Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

/pdf/a-comprehensive-survey-on-particle-swarm-optimization-1d380v5xx1.pdf

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

https://cyberleninka.org/article/n/468779.pdf

A review of metaheuristic scheduling techniques in cloud computing

Symbiotic Organism Search optimization based task scheduling in cloud computing environment

Predicting host CPU utilization in the cloud using evolutionary neural networks

It is possible for IT service providers to provide computing resources in an pay-per-use way in Cloud Computing environments. At the same time, terminal users can also get satisfying services conveniently. But if we take only execution time into consideration when scheduling the cloud resources, it may occur serious load imbalance problem between Virtual Machines (VMs) in Cloud Computing environments. In addition to solve this problem, a new task scheduling model is proposed in this paper. In the model, we optimize the task execution time in view of both the task running time and the system resource utilization. Based on the model, a Particle Swarm Optimization (PSO) --- based algorithm is proposed. In our algorithm, we improved the standard PSO, and introduce a simple mutation mechanism and a self-adapting inertia weight method by classifying the fitness values. In the end of this paper, the global search performance and convergence rate of our adaptive algorithm are validated by the results of the comparative experiments.

A PSO-Based algorithm for load balancing in virtual machines of cloud computing environment

The big data explosion has sparked a strong demand for high-performance data processing. Meanwhile, the rapid development of networked computing systems, coupled with the growth of Divisible-Load Theory (DLT) as an innovative technology with competent scheduling strategies, provides a practical way of conducting parallel processing with big data. Existing studies in the area of DLT usually consider the scheduling problem with regard to fine-grained divisible workloads. However, numerous big data loads nowadays can only be abstracted as coarse-grained workloads, such as large-scale image classification, context-dependent emotional analysis and so on. In view of this, this paper extends DLT from fine-grained to coarse-grained divisible loads by establishing a new multi-installment scheduling model. With this model, a subtle heuristic algorithm was proposed to find a feasible load partitioning scheme that minimizes the makespan of the entire workload. Simulation results show that the proposed algorithm is superior to the up-to-date multi-installment scheduling strategy in terms of achieving a shorter makespan of workloads when dealing with coarse-grained divisible loads.

/pdf/extension-of-divisible-load-theory-from-scheduling-fine-2du4uis3.pdf

Extension of Divisible-Load Theory from Scheduling Fine-Grained to Coarse-Grained Divisible Workloads on Networked Computing Systems

Fog computing relieves the Internet of Things (IoT) from a heavy burden of workload computation, consequently minimizes workload makespan, and reduces the considerable energy consumption of IoT devices. However, the fog-enabled IoT poses a risk to data security due to the intrinsic open and distributed structure of the fog. Hence, it is imperative to search for an optimal scheduling strategy on fog nodes to minimize workload makespan, while guaranteeing high data security. In this study, we investigate the trade-off between security and makespan and propose a multiobjective optimization model to optimize them simultaneously. To achieve this goal, we design an evolutionary algorithm together with two effective measures to handle large-scale partitionable workloads: a conflict-free multi-installment scheduling (ConfMIS) strategy as well as a security criterion based on Eigenvector centrality measure. Experimental results show that the proposed algorithm can generate a set of widely spread and uniformly distributed solutions in the decision space. Among these representative solutions, system designers/decision-makers can choose their favorite one based on specific IoT application demands.

Security and Makespan Trade-Off Strategy in Fog-Enabled IoT Networks

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Papers

A PSO-Based algorithm for load balancing in virtual machines of cloud computing environment

Extension of Divisible-Load Theory from Scheduling Fine-Grained to Coarse-Grained Divisible Workloads on Networked Computing Systems

Security and Makespan Trade-Off Strategy in Fog-Enabled IoT Networks