Biblio

For solving multi-objective optimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm II (NSGA-II) with good distribution to construct. Thus we propose a hybrid multi-objective optimization solving algorithm. Then, we consider that the population diversity needs to be improved while applying multi-objective particle swarm optimization (MOPSO) to solve the multi-objective optimization problems and an improved MOPSO algorithm is proposed. We give the distance function between the individual and the population, and the individual with the largest distance is selected as the global optimal individual to maintain population diversity. Finally, the simulation experiments are performed on the ZDT\textbackslashtextbackslashDTLZ test functions and track planning problems. The results indicate the better performance of the improved algorithms.

SDN is a promising architecture that can overcome the challenges facing traditional networks. SDN enables administrator/operator to build a simpler, customizable, programmable, and manageable network. In software-defined WAN deployments, multiple controllers are often needed, and the location of these controllers affect various metrics. Since these metrics conflict each other, this problem can be regarded as a multi-objective combinatorial optimization problem (MOCO). A particular efficient method to solve a typical MOCO, which is used in the relevant literature, is to find the actual Pareto frontier first and give it to the decision maker to select the most appropriate solution(s). In small and medium sized combinatorial problems, evaluating the whole search space and find the exact Pareto frontier may be possible in a reasonable time. However, for large scale problems whose search spaces involves thousands of millions of solutions, the exhaustive evaluation needs a considerable amount of computational efforts and memory used. An effective alternative mechanism is to estimate the original Pareto frontier within a relatively small algorithm's runtime and memory consumption. Heuristic methods, which have been studied well in the literature, proved to be very effective methods in this regards. The second version of the Non-dominated Sorting Genetic Algorithm, called NSGA-II has been carried out quite well on different discrete and continuous optimization problems. In this paper, we adapt this efficient mechanism for a new presented multi-objective model of the control placement problem [7]. The results of implementing the adapted algorithm carried out on the Internet2 OS3E network run on MATLAB 2013b confirmed its effectiveness.