A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.


INTRODUCTION
Scheduling and optimization techniques have been applied to cloud environments, leading to a research area called evolutionary scheduling and optimization and represents the integration of artificial intelligence (AI) and operational research. Working with a cloud environment is complex and tedious in that there is a large need for better scheduling and optimization strategies. Since the computational problems are generally multi-objective in nature and complex, the traditional scheduling and optimization techniques are considered inadequate. With the emergence of AI techniques, these AI-based algorithms boosted the performance of scheduling and optimization approaches. Over the years, it has become abundantly manifest that these biodiversity of resources strategies are great enhancer for cloud computing work environments; therefore, they provide a robust, reliable, and enhanced strategies for better task achievement and workload distribution among the available cloud resources.
The evolutionary algorithms mimic species evolution based on Darwin's theory. They form a cluster of algorithms in which the genetic algorithm (GA) was the first proposed one. Recently, several SI algorithms have been applied widely to solve complex multi-objective problems. The ant colony optimization (ACO), for example, has been utilized in weather routing and in the travelling salesman problem (TSP). The particle swarm optimization (PSO) algorithm had a large implementation in constrained and unconstrained functional optimization problems. However, this article aims to review and summarize the most recent publications of SI techniques. In cloud computing, task scheduling is very important (Zuo et al., 2015) due to its direct effect in the performance of systems. As task scheduling problems are considered NP-hard problems, they need to meet user needs and improve the overall performance of the systems. This work would be of interest to the students and readers in this domain since SI algorithms lack enough supporting publications and resources as compared to the well-known methods like neural networks or genetic algorithms. Moreover, the challenge of identifying the changes of parameter settings in the mentioned SI methods in this work, and the hybrid associations between the existing SI approaches, facilitates the way for students and readers in this domain better understand and build on the existing techniques. Figure 1 presents the reviewed SI-based algorithms in this work.
This review contributes to the overall deployment of SI based techniques namely PSO, ABC, ACO, and FA, respectively, in scheduling and optimization problems. As many researchers had emphasized the importance of hybrid swarms in solving multi-objective scheduling and optimization problems, this review provided a reference in which it summarizes the recent hybrid techniques used to solve multi-objective problems. The review also highlighted the importance of parameter settings in scheduling problems. Additionally, it elaborated on the basics of SI methods that focused on the implementation of PSO, ABC, ACO, and FA in cloud environment scenarios. However, this study is structured into different sections starting with a basic introductory background. This is followed by a survey methodology which is then followed by sections separately dealing with SI-based algorithms in details. The algorithms discussed among SI-based are PSO, ACO, ABC, and FA studies. A future directions of action discusses the probability of some algorithms to gain prominence in research. Finally, a conclusions section has been introduced.

SURVEY METHODOLOGY
This review is conducted in several stages. First of all, the particular algorithms to be investigated were identified. Second, the authors paid attention to the hybrid nature between the identified SI-algorithms and the commonly parametric settings for problem solving. The aim of this review was to survey the literature to determine how SI algorithms were modified and hybridized to improve the performance of scheduling in cloud computing environment. However, this review scoped the latest publications for the following algorithms namely (PSO, ACO, ABC, and FA) in scheduling and optimization problems. Therefore, the last 5 years' publications for the mentioned algorithms were almost reviewed with a focus on determining the main parameters that have been changed or modified to optimize the performance of each SI algorithm in cloud computing environment. However, this review highlighted the hybrid approach between the reviewed algorithms. In addition, a summarized and brief applications for the identified algorithms were conducted. Finally, the review is unbiased since it just highlighted the main modified parameters in the above-mentioned SI algorithms and their applications in the last five years. It is noteworthy that other SI methods such as Cuckoo search and Levy flights were excluded from the current study, thereby decreasing the scope in order to have a detailed discussion.

Particle Swarm Optimization (PSO)
The PSO represents a well-known metaheuristic optimization technique due to its ease implementation in unsupervised, and complex problems. It is a reliable technique that has been used for treating several optimization problems. In fact, it is based on a physical model in which its transition rules are constructed by mimicking the social collective behavior observed from flocks of birds and/ or schools of fish (Sengupta, Basak & Peters, 2018). The PSO initializes a swarm of particles in which they traverse the search space looking for an optimal global best. In fact, each particle represents a potential solution. Suppose that X i(t ) be the position of any ith particle at a given time t. Then, its velocity is regulated based on End-While the current position being lower than the global best position (Sengupta, Basak & Peters, 2018). Table 1 illustrates the metaheuristic PSO algorithm.
As the particles search for food sources using original PSO, they may stick in local search. This may lead to less convergence in the search space. Due to some major shortcomings of the original PSO algorithm such as stagnation and local optima convergence, several studies were conducted to overcome such flaws. For these reasons, several researchers proposed a modified PSO or hybrid PSO computational strategies to enhance the performance of the original PSO algorithm. Generally, any given optimization problem concerns the selection of best possible solution that minimizes or maximizes a utility function based on some constraints. Therefore, SI-based approaches could introduce an acceptable and reliable solution to scheduling and optimization problems. SI-based solution for any given optimization problem, can be though as watching a team of players who cooperate together, share information, and update their positions collectively seeking a goal that formulates the rapprochement of victory (global best solution). Hence, seeking an efficient task allocation, a hybrid GA-PSO algorithm was proposed by Manasrah & Ali (2018). The proposed GA-PSO aimed at the reduction of the following parameters: makespan, cost, and load balance of cloud computing dependent tasks. However, another study by Ebadifard & Babamir (2018) presented a static task scheduling approach to improve PSO performance by utilizing a load balancing method. Another study by Alsaidy, Abbood & Sahib (2020) tried to improve PSO's initialization using both ''longest job to fastest processor (LJFP)'' and ''minimum completion time (MCT)'' methods. Additionally, Zhou et al. (2018) proposed a cloud computing model based on energy consumption named M-PSO that is capable of handling the slow convergence issue and local optimum. Tabrizchi  determine the most important QoS considerations in cloud computing environments. The literature claimed that many researchers tried to develop new PSO scheduling strategies to enhance optimal solution convergence by introducing different load balancing methods. However, vectors in PSO can be updated using Eqs. (1), (2) below. Figure 2 illustrates the basic PSO algorithm.

Ant Colony Optimization (ACO)
Ant colony optimization algorithm has a great advantage in addressing the combinatorial optimization problems. Several studies had investigated the scheduling tasks using ant colony algorithm in cloud computing environment. They normally categorized according to their targets focus such as scheduling efficiency, system performance, or cost. ACO represents an intelligent algorithm for path planning (Dai et al., 2019;Jovanovic, Tuba & Voß, 2016;Wang, Lin & Wang, 2016). It has a strong calculative mechanism (Ahmed et al., 2020). Generally, it is used for optimization by updating the pheromone trails and orienting the ants around the search space by which each ant generates a new fitness function to be used for generating an overall global fitness. The next state in ACO strategy is determined by the roulette wheel method in which it will be repeated till the goal point has been achieved. Upon the completion of each iteration, the ants update the pheromone trails along the length of path planning. In the available literature, the ACO has been applied mostly to society detection with single objective (Shahabi Sani, Manthouri & Farivar, 2020), while it has been applied to a multi-objective ACO optimization using decomposition (Mu et al., 2019). In fact, ant colony inspired researchers in how ants find the best route to food source. Updating the pheromone trails is proposed by Ekmekci (2019) using a modified ACO that memorizes solution costs.
To solve the TSP problem, Tamura, Sakiyama & Arizono (2021) introduced a modified ACO algorithm using individual memories (IM) named Ant System Using Individual Memories (ASIM) that aims to optimize ant's diversity in the search space. Seeking an optimal solution for ship-weather routing multi-objective optimization problem, Zhang et al. (2021) introduced an improved ACO algorithm considering several parameters such as fuel consumption, sailing time, and navigation safety. A unified adaptive ACO algorithm was proposed by Yuan, Yuan & Huang (2017) related to SNP epistasis with multi-objective functions detection GWAS datasets. In addition, Senthil Kumar & Venkatesan (2019) proposed a hybrid genetic-PSO (HGPSO) algorithm to solve the problem of task scheduling. Another modified PSO algorithm named IPSO was introduced by Yu (2020) to optimize resource scheduling efficiency. The generated solution around the gbest for each ant can be expressed as in Eq. (3). The ACO algorithm diagram can be seen in Fig. 3.

Artificial Bee Colony Optimization (ABC)
The ABC represents a meta-heuristic approach for investigating the behavior of bees. A typical ABC's applications can be found in several areas such as cloud computing, image processing, big data analysis, and neural networks. The ABC algorithm represents the most successful optimization SI algorithms (Aslan & Karaboga, 2020

Firefly Algorithm (FA)
The firefly algorithm (FA) represents a metaheuristic approach that mimics the flashing behavior of fireflies (Farahlina Johari et al., 2017 (2017) proposed a modified FA algorithm that ranks the fireflies based on a quick sort algorithm instead of bubble sort technique.
To handle the optimal operation of thermal generating unit's problem, an improved FA algorithm named improved firefly algorithm (IFA) was proposed by Nguyen, Quynh & Van Dai (2018) to reduce the cost of electricity generation fuel. Another study by Ma et al. (2019) aimed to overcome the problem of FA being fall into slow convergence and local extremum, by using a reverse learning initialization and Levy perturbation mechanisms based on FA algorithm. A hybrid optimizer based on PSO and FA entitled ''FAPSO'' has been proposed by Xia et al. (2018) to sanction the flies to explore more favorable sub-regions. Another study by Tighzert, Fonlupt & Mendil (2019) tried to reduce the computational cost and memory storage of the traditional FA algorithm by introducing a compact firefly approach that uses a minimum computational cost. Wahid & Ghazali (2019) proposed pattern search (PS) to terminate the FA to handle the drawback of the standard FA in its ending phase as it fails to get the optimal value since there is no observed improvement to the quality of the outcomes. A study by Hussein & Jaber (2020) introduced a modified FA algorithm to handle unit commitment problems. They claimed that the modified FA algorithm is more efficient than the classical FA in the selection of generator and error amid load and generation. To solve several non-linear ''convex'' optimal power flow (OPF), a hybrid algorithm consists of FA and PSO named ''HFPSO'' algorithm was introduced by Khan et al. (2020). Additionally, Dai, Liang & Zhang (2020) proposed a new FA-based algorithm to search for the optimal solution for uplift effect in the high-pressure jet grouting (HPJG) project. Another study (Peng et al., 2018) proposed a hybrid fish swarm algorithm based on the behavior of Lévy flight and firefly named LFFSA to overcome the problem of local optimum convergence. Several components (logistic and Gauss, Lévy flight, and adaptive inertia weight) were utilized by Chou & Ngo (2017) to introduce a modified FA algorithm (MFA) to optimize the multidimensional structural design. To adjust the parameters of the proportional integral-derivative (PID) controller in a buck converter, a hybridized algorithm using FA and PSO named HFPSO was proposed by Ekinci et al. (2019). Figure 4 represents the common modified and hybrid FA algorithms.
V.1 FA Position Update In FA, the distance between any two fireflies i and j at X i and X j can be calculate using Cartesian distance as per Eq. (6).
where k represents spatial coordinate component andd represents the number of dimensions. However, the movement of any firefly i towards an attractive firefly j can be described as per Eq. (7) below.
where α randomization parameter and r and is function generator.

The future research directions of SI algorithms
This review explored the potential applications of PSO, ABC, ACO, and FA algorithms. However, there was a little research on multi-objective scheduling and optimization compared to a single-objective approach. This indeed highlights the importance of utilizing the swarm algorithms in solving complex multi-objective problems. As many scholars developed a modified SI algorithm, Table 2 summarizes the modified SI methods and their applications. It can be utilized to further introduce new modified or hybrid algorithms and to be used for multi-objective complex problems optimization. PSO algorithm, for instance, still has a potential for more optimized modifications in which it can be self-adaptive to handle loss of diversity, and/ or local optima stagnation issues. Moreover, despite the modifications that carried out on ACO algorithm, still it can be improved for applications that are more practical. Expert prior knowledge can also be applied to ACO algorithm in future, which believed to improve efficiency of ACO algorithm. Additionally, ABC can be applied to parallel computation in run-time by tuning its parameters in which this could optimize solutions to the NP-Hard combinatorial problems. Path prioritization represents a promising challenge for ABC algorithm. However, there are several areas in which FA algorithm can be utilized to cope with. Applying FA's variations such as Gaussian, and/ or multi-population in biomedical engineering (BME) and healthcare (HC) areas can be fruitful research opportunities. Table 3 illustrates the most used parameters for modification. However, the main challenges in SI-based in scheduling and optimization problems are multi-objective search, multidimensional numeric problems, fitness function improvement, local optima escaping, global optima finding. Figure 5 shows a count analysis for the reviewed articles in this survey.

CONCLUSIONS
This review focused on the overall deployment of SI-based algorithms (PSO, ABC, ACO, and FA, respectively) in scheduling and optimization problems. As stated in the literature, several scholars tried to introduce new SI-based scheduling strategies to optimize search space exploration and exploitation by proposing a variety of parametric-modification and load balancing schemes. However, many researchers had emphasized the importance of hybrid swarms in solving multi-objective scheduling and optimization problems. As task-scheduling represents a major issue in cloud computing environment, many hybrid techniques were proposed by scholars to improve the performance of traditional SI algorithms and to allocate the suitable cloud resources to user tasks. However, the reviewed algorithms in general lose solution quality when their dimensionality increased. It is noteworthy that parameter settings for one problem do not operate for every problem. However, the basics of SI methods that target the implementation and illustration of PSO, ABC, ACO, and FA in cloud environment scenarios have been elaborated in detail. In addition, some of the most current and noteworthy applications of SI-based techniques for cloud environment scheduling we are surveyed. The future plan for this work aims to develop a hybrid SI strategy utilizing the most dominant parameters such as inertia weight, acceleration coefficients, and Tabu list size.

ADDITIONAL INFORMATION AND DECLARATIONS Funding
The authors received no funding for this work.