Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology

摘要: Abstract The paper proposes a novel metaheuristic Socio Evolution & Learning Optimization Algorithm (SELO) inspired by the social learning behaviour of humans organized as families in societal setup. This population based stochastic methodology can be categorized under very recent and upcoming class optimization algorithms—the socio-inspired algorithms. It is tendency to adapt mannerisms behaviours other individuals through observation. SELO mimics socio-evolution parents children constituting family. Individuals family groups (parents children) interact with one another distinct attain some individual goals. In process, these learn from well society. helps them evolve, improve their intelligence collectively achieve shared proposed algorithm models this de-centralized which may result overall improvement each individual’s associated goals ultimately entire system. shows good performance on finding global optimum solution for unconstrained problems. problem solving success evaluated using 50 well-known boundary-constrained benchmark test compares results few evolutionary algorithms are popular across scientific real-world applications. SELO’s also compared methodology—the Ideology algorithm. Results indicate that demonstrates comparable comparison gives ground authors further establish effectiveness purposeful real world