Multiobjective multitasking evolutionary algorithms have shown promising performance for
tackling a set of multiobjective optimization tasks simultaneously, as the optimization …

In this article, a novel dynamic multiobjective optimization algorithm (DMOA) with a
cascaded fuzzy system (CFS) is developed, which aims to promote objective knowledge …

The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner
particles and then uses the winner particles to efficiently guide the search of the loser …

Solving the energy-efficient distributed flexible job shop scheduling problem (EEDFJSP)
obtains increased attention. However, most previous studies barely considered the large …

The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great
room for improvement in balancing populations convergence, diversity and feasibility on …

Robust multi-objective optimization problems (RMOPs) widely exist in real-world
applications, which introduce a variety of uncertainty in optimization models. While some …

Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established
itself as a cornerstone of Artificial Intelligence. Recently, with the surge in data-intensive …

Adaptive operator selection is an online method that automatically adjusts the application
rate of different operators based on their actual performance. This paper proposes an …

Optimization problem of space scale expansion widely exists in practical applications, such
as transportation, logistics, scheduling, social networks, etc. According to different expansion …

The framework of decomposing a multi-objective optimization problem (MOP) into some
MOPs holds considerable promise. However, its advancement is constrained by numerous …