This paper investigates quality-driven cross-layer optimization for resource allocation in direct sequence code division multiple access wireless visual sensor networks. We consider a single-hop network topology, where each sensor transmits directly to a centralized control unit (CCU) that manages the available network resources. Our aim is to enable the CCU to jointly allocate the transmission power and source-channel coding rates for each node, under four different quality-driven criteria that take into consideration the varying motion characteristics of each recorded video. For this purpose, we studied two approaches with a different tradeoff of quality and complexity. The first one allocates the resources individually for each sensor, whereas the second clusters them according to the recorded level of motion. In order to address the dynamic nature of the recorded scenery and re-allocate the resources whenever it is dictated by the changes in the amount of motion in the scenery, we propose a mechanism based on the particle swarm optimization algorithm, combined with two restarting schemes that either exploit the previously determined resource allocation or conduct a rough estimation of it. Experimental simulations demonstrate the efficiency of the proposed approaches.

Standard Particle Swarm Optimization (PSO) allocates the total available computational budget, in terms of function evaluations, equally among the particles at each iteration of the algorithm. The present work introduces an alternative, which employs neighborhood ranking for allocating the computational budget to the particles. The proposed PSO variant favors the particles that belong to more promising neighborhoods by providing them with more function evaluations than the rest, based on a stochastic neighborhood selection scheme. Preliminary experimental results on standard test problems reveal that the proposed approach is highly competitive.

We propose an adaptive Memetic Particle Swarm Optimization algorithm where local search is selected from a pool of different algorithms. The choice of local search is based on a probabilistic strategy that uses a simple metric to score the efficiency of local search. Our study investigates whether the pool size affects the memetic algorithm's performance, as well as the possible benefit of using the adaptive strategy against a baseline static one. For this purpose, we employed the memetic algorithms framework provided in the recent MEMPSODE optimization software, and tested the proposed algorithms on the Benchmarking Black Box Optimization (BBOB 2012) test suite. The obtained results lead to a series of useful conclusions.

Applications that include real-time video delivery are demanding on network performance, while the various network resources are usually constrained. This fact boosts the need for efficient resource management, aiming at the amelioration of the video quality that reaches the end-user. The present paper considers a wireless direct sequence code division multiple access visual sensor network, which employs a cross-layer design. The objective is the maximization of the nodes' utilities under the constraints of a maximum bit rate and a maximum power level for each node. In this vein, the Kalai-Smorodinsky bargaining solution is applied, which is geometrically derived from the graphical representations of the utility sets, under a centralized topology. Ultimately, we have to deal with an optimization problem that concerns the optimal determination of the source coding rates, channel coding rates, and power levels of all nodes of the network, under certain modeling conditions. The experimental results provided by the Kalai-Smorodinsky bargaining solution are compared with results using the Nash bargaining solution and two other schemes that aim at the maximization of an unweighted and a weighted version of the total network utility, respectively. A metric that captures fairness and performance issues is used in order to compare the performance of the schemes. The results are also evaluated in terms of the total consumed power relative with the total achieved utility.

Noisy optimization problems arise very often in real-life applications. A common practice to tackle problems characterized by uncertainties, is the re-evaluation of the objective function at every point of interest for a fixed number of replications. The obtained objective values are then averaged and their mean is considered as the approximation of the actual objective value. However, this approach can prove inefficient, allocating replications to unpromising candidate solutions. We propose a hybrid approach that integrates the established Particle Swarm Optimization algorithm with the Reinforcement Learning approach to efficiently tackle noisy problems by intelligently allocating the available computational budget. Two variants of the proposed approach, based on different selection schemes, are assessed and compared against the typical alternative of equal sampling. The results are reported and analyzed, offering significant evidence regarding the potential of the proposed approach.

We propose a novel priority-based approach that enables the optimal control of the transmission power and the use of the available network resources of a multihop Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Visual Sensor Network (WVSN). TheWVSN nodes can either monitor different scenes (source nodes) or retransmit videos of other nodes (relay nodes). Moreover, in real environments the source nodes monitor different scenes that may be of dissimilar importance. Hence a higher end-to-end quality is demanded for those nodes that are assigned a higher priority. Overall, each node has different power and resource requirements, and therefore a global optimization approach is required. For the purpose of enhancing the delivered video quality of the source nodes with respect to their priorities, we define and suggest the use of priority-based optimization criteria. Experimental results that assess the proposed approach are provided and conclusions are drawn.

In the present paper, we deal with the problem of allocating the network resources in multi-rate Direct Sequence Code Division Multiple Access (DS-CDMA) Visual Sensor Networks (VSNs). We consider a single-cell system where each node uses the same chip rate, but can transmit at a different bit rate. In wireless VSNs, we face the constraints of limited power lifetime and of an error-prone environment, mainly due to attenuation and interference. The proposed cross-layer scheme enables the Centralized Control Unit (CCU) to jointly allocate the transmission power, the transmission bit rate and the source-channel coding rates for each VSN node in order to optimize the delivered video quality. The transmission power of each visual sensor assumes values from a continuous range, while the rest of the resources take values chosen from an available discrete set. The numerical results demonstrate the performance of the proposed multi-rate scheme vs a single-rate system.

We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN). We use the Nash Bargaining Solution (NBS) from game theory in order to determine the transmission power and source and channel coding rate for each node. The NBS assumes that the nodes negotiate (using the help of a centralized control unit) in order to jointly determine their transmission parameters. The transmission powers are allowed to take continuous values, whereas the source and channel coding rate combination can only assume discrete values. Thus, the resulting optimization problem is a mixed-integer optimization task and is solved using Particle Swarm Optimization (PSO). Experimental results are provided and conclusions are drawn.

We propose a novel approach for the optimized network resource management of a Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor network. The visual sensors monitor different scenes of varying motion levels, thus different network resources need to be allocated to each sensor. For each recorded scene, our approach considers its individual content-related parameters, in contrast with previous methods that group the sensors according to the amount of motion present in the scene and assign the same transmission parameters to all members of a group. Cross-layer optimization is used across the physical, link and application layers. Based on quality-driven criteria (under the constraint of constant chip rate), we allocate to each node a suitable continuous power level, a discrete source coding rate and a discrete channel coding rate. The resulting problem is solved using the Particle Swarm Optimization algorithm. Experimental results demonstrate the performance and efficiency of each criterion.

High–dimensional optimization problems appear very often in demanding applications. Although evolutionary algori-thms constitute a valuable tool for solving such problems, their standard variants exhibit deteriorating performance as dimension increases. In such cases, cooperative approaches have proved to be very useful, since they divide the com-putational burden to a number of cooperating subpopula-tions. In contrast, Micro–evolutionary approaches consti-tute light versions of the original evolutionary algorithms that employ very small populations of just a few individu-als to address optimization problems. Unfortunately, this property is usually accompanied by limited efficiency and proneness to get stuck in local minima. In the present work, an approach that combines the basic properties of coopera-tion and Micro-evolutionary algorithms is presented for the Differential Evolution algorithm. The proposed Cooperative Micro–Differential Evolution approach employs small coope-rative subpopulations to detect subcomponents of the origi-nal problem solution concurrently. The subcomponents are combined through cooperation of subpopulations to build complete solutions of the problem. The proposed approach is illustrated on high-dimensional instances of five widely used test problems with very promising results. Compari-sons with the standard Differential Evolution algorithm are also reported and their statistical significance is analyzed.

A technique for the visualization of stochastic population-based algorithms in multidimensional problems with known global minimizers is proposed. The technique employs projections of the populations in the 2-dimensional vector space spanned by the two extremal eigenvectors of the Hessian matrix of the objective function at a global minimizer. This space condenses information regarding the shape of the objective function around the given minimizer. The proposed approach can provide intuition regarding the behavior of the algorithm in unknown high-dimensional problems. It also provides an alternative visualization framework for problems of any dimension, which alleviates drawbacks of the most popular projection methods. The proposed technique is illustrated for three well-known population-based algorithms, namely, differential evolution, covariance matrix adaptation evolution strategies and particle swarm optimization, on three test problems of different dimensionality.

We propose a Particle Filter model that incorporates Particle Swarm Optimization for predicting systems with multiplicative noise. The proposed model employs a conventional multiobjective optimization approach to weight the likelihood and prior of the filter in order to alleviate the particle impoverishment problem. The resulting scheme is tested on a well-known test problem with multiplicative noise. Results are promising, especially in cases of high system and measurement noise levels.

The computation of periodic orbits of nonlinear mappings is very important for studying and better understanding the dynamics of complex systems. Evolutionary algorithms have shown to be an efficient alternative for the computation of periodic orbits in cases where the inherent properties of the problem at hand render gradient-based methods invalid. Such cases usually involve nondifferentiable mappings or poorly behaved partial derivatives. We propose a Memetic Particle Swarm Optimization algorithm that exploits Shannon’s information entropy for decision making in swarm level, as well as a probabilistic decision making scheme in particle level, for determining when and where local search is applied. These decisions have a significant impact on the required number of function evaluations, especially in cases where high accuracy is desirable. Experimental results are performed on well-known problems and useful conclusions are derived.

Fuzzy cognitive maps constitute an important simulation methodology that combines neural networks and fuzzy logic. The Fuzzy cognitive maps designed by the experts can be enhanced significantly through learning algorithms, which proved to increase their efficiency and accuracy of simulation. Recently, learning algorithms that employ particle swarm optimization for the minimization of properly defined objective functions have been introduced. In this work, we enhance these learning schemes by incorporating local search in PSO, resulting in a memetic particle swarm optimization learning algorithm. Three variants of the memetic algorithm are applied successfully for the optimization of an Ecological Industrial Park simulation system and they are compared also with the established particle swarm optimization learning schemes. Results are reported and discussed, deriving useful conclusions

We investigate the performance of the recently proposed unified particle swarm optimization algorithm on two categories of operations research problems, namely minimax and integer programming problems. Different variants of the algorithm are employed and compared with established variants of the particle swarm optimization algorithm. Statistical hypothesis testing is performed to justify the significance of the results. Conclusions regarding the ability of the unified particle swarm optimization method to tackle operations research problems as well as on the performance of each variant are derived and discussed.

A parallel, multi-population differential evolution algorithm for multiobjective optimization is introduced. The algorithm is equipped with a domination selection operator to enhance its performance by favouring non-dominated individuals in the populations. Preliminary experimental results on widely used test problems are promising. Comparisons with the VEGA approach are provided and discussed.

This paper presents approaches for effectively computing all global minimizers of an objective function. The approaches include transformations of the objective function through the recently proposed deflection and stretching techniques, as well as a repulsion source at each detected minimizer. The aforementioned techniques are incorporated in the context of the particle swarm optimization (PSO) method, resulting in an efficient algorithm which has the ability to avoid previously detected solutions and, thus, detect all global minimizers of a function. Experimental results on benchmark problems originating from the fields of global optimization, dynamical systems, and game theory, are reported, and conclusions are derived.

During the last decade, numerous heuristic search methods for solving multi-objective optimization problems have been developed. Population oriented approaches such as evolutionary algorithms and particle swarm optimization can be distinguished into the class of archive-based algorithms and algorithms without archive. While the latter may lose the best solutions found so far, archive based algorithms keep track of these solutions. In this article, a new particle swarm optimization technique, called DOPS, for multi-objective optimization problems is proposed. DOPS integrates well-known archiving techniques from evolutionary algorithms into particle swarm optimization. Modifications and extensions of the archiving techniques are empirically analyzed and several test functions are used to illustrate the usability of the proposed approach. A statistical analysis of the obtained results is presented. The article concludes with a discussion of the obtained results as well as ideas for further research.

We introduce a new algorithm for fuzzy cognitive maps learning. The proposed approach is based on the particle swarm optimization method and it is used for the detection of proper weight matrices that lead the fuzzy cognitive map to desired steady states. For this purpose a properly defined objective function that incorporates experts' knowledge is constructed and minimized. The application of the proposed methodology to an industrial control problem supports the claim that the proposed technique is efficient and robust.

The existence of roots of functions is a topic of major significance in nonlinear analysis, and it is directly related to the problem of detection of extrema of a function. The topological degree of a function is a mathematical tool of great importance for investigating the existence and the number of roots of a function with certainty. For the computation of the topological degree according to Stenger's theorem, a sufficient refinement of the boundary of the polyhedron under consideration is needed. The sufficient refinement can be computed using the optimal complexity algorithm of Boult and Sikorski. However, the application of this algorithm requires the computation of the infinity norm on the boundary of the polyhedron under consideration as well as an estimation of the Lipschitz constant of the function. We introduced a new technique for the computation of the infinity norm on the polyhedron's boundary as well as for the estimation of the Lipschitz constant. The proposed approach is illustrated on several test problems and the results are reported and discussed.

Periodic orbits of nonlinear mappings play a central role in the study of dynamical systems. Traditional root finding algorithms, such as the Newton-family algorithms, have been widely applied for the detection of periodic orbits. However, in the case of discontinuous/nondifferentiable mappings and mappings with poorly behaved partial derivatives, this approach is not valid. In such cases, stochastic optimization algorithms have proved to be a valuable tool. In this paper, a new approach for computing periodic orbits through particle swarm optimization is introduced. The results indicate that the algorithm is robust and efficient. Moreover, the method can be combined with established techniques, such as deflection, to detect several periodic orbits of a mapping. Finally, the minor effort which is required to implement the proposed approach renders it an efficient alternative for computing periodic orbits of nonlinear mappings.

This paper investigates the ability of the Particle Swarm Optimization (PSO) method to cope with minimax problems through experiments on well-known test functions. Experimental results indicate that PSO tackles minimax problems effectively. Moreover, PSO alleviates difficulties that might be encountered by gradient-based methods, due to the nature of the minimax: objective function, and potentially lead to failure. The performance of PSO is compared with that of other established approaches, such as the sequential quadratic programming (SQP) method and a recently proposed smoothing technique.

The investigation of the performance of the particle swarm optimization (PSO) method in integer programming problems, is the main theme of the present paper. Three variants of PSO are compared with the widely used branch and bound technique, on several integer programming test problems. Results indicate that PSO handles efficiently such problems, and in most cases it outperforms the branch and bound technique.