Lakhasly

3.In addition to the aforementioned common characteristics of theoretical implementation, these common NIOAs are varied to different versions to handle different problems, including combinational optimization problems (COPs) and multi-objective optimization problems (MOOPs).Similar variant methods are adopted to improve the optimization performance of NIOAs, for example, adaptive technology, fuzzy theory, chaos theory, quantum theory and hybridization technology[21][24][25].The individuals in the NIOAs move toward the global best solution through different mechanisms of information exchange.and ?, which have better solutions in the wolf population.Optimality.

3. Optimality. The individuals in the NIOAs move toward the global best solution through different mechanisms of information exchange. For example, the good genes in Genetic Algorithm, Artificial Intelligence and Differential are chosen as the next generation through the operators so, co and mo; particles in Particle Swarm Optimization update their positions, influenced by the local optimum pi(t) and the global optimum pg(t); the bat in Bat Algorithm generates a new solution and updates its position only if the new solution is better than the old one; the good antibodies in IA would save in a memory database to participate in the next iteration; Cuckoo Search replaces solutions only when the new one is better than the chosen solution and a fraction of worse solutions will be replaced by the newly generated solutions; in Gravitational Search Algorithm, the gravitational and inertial masses are calculated by the fitness evaluation and the better individuals have higher attractions and walk more slowly; wolves in Grey Wolf Optimization update their positions according to the positions of wolves α, β and δ, which have better solutions in the wolf population.
   
   In addition to the aforementioned common characteristics of theoretical implementation, these common NIOAs are varied to different versions to handle different problems, including combinational optimization problems (COPs) and multi-objective optimization problems (MOOPs). Similar variant methods are adopted to improve the optimization performance of NIOAs, for example, adaptive technology, fuzzy theory, chaos theory, quantum theory and hybridization technology[21][24][25].