Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms <s> There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms <s> Abstract This paper gives an overview of meta-heuristics methods utilized within the paradigm of multi-objective programming. This is an area of research that has undergone substantial expansion and development in the past decade. A literature review for this period is presented and analyzed. Analysis of the types of multi-objective techniques and meta-heuristics is undertaken and reasons for their use hypothesized. The strengths and weaknesses of meta-heuristic methods as applied to multi-objective programmes are discussed. Finally, a summary is given together with suggestions for future research. <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms <s> Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majority of metaheuristic algorithms, while efficiency analysis is equally challenging. In this paper, we intend to provide an overview of convergence and efficiency studies of metaheuristics, and try to provide a framework for analyzing metaheuristics in terms of convergence and efficiency. This can form a basis for analyzing other algorithms. We also outline some open questions as further research topics. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic 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> Meta-heuristic Algorithms <s> Metaheuristic algorithms (MHs) have a long history that can be traced back to genetic algorithms and evolutionary computing in the 1950s. Since February 2008, with the birth of the Firefly algorithm, MHs started to receive attention from researchers around the globe. Variants and new species of MH algorithms have bloomed like sprouts after rain. However, the necessity for creating more new species of such algorithms is questionable. It can be observed that these algorithms are fundamentally made up of several widely used core components. By explaining these components, the underlying design for a collection of the so-called modern MH optimisation algorithms is revealed. In this paper, the core components in some of the more popular MH algorithms are reviewed, thereby debunking the myths of their novelty, and perhaps dampening claims that something really `new' is invented simply by branding an MH search method with the name of another living creature. Counterintuitive experimentations have shown that by taking snapshots, anyone can show some improvements of an MH over another in some situation. Mixing certain components up indeed adds advantage over the original MH. The same goes to extending MH with slight functional modification. This work also serves as a general guideline and a reference for any algorithm architect who wants to create a new MH algorithm in the future. <s> BIB005 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms <s> The Whale Optimization Algorithm inspired by humpback whales is proposed.The WOA algorithm is benchmarked on 29 well-known test functions.The results on the unimodal functions show the superior exploitation of WOA.The exploration ability of WOA is confirmed by the results on multimodal functions.The results on structural design problems confirm the performance of WOA in practice. This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html <s> BIB006
The heuristic algorithms use a problem-specific heuristic, whereas the meta-heuristic algorithms form an efficient general purpose approach that includes a wide range of application areas ranging from finance to engineering and networking BIB004 . Meta-heuristics have been increasingly employed to solve hard optimization problems BIB003 that cannot be solved by any deterministic (exact) approach within a reasonable time BIB004 . Most of these algorithms are nature-inspired BIB003 , from simulated annealing (SA) BIB001 to genetic algorithms (GA)  , and from ant colony optimization (ACO)  to whale optimization algorithm (WOA) BIB006 . These algorithms are widely used to solve difficult problems such as combinatorial, highly non-linear and multi-modal optimization problems BIB005 . Each one of these algorithms has different advantages, therefore many efforts were made to design hybrid approaches that combine their benefits and ultimately attain better results BIB005 . The success of these algorithms is determined by achieving a balanced performance between the exploration and the exploitation BIB004 . Exploitation is useful for determining the most promising high-quality solutions in the search space, whereas exploitation is needed to concentrate search in some areas based on previous search results BIB004 . The main disadvantage of meta-heuristics is that these methods find good solutions rather than a guaranteed optimal solution. Another drawback is that these methods include a large number of parameters that need to be set in order to produce a good solution BIB002 . Learns from labelled data. Generalizes well based on a sufficient dataset. Requires a dataset that represents the system. The data is manually labelled by human experts, which is not appropriate for many real-world applications.