Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Bee colony optimization-based algorithms <s> Teamwork among software agents is an active research area in the agent community. Researchers are working to define efficient teamwork architecture but a limited progress has been made towards efficient task execution mechanisms by group of agents in collaboration and coordination with each other. We have developed a teamwork architecture to enhance the performance and task execution efficiency of software agents. This architecture has been inferred from Honey Bee teamwork strategies and a short comparison has been made between the similarities of Honey Bee and Agents teamwork. We have also classified and analyzed two different teamwork approaches depending upon the type of collaboration among agents using case study approach. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Bee colony optimization-based algorithms <s> Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers. <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Bee colony optimization-based algorithms <s> Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Bee colony optimization-based algorithms <s> Metaheuristics are widely recognized as efficient approaches for many hard optimization problems. This paper provides a survey of some of the main metaheuristics. It outlines the components and concepts that are used in various metaheuristics in order to analyze their similarities and differences. The classification adopted in this paper differentiates between single solution based metaheuristics and population based metaheuristics. The literature survey is accompanied by the presentation of references for further details, including applications. Recent trends are also briefly discussed. <s> BIB004 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Bee colony optimization-based algorithms <s> Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue. <s> BIB005
Bee colony optimization-based algorithms are new swarmintelligence-based algorithms motivated mainly by the collective behaviour of honeybee colony BIB004 . Artificial bee colony (ABC)  is one of the well-known and common used foraging-inspired optimization algorithm, which makes use of the bees' decentralized foraging behaviour. Interestingly, honey bees balance between exploiting known food sources and exploring potentially better food sources in the surrounding environment BIB004 . Bees in hive are divided into three sets: employed bees, onlooker bees and scouts. The number of employed bees is the same as the number of available food sources. When a food source is consumed an employed bee becomes a scout bee, which randomly searches for new food resources. Employed bees share the information about food resources with a certain probability using waggle dance BIB004 . ABC has a global search ability implemented through neighbourhood source production mechanism  BIB005 . In addition to ABC, many other new algorithms have been developed based on the cooperative behaviour of social honey bees BIB005 BIB001 BIB003 BIB002 . For more details, we refer the reader to BIB003 .