Chromosomes are sorted and put into fronts based on Within Pareto Non dominated sets. a Pareto front, the chromosomes are ranked based on the Euclidean distance between solutions. Generally, solutions which are far away (not crowded) from other solutions are given a higher preference during selection. This is done in order to make a diverse and to avoid a crowded solution set (local minima or maxima). solution set The best N (population) chromosomes are picked from the current population and put into a mating pool. In the mating pool, and is done. tournament selection, crossover mutation Crossover refers to breaking two chromosomes from a predefined point and joining them together to create a new chromosome. While refers to random single bit change in a mutation chromosome. In this way, new population is created in mating pool. The mating pool and current population is combined. The resulting set is sorted, and the best N chromosomes make it into the new population. Go to step 2, unless maximum number of have been reached. generations The solution set is the highest ranked Pareto non dominated set from the latest population. and has a influence on the efficiency of the Population size number of generations algorithm. Pareto Multi-objective Optimization in Cloud and Network In literature a lot of efforts are been found in the context of optimizing resources in the network and cloud. Some author's has opted In [91], authors pareto optimization methods. apply MOP on LTE based cellular multi-user scenario and maximize the user throughput by marginalizing the (ICIC). Similarly in [92], Inter-cell interference cancellation coordination authors propose an adaptive algorithm to allocate resources in multi-user cellular scenario at considering objective of within the bit and subcarrier level, rate maximization total power using NSGA-II algorithm. Similarly, authors in [93] attempt to improve constraint the energy of cellular system by dynamically switching the LTE base stations based on user efficiency demands. In computing, pareto based multi-optimization has been used to dynamically the (CPU, RAM, Virtual Machines). In [94], authors distribute and schedule cloud resources manage to using a pareto optimization based scheduler. balance the load on cloud resources Also they have applied ant colony optimization to improve the of the cloud. task scheduling In this way, scheduler adapts its strategy according to the changing environment and types of
Chromosomes are sorted and put into fronts based on Pareto Non dominated sets. Within a Pareto front, the chromosomes are ranked based on the Euclidean distance between solutions. Generally, solutions which are far away (not crowded) from other solutions are given a higher preference during selection. This is done in order to make a diverse solution set and to avoid a crowded solution set (local minima or maxima). The best N (population) chromosomes are picked from the current population and put into a mating pool. In the mating pool, tournament selection, crossover and mutation is done. Crossover refers to breaking two chromosomes from a predefined point and joining them together to create a new chromosome. While mutation refers to random single bit change in a chromosome. In this way, new population is created in mating pool. The mating pool and current population is combined. The resulting set is sorted, and the best N chromosomes make it into the new population. Go to step 2, unless maximum number of generations have been reached. The solution set is the highest ranked Pareto non dominated set from the latest population. Population size and number of generations has a influence on the efficiency of the algorithm. Pareto Multi-objective Optimization in Cloud and Network In literature a lot of efforts are been found in the context of optimizing resources in the network and cloud. Some author's has opted pareto optimization methods. In [91], authors apply MOP on LTE based cellular multi-user scenario and maximize the user throughput by marginalizing the Inter-cell interference cancellation coordination (ICIC). Similarly in [92], authors propose an adaptive algorithm to allocate resources in multi-user cellular scenario at bit and subcarrier level, considering objective of rate maximization within the total power constraint using NSGA-II algorithm. Similarly, authors in [93] attempt to improve the energy efficiency of cellular system by dynamically switching the LTE base stations based on user demands. In computing, pareto based multi-optimization has been used to dynamically distribute and schedule the cloud resources (CPU, RAM, Virtual Machines). In [94], authors manage to balance the load on cloud resources using a pareto optimization based scheduler. Also they have applied ant colony optimization to improve the task scheduling of the cloud. In this way, scheduler adapts its strategy according to the changing environment and types of
Dr.Asad-Javied-PhD.docx