The field of optimization is witnessing significant developments, with a focus on improving the efficiency and effectiveness of algorithms for complex problems. Researchers are exploring new approaches to tackle challenges such as large-scale global optimization, expensive multi-objective optimization, and mixed categorical-continuous variables. Notably, innovative frameworks and algorithms are being proposed to address these challenges, including cooperative co-evolution, composite indicator-guided infilling sampling, and massively parallelizable memetic tabu search. These advancements have the potential to revolutionize various applications, from protein binder design to multi-modal optimization. Some particularly noteworthy papers include:

• A Novel Two-Phase Cooperative Co-evolution Framework, which proposes a novel framework for large-scale global optimization with complex overlapping.
• Composite Indicator-Guided Infilling Sampling, which introduces a composite indicator-based evolutionary algorithm for expensive multi-objective optimization.
• New Improvements in Solving Large LABS Instances Using Massively Parallelizable Memetic Tabu Search, which presents a massively parallelized implementation of the memetic tabu search algorithm for solving large Low Autocorrelation Binary Sequences instances.