Nature-inspired metaheuristics are very popular these days; their creation is usually justified based on the “no free lunch” theorem by Wolpert and MacReady. However, the creation of novel metaheuristics should be realized with care, not only for the sake of creation (cf. Sörensen reports on superficial metaheuristics); in other words, the inspiration should be solid and well-intended (it would be ideal if such methods were formally verified, however this happens very seldom, because of their complexity). In the case of the metaheuristics presented in this monograph, the inspiration comes from the works of Albert Bandura, a renowned Canadian-American psychologist. One of his most important contributions to contemporary science is the theory of social cognitive learning, showing that people do not only learn from their experiences (trial and error) but also by perceiving other people and (fortunately) their trials and errors. This saves a lot of effort, allowing us to utilize the knowledge gathered by perceiving others in order to build humankind’s self-knowledge. This inspiration leads to the proposal of a socio-cognitive metaheuristic paradigm consisting of the introduction or enhancement of the cognitive properties of particular metaheuristics. The most important achievements in this area are socio-cognitive Ant Colony Optimization and socio-cognitive Particle Swarm Optimization. The introduction of cognitive features into such computing algorithms allows us to reach better efficiency in solving selected hard benchmark problems. In this work, the above-mentioned novel algorithms are presented along with selected experimental results. Moreover, the socio-cognitive computing paradigm is defined, and the relationship of the selected metaheuristic algorithm to this paradigm is discussed. This metaphor is also considered as a reference for the selected classic and agent-based metaheuristics. These algorithms are identified by relating them to the literature background, and the possibilities of enhancing them with socio-cognitive features are discussed. Certain examples of further research are also identified. This monograph is meant to introducea novel perspective on the selected metaheuristics, defining the socio-cognitive computing paradigm and providing guidance in this area for readers who are interested in such nature-inspired computing methods.

Metaheurystyki inspirowane naturą należą do popularnych obecnie metod rozwiązywania trudnych problemów optymalizacyjnych. Metody obliczeniowe prezentowane w tej monografii czerpią inspirację z prac Alberta Bandura, znanego kanadyjsko-amerykańskiego psychologa. Do jego najważniejszych sukcesów należy opracowanie teorii socjalno-kognitywnego uczenia się, zgodnie z którą ludzie uczą się nie tylko na podstawie własnych doświadczeń (najczęściej prób i błędów), ale również obserwują innych ludzi, a co za tym idzie - ich próby i błędy. Gromadzenie wiedzy o innych pozwala na budowanie samoświadomości i znacznie ułatwia proces uczenia. W monografii zostały zaprezentowane dwa inspirowane powyższą teorią algorytmy socjokognitywne, a mianowicie algorytm mrówkowy oraz roju cząstek. Celem autora książki jest wprowadzenie nowej perspektywy postrzegania wybranych metaheurystyk, zaproponowanie socjokognitywnego paradygmatu obliczeniowego oraz umożliwienie czytelnikowi zainteresowanemu obliczeniami inspirowanymi naturą poszerzenie wiedzy na ten temat.