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Location-based services in wireless sensor networks demand precise information of locations of sensor nodes. Range-based localization, a problem formulated as a two-dimensional optimization problem, has been addressed in this paper as a multistage exercise using bio-inspired metaheuristics. A modified version of the shuffled frog leaping algorithm (MSFLA) has been developed for accurate sensor localization. The results of MSFLA have been compared with those of geometric trilateration, artificial bee colony and particle swarm optimization algorithms. Dependance of localization accuracies achieved by these algorithms on the environmental noise has been investigated. Simulation results show that MSFLA delivers the estimates of the locations over 30% more accurately than the geometric trilateration method does in noisy environments. However, they involve higher computational expenses. The MSFLA delivers the most accurate localization results; but, it requires the longest computational time.

Recently, software-defined networking (SDN) is the most promising solutions for future network. In SDN, networking architecture combines central management along with network programmability. It separates network management from the underlying network infrastructure, allowing administrators to dynamically adjust network-wide traffic flow to meet the changing needs. Due to these unique features, SDN easily manages network, gives better performance than traditional network, and provides higher flexibility. In this paper, we have proposed an ant colony optimization-based routing protocol targeting to achieve significant value of various QoS parameters. The proposed methodology is based on the classification of various types of traffic. To classify the traffic, we introduce to the bandwidth requirement for multimedia traffic like audio, video, and text. Here, our method is compared with particle swarm optimization-software-defined network (PSO-SDN), The results obtained in terms of various QoS parameters show that ant colony optimization outperforms to PSO-SDN. Finally, we concluded the paper with few suggested open research challenges.

Proportional, integral and derivative (PID) and fuzzy logic controller (FLC) are remarkably successful. Metaheuristics, such as particle swarm optimization (PSO) are becoming popular in tuning PID parameters. Details of the development and the comparison of computationally intelligent controllers have been presented in this paper. A PSO-based PID controller (PSO-PIDC) and an FLC for the ball and beam system have been developed. Physical and mathematical models of the ball and beam system constructed for the empirical investigation have been presented. Further, PSO-FLC, a hybrid controller of PSO-PIDC and FLC has been proposed. The offline tuning precision of the PSO-PIDC and the online tuning capability of the FLC have been combined in the PSO-FLC. This underscores its suitability for real-time dynamic control applications.

The challenge of determining optimal transformation parameters for image registration has been treated traditionally as a multidimensional optimization problem. Non-rigid registration of medical images has been approached in this article using the particle swarm optimization algorithm, dragonfly algorithm, and the artificial bee colony algorithm. Brief introductions to these algorithms have been presented. Results of MATLAB simulations of medical image registration approached through these algorithms have been analyzed. The simulation shows that the dragonfly algorithm results in higher quality image registration, but takes longer to converge. The trade-off issue between the quality of registration and the computing time has been brought forward. This has a strong impact on the choice of the most suitable algorithm for medical applications, such as monitoring of tumor progression.

The search for transformation parameters for image registration has been treated traditionally as a multidimensional optimization problem. Non-rigid registration of medical images has been approached in this paper using the particle swarm optimization algorithm and the artificial bee colony algorithm (ABC). Brief introductions to these algorithms have been presented. Results of Matlab simulations of medical image registration approached through these algorithms have been analyzed. The results show that the ABC algorithm results in higher quality of image registration; but, takes longer to converge. The tradeoff issue between the quality of registration and the computing time has been brought forward. This has a strong impact on the choice of the most suitable algorithm for a specific medical application.

Accurate localization of randomly deployed sensor nodes is critically important in wireless sensor networks (WSNs) deployed for monitoring and tracking applications. The localization challenge has been posed as a multidimensional global optimization problem in earlier literature. Many swarm intelligence algorithms have been proposed for accurate localization. The untapped vast potential of the artificial bee colony (ABC) algorithm has inspired the research presented in this paper. The ABC algorithm has been investigated as a tool for anchor-assisted sensor localization in WSNs. Results of Matlab simulation of ABC-based multistage localization have been presented. Further, the results are compared with those of the localization method based on the particle swarm optimization (PSO) algorithm. A comparison of the performances of ABC and PSO algorithms has been presented in terms of the number of nodes localized, localization accuracy and the computation time. The results show that the ABC algorithm delivers higher accuracy of localization than the PSO algorithm does; but, it takes longer to converge. This results in a trade off between speed and accuracy of localization in WSNs.

Wireless-sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bioinspired techniques. Particle swarm optimization (PSO) is a simple, effective, and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering, and data aggregation. This paper outlines issues in WSNs, introduces PSO, and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues.

Optimal deployment and accurate localization of sensor nodes have a strong influence on the performance of a wireless sensor network (WSN). This paper considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). Such a deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. The objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. Bioinspired algorithms, particle swarm optimization (PSO) and bacterial foraging algorithm (BFA), are presented in this paper for image segmentation. In addition, PSO and BFA are presented for distributed localization of the deployed nodes. Image segmentation for autonomous deployment and distributed localization are formulated as multidimensional optimization problems, and PSO and BFA are used as optimization tools. Comparisons of the results of PSO and BFA for autonomous deployment and distributed localization are presented. Simulation results show that both the algorithms perform multilevel image segmentation faster than the exhaustive search for optimal thresholds. Besides, PSO-based localization is observed to be faster, and BFA-based localization is more accurate.

A novel action-dependent adaptive critic design (ACD) is developed for dynamic optimization. The proposed combination of a particle swarm optimization-based actor and a neural network critic is demonstrated through dynamic sleep scheduling of wireless sensor motes for wildlife monitoring. The objective of the sleep scheduler is to dynamically adapt the sleep duration to node's battery capacity and movement pattern of animals in its environment in order to obtain snapshots of the animal on its trajectory uniformly. Simulation results show that the sleep time of the node determined by the actor critic yields superior quality of sensory data acquisition and enhanced node longevity.

Feedforward neural networks such as multilayer perceptrons (MLP) and recurrent neural networks are widely used for pattern classification, nonlinear function approximation, density estimation and time series prediction. A large number of neurons are usually required to perform these tasks accurately, which makes the MLPs less attractive for computational implementations on resource constrained hardware platforms. This paper highlights the benefits of feedforward and recurrent forms of a compact neural architecture called generalized neuron (GN). This paper demonstrates that GN and recurrent GN (RGN) can perform good classification, nonlinear function approximation, density estimation and chaotic time series prediction. Due to two aggregation functions and two activation functions, GN exhibits resilience to the nonlinearities of complex problems. Particle swarm optimization (PSO) is proposed as the training algorithm for GN and RGN. Due to a small number of trainable parameters, GN and RGN require less memory and computational resources. Thus, these structures are attractive choices for fast implementations on resource constrained hardware platforms.

Wireless sensor network (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Sensor Localization is a fundamental challenge in WSN. In this paper localization is modeled as a multi dimensional optimization problem. A comparison study of energy of processing and transmission in a wireless node is done, main inference made is that transmission process consumes more than processing. An energy efficient distributed localization technique is proposed. Distributive localization is addressed using swarm techniques Particle Swarm Optimization (PSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) because of their quick convergence to quality solutions. The performances of both algorithms are studied. The accuracy of both algorithms is analyzed using parameters such as number of nodes localized, computational time and localization error. A simulation was conducted for 100 target nodes and 20 beacon nodes, the results show that the PSO based localization is faster and CLPSO is more accurate.

Wireless Sensor Networks (WSNs) consist of distributed autonomous devices which sense the environmental or physical conditions cooperatively and pass the information through the network to a base station. Sensor Localization is a fundamental challenge in WSN. Location information of the node is critically important to detect an event or to route the packet via the network. In this paper localization is modeled as a multi dimensional optimization problem. This problem is solved using bio inspired algorithms, because of their quick convergence to quality solutions. Distributive localization is addressed using Particle Swarm Optimization (PSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). The performances of both algorithms are studied. The accuracy of both algorithms is analyzed using parameters such as number of nodes localized, computational time and localization error. Comparison of both the results is presented. A simulation was conducted for 100 target nodes and 20 beacon nodes, which resulted in CLPSO being 80.478% accurate, and PSO 61.48% accurate. The simulation results show that the PSO based localization is faster and CLPSO is more accurate.

Many applications of wireless sensor networks (WSNs) require location information of the randomly deployed nodes. A common solution to the localization problem is to deploy a few special beacon nodes having location awareness, which help the ordinary nodes to localize. In this approach, non-beacon nodes estimate their locations using noisy distance measurements from three or more non-collinear beacons they can receive signals from. In this paper, the ranging-based localization task is formulated as a multidimensional optimization problem, and addressed using bio-inspired algorithms, exploiting their quick convergence to quality solutions. An investigation on distributed iterative localization is presented in this paper. Here, the nodes that get localized in an iteration act as references for remaining nodes to localize. The problem has been addressed using particle swarm optimization (PSO) and bacterial foraging algorithm (BFA). A comparison of the performances of PSO and BFA in terms of the number of nodes localized, localization accuracy and computation time is presented.

There exist several application scenarios of mobile ad hoc networks (MANET) in which the nodes need to locate a target or surround it. Severe resource constraints in MANETs call for energy efficient target localization and collaborative navigation. Centralized control of MANET nodes is not an attractive solution due to its high network utilization that can result in congestions and delays. In nature, many colonies of biological species (such as a flock of birds) can achieve effective collaborative navigation without any centralized control. Particle swarm optimization (PSO), a popular swarm intelligence approach that models social dynamics of a biological swarm is proposed in this paper for network-centric target localization in MANETs that are enhanced by mobile robots. Simulation study of two application scenarios is conducted. While one scenario focuses on quick target localization, the other aims at convergence of MANET nodes around the target. Reduction of swarm size during PSO search is proposed for accelerated convergence. The results of the study show that the proposed algorithm is effective in network-centric collaborative navigation. Emergence of converging behavior of MANET nodes is observed.

PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation of distribution algorithm and two PSO variants. These algorithms are tested on benchmark functions having high dimensionalities. Results indicate that the methods strengthen the global optimization abilities of PSO and therefore, serve as attractive choices to determine solutions to optimization problems in areas including sensor networks.