In the sixth generation (6G) of mobile communication networks, radio resource allocation
management (RRAM) systems must offer high levels of customization and sustainability in …

Particle swarm optimization (PSO) algorithms face performance challenges, which lean on
improving solutions quality, speed-up, dealing with large-scale problems, and exploitation of …

Multi-Objective Optimization Evolutionary Algorithms (MOEAs) belong to heuristic methods
proposed for solving Multi-objective Optimization Problems (MOPs). In fact, MOEAs search …

Most real-world problems aim at achieving multiple objectives under a pool of constraints.
Generally, the objectives of this problem category are contradictory. These problems are …

Google's Tensor Processing Unit (TPU) verifies the power of the systolic array architecture in
accelerating specific computational tasks. Since the present Neural Processing Unit (NPU) …

Hypervolume indicator is one of the most classic and commonly used metrics in the field of
multi-objective optimization. It is widely used to solve multi-objective optimization problems …

Many dynamic multi-objective optimization problems have come to exist as a direct result of
advances in telecommunications, real-time systems, and flexible/scalable manufacturing …