Machine learning and parallel processing are extremely commonly used to enhance computing power to induce knowledge from an outsized volume of data. To deal with the problem of complexity and high dimension, machine learning algorithms like Deep Reinforcement Learning (DRL) are used, while parallel processing algorithms like Parallel Particle Swarm Optimization (PPSO) are parallelized to speed up the operation and reduce the processing time to train the neural network. Due to the arrival of a large number of incoming tasks in the cloud environment, load balancing is an important issue. To solve this problem, the datacenter controller or an agent makes an intelligent decision to handle a large number of tasks within a minimum time period or at high speed. In this work, we proposed an effective scheduling algorithm named Deep Reinforcement Learning with Parallel Particle Swarm Optimization (DRLPPSO) to solve the load balancing problem and its various parameters with greater accuracy and high speed. Our experimental results show that our proposed scheduling algorithm increases the reward by 15.7%, 12%, and 13.1% when the task set is 2000 and improves the reward by 17.5%, 12.6%, and 15.3% when the task set is 4000, as compared to the Modified Particle Swarm Optimization (MPSO), Asynchronous Advantage Actor-Critic (A3C), and Deep Q-Network (DQN) techniques.

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

The Traveling Salesman Problem (TSP) is a combinatorial optimization problem that is useful in a number of applications. Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. Several variants of PSO have been proposed for solving discrete optimization problems like TSP. Among them, the basic algorithm is the swap sequence based PSO, however, it does not perform well in providing high quality solutions. To improve the performance of the swap sequence based PSO, this paper introduces an enhanced swap sequence based PSO algorithm by integrating the strategies of the Expanded PSO (XPSO) in the swap sequence based PSO. This is because although XPSO is only suitable for solving continuous optimization problems, it has a high performance among the variants of PSO. In our work, the TSP problem is used to model a package delivery system in the Kuala Lumpur area. The problem set consists of 50 locations in Kuala Lumpur. Our aim is to find the shortest route in the delivery system by using the enhanced swap sequence based PSO. We evaluate the algorithm in terms of effectiveness and efficiency while solving TSP. To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. The proposed algorithm is found to provide better solutions with shorter paths when applied to TSP as compared to swap sequence based PSO. However, the swap sequence based PSO is found to converge faster than the proposed algorithm when applied to TSP.

Optimization problems are usually solved using heuristic algorithms such as swarm intelligence algorithms owing to their ability of providing near-optimal solutions in a feasible amount of time. An example of optimization problem is the training of artificial neural networks to obtain the most optimal connection weights. Artificial Neural Network (ANN), being the most prominent machine learning algorithm, has a multitude of applications in a myriad of areas. Recently, the use of ANNs has risen exponentially owing to its effective ability of making conclusions based on certain inputs. This ability is primarily achieved during the training phase of the ANN, which is a vital process prior to being able to use the ANN. Gradient descent-based algorithms, which are usually used for the training process, often encounter the problem of local optima, thus being unable to obtain the optimal connection weights of the ANN. Metaheuristic algorithms, including swarm intelligence algorithms, have been found to be a better alternative to train ANNs. The Dragonfly Algorithm (DA) is a swarm intelligence algorithm that has been found to be more effective than multiple swarm intelligence algorithms. However, despite having a good performance, it still suffers from a low exploitation. In this paper, we propose to further improve the performance of DA by using hill climbing as a local search so as to enhance its low exploitation. The optimized DA algorithm is then used for training artificial neural networks which are employed for classification problems. Based on the experimental results, the optimized DA algorithm has a higher effectiveness than the original DA as the ANNs trained by the optimized DA have a lower root mean squared error and a higher classification accuracy than the ones trained by the original DA.

Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.INDEX TERMS Artificial neural network, swarm intelligence, optimization. I. INTRODUCTIONArtificial Neural Networks (ANNs) are computational models that simulate the biological neural network that constitutes the human brain to generate inferences based on certain given information. They are suitable for both supervised and unsupervised learning for solving a myriad of classification, regression, clustering, and association problems in a multitude of areas. Notably, ANN has been a prominent algorithm in the domain of machine learning, and has paved the way for the advancement in multiple areas such as natural language processing, fraud detection, computational biology, computer vision, unassisted control of vehicles, speech recognition, medical diagnosis and recommendation systems [1]. Recently, ANNs have been applied for making decisions in healthcare organizations [2], for forecasting the energy use in buildings [3], for the development of greenhouse technol-The associate editor coordinating the review of this manuscript and approving it for publication was Yeliz Karaca .