Lessons from the Evolutionary Computation Bestiary.

The field of metaheuristics has a long history of finding inspiration in natural systems, starting from evolution strategies, genetic algorithms, and ant colony optimization in the second half of the 20th century. In the last decades, however, the field has experienced an explosion of metaphor-centered methods claiming to be inspired by increasingly absurd natural (and even supernatural) phenomena-several different types of birds, mammals, fish and invertebrates, soccer and volleyball, reincarnation, zombies, and gods. Although metaphors can be powerful inspiration tools, the emergence of hundreds of barely discernible algorithmic variants under different labels and nomenclatures has been counterproductive to the scientific progress of the field, as it neither improves our ability to understand and simulate biological systems nor contributes generalizable knowledge or design principles for global optimization approaches.

Faster Convergence in Multiobjective Optimization Algorithms Based on Decomposition.

The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in performance of MOEA/D with RA.