Modern web services rely on Content Delivery Networks (CDNs) to efficiently deliver contents to end users. In order to minimize the experienced communication cost, it is necessary to send the end user's requests to the nearest servers. However, it is shown that this naive method causes some servers to get overloaded. Similarly, when distributing the requests to avoid overloading, the communication cost increases. This is a well-known trade-off between communication cost and load balancing in CDNs. In this work, by introducing a new meta-heuristic algorithm, we try to optimize this trade-off, that is, to have less-loaded servers at lower experienced communication cost. This trade-off is even better managed when we optimize the way servers update their information of each others' load. The proposed scheme, which is based on Honeybee algorithm, is an implementation of bees algorithm which is known for solving continuous optimization problems. Our proposed version for CDNs is a combination of a request redirecting method and a server information update algorithm. To evaluate the suggested method in a large-scale network, we leveraged our newly developed CDN simulator which takes into account all the important network parameters in the scope of our problem. The simulation results show that our proposed scheme achieves a better trade-off between the communication cost and load balancing in CDNs, compared to previously proposed schemes.
Bees algorithm is a population-based search scheme, which is categorized in the class of meta heuristic algorithms. The process is a mimicry of foraging behavior of honeybees to look for the best solution to an optimization problem @cite_3 .
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Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Open Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Open Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm and the Expanding Neighborhood Search (ENS) algorithm. Two set of benchmark instances is used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen instances proposed by Christofides, the average quality is 0.35 when a hierarchical objective function is used, where, first, the number of vehicles is minimized and, afterwards, the total travel distance is minimized and the average quality is 0.42 when only the travel distance is minimized, while for the eight instances proposed by when a hierarchical objective function is used the average quality is 0.21 . Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms. Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper-heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance. Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. We put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief overview of existing applications.