… by a neighborhood restriction and adaptive multi-operator. … [29] proposed a hybrid mutation
strategy (HM) by making use … different effects on the population convergence and diversity. …

… multi-objective optimization algorithm with adaptive mating and … S-CDAS that includes the
extreme solutions always in the top … (SBX) and polynomial mutation as variation operators with …

… to let objective vectors undergo cross-mutation as we do to decision … In extreme cases, all
decision variables are classified as … optimization algorithmsbased covariance matrix adaptation …

… relay-type hybrid optimization algorithm capable of solving … , of the algorithm, P A denotes
the population set obtained by … -R1B algorithm uses the “rand/1/bin” (R1B) mutation operator …

… mutation, an offspring population of equal size to the parent … this algorithm, a hyperplane was
constructed using M extreme … provided a manyobjective optimization algorithm using social …

… This paper develops a dual-population co-evolutionary paradigm for solving many-objective
service selection problems. It evolves two co-evolving populations separately with different …

… respectively to handle many-objective optimization problems (… solutions are mutated to
generate offspring population. After … The proposed operator can also be seen as a hybrid version …

… Instead, we obtain the current population’s extreme points (ie, maximum point and … mutation
strategy to generate a merged population. Finally, DVA proposes an angle-based adaptive …

… algorithms performance (Ishibuchi et al. 2003; Zhao et al. 2019; Long et al. 2022; Sharma et
al. 2023). Its population size, crossover and mutation … multi-objective optimization algorithms …

… as the hybrid surrogate model-based algorithm SAEMO [58] and the random forest-assisted …
3 is the normalization for combining the objectives of the current population and the current …