Classical approaches of multicast routing consider a tree path whose computational cost entails high use resources such time and memory in the optimization process. This paper presents genetic algorithm model applied to problem, which no is built. The solution aims maximize common paths source-destinations routes minimize route sizes. New options fitness functions, variation selection operators were proposed increase ability generate feasible routes. simulations performed two networks:...

This paper presents a multiobjective genetic algorithm to solve the multicast routing problem without using trees. The mechanism find routes aims fulfill two conflicting objectives: maximization of common links in source-destination and minimization route sizes. proposed GA can be characterized by representation network permutation problem, local viability restrictions generate initial population with significant number feasible routes, variation operators constraints, selection select most...

The Multiobjective Evolutionary Algorithms (MOEAs) are often applied to solve difficult optimization problems, but the dynamic case is even more special. During optimization, if environment changed, a algorithm must temporarily increase exploration and decrease exploitation generate genetic diversity then be capable of handling new behavior environment. A technique may impose an extra delay such that needs fast because changes arrive at any time. This paper proposes model adds mutation...

The complexity of real-world problems requires, in most cases, optimized solutions considering multiple objectives. For this reason, the multi-objective optimization has been increasingly used to treat kind problems. In work, an approach is proposed deal with routes generation metrics and traffic congestion estimates. experiments include vehicles that intend perform stops large-scale road networks. OpenStreetMap data was create network contains all information needed. Four scenarios were...

In recent years, many evolutionary algorithms approaches were introduced to improve existing as well solve optimization and search for problems. Problems involving the of objectives require a set optimal solutions known Pareto frontier. Unfortunately, similarly single objective Evolutionary Algorithms, Multiobjective Algorithms (MOEAs) also suffer from loss genetic diversity. When converge few points, mechanism maintain diversity population throughout generations is needed. It expected that,...

The optimization of many objectives requires a set optimal solutions known as Pareto solutions. Similarly to the single objective in Evolutionary Algorithms (EAs), Multiobjective (MOEAs) also suffer from loss genetic diversity, allowing appearance sparse regions along frontier. A mechanism maintain population diversity generations is needed. It expected that, if controlled effectively, at end evolutionary process, Front optimum will be uniformly distributed possible. This paper proposes new...

Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary (MOEAs) has facilitated the adaptation creation new methods to handle more complex realistic optimizations, such as dynamic problems (DMOPs). A MOEA (DMOEA) can be constructed by changing structure variation operators used solve DMOPs. Furthermore, DMOEAs implement change-detection strategies mechanisms dynamics environment. are often designed unconstrained...

With the growth of e-learning discussion forums have been widely used to promote interaction and collaboration between students teachers asynchronously. Despite benefits teaching learning process use in can mean overload for teachers/tutors given large amount information generated debates. Taking into consideration importance performance follow-up discussions consequent help e-learning, teacher/tutor is considered a problem. Therefore, this study presents system that integrates Natural...