distance
(GD) and inverted generational distance (IGD). These performance indicators evaluate the …

Artificial bee colony (ABC) algorithm has shown good performance on many optimization
problems. However, these problems mainly focus on single-objective and ordinary multi …

Taking both convergence and diversity into consideration, this paper suggests a vector
angle-based evolutionary algorithm for unconstrained (with box constraints only) many …