The field of metaheuristic optimization and decision making is witnessing significant developments, with a focus on improving the efficiency and effectiveness of algorithms. Researchers are exploring parallel implementations of optimization algorithms to leverage GPU acceleration, resulting in substantial performance gains. Additionally, there is a growing interest in designing decision support systems that combine human expertise with AI agents to achieve complementarity in sequential decision making tasks. These systems have been shown to outperform both human experts and AI agents alone, leading to more accurate predictions and better decision making. Furthermore, bio-inspired algorithms continue to be a rich source of innovation, with novel algorithms being proposed to solve complex optimization problems. Noteworthy papers in this area include: Narrowing Action Choices with AI Improves Human Sequential Decisions, which introduces a decision support system that uses a pre-trained AI agent to narrow down the set of actions a human can take, resulting in a 30% improvement in performance. Design and Analysis of Parallel Artificial Protozoa Optimizer, which presents a parallel implementation of the Artificial Protozoa Optimizer using NVIDIA CUDA framework, achieving a speedup of up to 6.7 times. Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making, which introduces a sequential reinforcement learning framework for imitation learning designed to model heterogeneous cognitive strategies in pollinators. Bombardier Beetle Optimizer: A Novel Bio-Inspired Algorithm for Global Optimization, which proposes a novel bio-inspired optimization algorithm inspired by the defense mechanism of Bombardier Beetles.