In this paper, a general-purpose double auction mechanism that is specifically relevant for energy transactions among competing buyers and sellers, i.e., agents, in a microgrid has been investigated. This auction algorithm is implemented by means of a microgrid controller (MC), and is individually rational, weakly budget balanced, and efficient (social welfare maximizing). Furthermore, the mechanism does not violate any physical grid constraints and does not require agents' private information. These characteristics of the auction are accomplished by a suitable surrogate objective function which implicitly maximizes the social welfare while the agents place bids in a selfish manner and can sell or procure energy. The sellers of energy are modeled as prosumers that are equipped with photovoltaic panels as well as advanced smart meters and are willing to participate in selling their excess energy due to saturation in their energy consumption utility curves. Buyers of energy are assumed to be conventional consumers with smart meter capabilities to communicate with the MC and place bids to get optimal allocations. Buyers aim to maximize their energy usage utility plus their payment amount whereas sellers maximize their own energy consumption utility plus the reimbursement that they receive for selling their excess generation. Furthermore, the mechanism allows the possibility of sellers' coalition where a fair redistribution of the energy supply is possible in a competitive market with less demand and more supply.

A central concern for an optimizing compiler is the design of its intermediate representation (IR) for code. The IR should make it easy to perform transformations, and should also afford efficient and precise static analysis. In this paper we study an aspect of IR design that has received little attention: the role of undefined behavior. The IR for every optimizing compiler we have looked at, including GCC, LLVM, Intel's, and Microsoft's, supports one or more forms of undefined behavior (UB), not only to reflect the semantics of UB-heavy programming languages such as C and C++, but also to model inherently unsafe low-level operations such as memory stores and to avoid over-constraining IR semantics to the point that desirable transformations become illegal. The current semantics of LLVM's IR fails to justify some cases of loop unswitching, global value numbering, and other important "textbook" optimizations, causing long-standing bugs. We present solutions to the problems we have identified in LLVM's IR and show that most optimizations currently in LLVM remain sound, and that some desirable new transformations become permissible. Our solutions do not degrade compile time or performance of generated code.

Several applications within a shipboard system involve the integration of physical systems (e.g., power systems) and cyber computing platforms such as command and control, communications, and sensing networks, and require real-time quality of service (QoS) guarantees. In this paper, the distribution of total (end-to-end) delay associated with fault diagnosis and reconfiguration of shipboard power system (SPS) is investigated from a cyber-physical systems (CPS) perspective. Specifically, a cross-layer end-to-end delay analysis framework is introduced for SPS reconfiguration. The proposed framework stochastically models the heterogeneity of actions of various subsystems involved in the reconfiguration tasks viz., generation of fault information by sensor nodes associated to the power system, processing of actions at control center to resolve fault locations and reconfiguration, and flow of information through communication network to perform necessary actions. The proposed framework then combines appropriately the output delay distributions from each subsystem to: 1) analytically predict the distribution of end-to-end delay in SPS reconfiguration after the occurrence of faults and 2) analyze and design real-time reconfiguration solutions for shipboard CPS, that meet total delay requirements. Simulations using various topological scenarios demonstrate that the proposed analytical framework closely predict the total delay associated with SPS reconfiguration.

In this paper, a hierarchical Pareto dominance based multi-objective evolutionary approach is proposed for the optimization of gene regulatory network models. The approach is presented based on the neglected observations in GRN optimization that (i) structural dependencies exist among objectives; and (ii) some objectives may be more important than others. The hierarchical Pareto dominance is able to reduce the number of objectives during optimization process and increase the selection pressure to relieve the many objective problem. The proposed hierarchical Pareto dominance based multi-objective approach is verified and compared with classical Pareto dominance based algorithm NSGAII on the gene regulatory network optimization problem. The results obtained indicate that the presented approach has great performance when no noise exist. Also it shows superior results compared to NSGAII.

In this paper, we describe a multi-objective approach to determine an effective mitigation strategy during an outbreak of foot and mouth disease in cattle. The goal of the approach is to minimize the total number of infections. In addition, the direct costs must also be minimized as a secondary objective. The animals culled and vaccinations may also be minimized, as long as the number of infections is not compromised. Thus a three-tier hierarchy of objectives exists. A framework that considers objective hierarchies during a multi-objective optimization algorithm is suggested. Results show that this produces better results than a conventional multi-objective optimization approach.

This paper proposes a new approach for biologically inspired computing on the basis of Gene Regulatory Networks. These networks are models of genes and dynamic interactions that take place between them. The differential equation representations of such networks resemble neural networks as well as idiotypic networks in immune system. Although several potential applications have been outlined, an example, the problem of placing sensors optimally in a distributed environment is considered in detail. A comparison with NSGA-II suggest that the new method is able to accomplish near-optimal coverage of sensors in a network.

This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder-Mead simplex-based local search. The proposed GA is shown to perform better than NSGA-II and SPEA-2 on standard benchmarks, as well as for the optimization of a genetic model for flowering time control in rice. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks. The hybrid version also compares well with ParEGO on a few other benchmarks. The proposed hybrid algorithm is then applied to estimate the parameters of an elaborate gene network model of flowering time control in Arabidopsis. Overall solution quality is quite good by biological standards. Tradeoffs are discussed between accuracy in gene activity levels versus in the plant traits that they influence. These tradeoffs suggest that data mining the Pareto front may be useful in bioinformatics.

In this paper, we describe a Genetic Programming and Particle Swarm Hybrid algorithm for Gene Network discovery.

The artificial immune system approach for self-nonself discrimination and its application to anomaly detection problems in engineering is showing great promise. A seminal contribution in this area is the V-detectors algorithm that can very effectively cover the nonself region of the feature space with a set of detectors. The detector set can be used to detect anomalous inputs. In this paper, a multistage approach to create an effective set of V-detectors is considered. The first stage of the algorithm generates an initial set of V-detectors. In subsequent stage, new detectors are grown from existing ones, by means of a mechanism called procreation. Procreating detectors can more effectively fill hard-to-reach interstices in the nonself region, resulting in better coverage. The effectiveness of the algorithm is first illustrated by applying it to a well-known fractal, the Koch curve. The algorithm is then applied to the problem of detecting anomalous behavior in power distribution systems, and can be of much use for maintenance-related decision-making in electrical utility companies.

This paper describes a PSO-Nelder Mead Simplex hybrid multi-objective optimization algorithm based on a numerical metric called µ -fuzzy dominance. Within each iteration of this approach, in addition to the position and velocity update of each particle using PSO, the k-means algorithm is applied to divide the population into smaller sized clusters. The Nelder-Mead simplex algorithm is used separately within each cluster for added local search. The proposed algorithm is shown to perform better than MOPSO on several test problems as well as for the optimization of a genetic model for flowering time control in Arabidopsis. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks.

Point estimates of the parameters in real world models convey valuable information about the actual system. However, parameter comparisons and/or statistical inference requires determination of parameter space confidence regions in addition to point estimates. In most practical applications, the relation of the parameters to model fitness is highly nonlinear and noisy data leads to further deviations. Thus the confidence regions obtained by using locally linearized models are often misleading. Uniform covering by probabilistic rejection (UCPR) is a robust technique that has been developed to solve this problem, and has been proven to be more efficient than other approximate random search techniques. In this paper, we propose a contour particle swarm optimization (C-PSO) technique and compare its performance against UCPR in predicting the confidence regions. Results indicate that for problems with low number of parameters, both the algorithms are quite comparable. However, real world models such as genetic networks have a large number of parameters and the UCPR fails in finding good convergence due to its limited search capabilities. In such problems, the C-PSO technique was able to find the confidence regions with better resolution and efficiency.

Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines and models are incapable of handling the problems. This study applies Fuzzy dominance based Simplex Genetic Algorithm (a multi-objective evolutionary optimization algorithm) in generating decision rules for predicting loan default in a typical credit institution.

Particle swarm optimization is a stochastic algorithm for optimizing continuous functions. It uses a population of particles that follow trajectories through the search space towards good optima. This paper proposes adding a local search component to PSO to improve its convergence speed. Two possible methods are discussed. The first adds a term containing estimated gradient information to the velocity of each particle. The second explicitly incorporates the Nelder-Mead algorithm, a known local search technique, within PSO. The suggested methods have been applied to the problem of estimating parameters of a gene network model. Results indicate the effectiveness of the proposed strategies.

This paper outlines a radial basis function neural network approach to predict the failures in overhead distribution lines of power delivery systems. The RBF networks are trained using historical data. The network sizes and errors are simultaneously minimized using the Pareto Archive Evolutionary Strategy algorithm. Mutation of the network is carried out by invoking an orthogonal least square procedure. The performance of the proposed method was compared to a fuzzy inference approach and with multilayered perceptrons. The results suggest that this approach outperforms the other techniques for the prediction of failure rates.

This paper explores the application of the ant colony algorithm for an NP-complete problem in the area of wireless communications. The specific problem is one of detecting users (binary vectors) in a multi-user environment in synchronous MC-CDMA (multi-carrier Code Division Multiple Access) systems, such that the total interference noise in minimized. This approach is particularly attractive as ACO is well suited for physically realizable, real-time use, where fast convergence is absolutely necessary. Results suggest that the algorithm reduces the computer time requirement by as much as 98% over an exhaustive search method.

In this paper, the parameters of a genetic network for rice flowering time control have been estimated using a multi-objective genetic algorithm approach. We have modified the recently introduced concept of fuzzy dominance to hybridize the well-known Nelder Mead Simplex algorithm for better exploitation with a multi-objective genetic algorithm. A co-evolutionary approach is proposed to adapt the fuzzy dominance parameters. Additional changes to the previous approach have also been incorporated here for faster convergence, including elitism. Our results suggest that this hybrid algorithm performs significantly better than NSGA-II, a standard algorithm for multi-objective optimization.

This paper proposes a new algorithm based on the clonal selection principle for the design of spreading codes for DS-CDMA. The algorithm follows a multi-objective approach, generating complex spreading codes with good autocorrelation as well as good cross-correlation properties. It also enables spreading code design with no restrictions on the number of users or code length. The algorithm maintains a repertoire of codes that are subject to cloning and undergo a process of affinity maturation to obtain better codes. Results indicate that the produces code sets that lie very close to the theoretical Pareto front.

In this paper, we present a novel multiuser detection (MUD) technique based on ant colony optimization (ACO), for synchronous multi-carrier code division multiple access (MC-CDMA) systems. ACO algorithms are based on the cooperative foraging strategy of real ants. While an optimal MUD design using an exhaustive search method is prohibitively complex, we show that the ACO based MUD converges to the optimal MUD BER performance in relatively few iterations providing 98% saving in computational complexity.

This paper proposes a novel evolutionary approach to spreading code design in direct sequence code division multiple access (DS-CDMA). Specifically, a multiobjective evolutionary algorithm (EA) is used to generate complex spreading sequences that are optimized with respect to the average mean-square cross- and/or autocorrelation (CC and/or AC) properties. A theoretical model is developed in order to demonstrate the optimality of the generated codes. The proposed algorithm enables spreading code design with no constraints on the code length. Furthermore, it is possible to generate K/spl ges/N codes of length N with very little cost in correlation properties. This results in significant capacity enhancement in DS-CDMA systems.

An ant colony algorithm is proposed to determine the optimal locations and ratings of capacitors in a distribution network for reactive power compensation. The approach is multilevel. Two separate tables of pheromones are maintained by the algorithm. Ants generate solution stochastically, based on these pheromone tables. The pheromone tables are updated periodically, so that pheromones accrue more along better solutions. Results obtained by the proposed algorithm have been compared with earlier schemes. We conclude that the proposed approach is an effective approach for optimally placing capacitors in a distribution system.

Genetic algorithms are exploratory search techniques that rely on a large population of individuals. In order to improve the search process, several hybrid approaches have been proposed that make use of a local exploitative search technique, such as the Nelder-Mead simplex algorithm. The simplex algorithm has been modified for multi-objective optimization, by introducing the concept of fuzzy dominance. A hybrid algorithm, the fuzzy dominance based simplex - genetic algorithm (FSGA) has been proposed. This algorithm was shown to be a very effective search strategy when applied to a multi-objective problem in modelling the gene regulatory network of flowering time control of Oryza sativa.

The paper proposes a novel evolutionary approach to spreading code design in DS-CDMA. Specifically, a multiobjective evolutionary algorithm is used to generate complex spreading sequences that are optimized with respect to average mean square cross correlation and/or autocorrelation properties. A theoretical model is developed in order to demonstrate the optimality of the generated codes. The proposed algorithm enables spreading code design with no constraints on the code length. Furthermore, it is possible to generate K/spl ges/N codes of length N with very little cost in correlation properties. This results in significant capacity enhancement in DS-CDMA systems.

With the advances in processor and memory technology, sensor nodes are likely to be widely deployed leading to creation of sensor networks involving a large number of sensors for monitoring and tracking data. In this paper, we study the problem of rate-based data collection and routing in the context of sensor networks. We consider networks wherein data sources are sampling/collecting data and making it available to potential consumers. Consumers may require data at rates that may vary for each consumer. The problem studied is that of constructing a data distribution tree to efficiently disseminate data at the required rate to each consumer. We first propose an algorithm based on breadth-first tree construction which operates in two phases. The first phase constructs a breadth first tree (by ignoring the rates) and the second phase assigns rates to the tree edges. Subsequently, we present a more efficient algorithm that constructs the tree in a single phase taking rates into account. We also study a variation of the problem taking into consideration weights of the edges. The algorithms have been evaluated using discrete event simulation and we present experimental results comparing the performance of the algorithms.

The ant systems optimization approach is a new method of solving combinatorial optimization problems. It was originally introduced as a metaheuristic approach for the well-known traveling salesman problem. But it was subsequently shown to be an equally effective algorithm for solving other optimization problems. This article supplies more results for an extended ant colony algorithm, which can be used to compute a minimum cost Steiner tree from a graph. In each pass of the proposed algorithm, ants are placed at the terminal nodes of the Steiner tree to be computed. They are then allowed to move towards one another, along the edges of the graph, until they merge into a single entity. In this process, the paths taken by the ants define a Steiner tree. Edges receive reinforcement in the form of pheromone deposits along the paths taken by the ant, that is based on the quality of the Steiner tree produced. Pheromones eventually accrue most along better edges. As a result, after several iterations, a good Steiner tree can be extracted from the deposit. The algorithm can easily be used in several practical applications.

The ant colony algorithm is a new technique for combinatorial optimization borrowed from swarm intelligence. This paper outlines an ant colony algorithm to compute the optimal order of restoring sections in a power distribution system. Restoration of distribution feeders after long interruptions creates cold load pickup conditions due to loss of diversity among the loads. The distribution system load may have to be restored step-by-step using sectionalizing switches under such conditions to prevent overheating of substation transformer. The restoration time is dependent on the order in which sections are restored. Results obtained using this method for two test cases are presented including a comparison with the simulated annealing algorithm.

Restoration of distribution feeders after long interruptions creates cold load pickup conditions due to loss of diversity among the loads. The distribution system load may have to be restored step-by-step using sectionalizing switches under such conditions to prevent overheating of the substation transformer. The restoration time is dependent on the order in which sections are restored. A method based on a genetic algorithm to find the globally best restoration sequence of sections is presented in this paper. Results obtained using this method for a test case are presented.

Prokaryotes mainly reproduce by binary fission where the offspring are genetically identical to their parents. In order to take advantage of the benefits of sexual reproduction, these organisms have evolved ingenious ways to exchange genetic information. This article proposes an evolutionary algorithm called SPILL (Simulated Prokaryote Interchange of aLLeles) that is based on prokaryote genetic exchange patterns.