An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. In this paper, we propose a new formulation of the joint scheduling of EV and Unit Commitment (UC), called EVUC. Our formulation considers the characteristics of EVs while optimizing the system total running cost. We employ Chemical Reaction Optimization (CRO), a general-purpose optimization algorithm to solve this problem and the simulation results on a widely used set of instances indicate that CRO can effectively optimize this problem.
CRO is a recently proposed metaheuristic, which has been developed intensely in the past few years. CRO was originally designed to solve combinatorial optimization problems in @cite_21 , where CRO is adopted to solve the Quadratic Assignment Problem, the Resource-Constraint Project Scheduling Problem, and the Channel Assignment Problem. The Cognitive Radio Spectrum Allocation Problem is addressed in @cite_22 . Yu proposed and solved a Sensor Deployment Problem with CRO in @cite_10 . Lam analyzed the convergence of CRO for combinatorial optimization in @cite_1 . Lam also proposed Real-Coded CRO, a variant of CRO, to solve continuous optimization problems in @cite_11 . Yu solved an Artificial Neural Network training problem in @cite_17 , and proposed several perturbation functions for RCCRO in @cite_2 .
@cite_22 @cite_21 @cite_1 @cite_17 @cite_2 @cite_10 @cite_11
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Cognitive radio can help increase the capacity of wireless networks by allowing unlicensed users to use the licensed bands, provided that the occupancy do not affect the prioritized licensed users. One of the fundamental problems in cognitive radio is how to allocate the available channels to the unlicensed users in order to maximize the utility. In this work, we develop an allocation algorithm based on the newly proposed chemical reaction-inspired metaheuristic called Chemical Reaction Optimization (CRO). We study three utility functions for utilization and fairness, with the consideration of the hardware constraint. No matter which utility function is used, simulation results show that the CRO-based algorithm always outperforms the others dramatically. Cognitive radio (CR) is a revolution in radio technology and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to design distributed algorithm for a future multi-hop CR network, with the objective of maximizing data rates for a set of user communication sessions. We study this problem via a cross-layer optimization approach, with joint consideration of power control, scheduling, and routing. The main contribution of this paper is the development of a distributed optimization algorithm that iteratively increases data rates for user communication sessions. During each iteration, there are two separate processes, a Conservative Iterative Process (CIP) and an Aggressive Iterative Process (AIP). For both CIP and AIP, we describe our design of routing, minimalist scheduling, and power control scheduling modules. To evaluate the performance of the distributed optimization algorithm, we compare it to an upper bound of the objective function, since the exact optimal solution to the objective function cannot be obtained via its mixed integer nonlinear programming (MINLP) formulation. Since the achievable performance via our distributed algorithm is close to the upper bound and the optimal solution (unknown) lies between the upper bound and the feasible solution obtained by our distributed algorithm, we conclude that the results obtained by our distributed algorithm are very close to the optimal solution. Cognitive radio (CR) users are expected to be uncoordinated users that opportunistically seek the spectrum resource from primary users (PUs) in a competitive way. In most existing works, however, CR users are required to share the interference channel information and power strategies to conduct the game with pricing mechanisms that incur the frequent exchange of information. The requirement of significant communication overheads among CR users impedes fully distributed solutions for the deployment of CR networks, which is a challenging problem in the research communities. In this paper, a robust distributed power control algorithm is designed with low implementation complexity for CR networks through reinforcement learning, which does not require the interference channel and power strategy information among CR users (and from CR users to PUs). To the best of our knowledge, this research provides the solution for the first time for the incomplete-information power control game in CR networks. During the repeated game, CR users can control their power strategies by observing the interference from the feedback signals of PUs and transmission rates obtained in the previous step. This procedure allows achieving high spectrum efficiency while conforming to the interference constraint of PUs. This constrained repeated stochastic game with learning automaton is proved to be asymptotically equivalence to the traditional game with complete information. The properties of existence, diagonal concavity and uniqueness for the game are studied. A Bush-Mosteller reinforcement learning procedure is designed for the power control algorithm, and the properties of convergence and learning rate of the algorithm are analyzed. The performance of the learning-based power control algorithm is thoroughly investigated with simulation results, which demonstrates the effectiveness of the proposed algorithm in solving variety of practical CR network problems for real-world applications. A novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-world problems. However, all studies concerning CRO have been empirical in nature and no theoretical analysis has been conducted to study its convergence properties. In this paper, we present some convergence results for several generic versions of CRO, each of which adopts different combinations of elementary reactions. We investigate the limiting behavior of CRO. By modeling CRO as a finite absorbing Markov chain, we show that CRO converges to a global optimum solution with a probability arbitrarily close to one when time tends to infinity. Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO. Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain. Wireless cognitive radio (CR) is a newly emerging paradigm that attempts to opportunistically transmit in licensed frequencies, without affecting the pre-assigned users of these bands. To enable this functionality, such a radio must predict its operational parameters, such as transmit power and spectrum. These tasks, collectively called spectrum management, is difficult to achieve in a dynamic distributed environment, in which CR users may only take local decisions, and react to the environmental changes. In this paper, we introduce a multi-agent reinforcement learning approach based spectrum management. Our approach uses value functions to evaluate the desirability of choosing different transmission parameters, and enables efficient assignment of spectrums and transmit powers by maximizing long-term reward. We then investigate various real-world scenarios, and compare the communication performance using different sets of learning parameters. We also apply Kanerva-based function approximation to improve our approach's ability to handle large cognitive radio networks and evaluate its effect on communication performance. We conclude that our reinforcement learning based spectrum management can significantly reduce the interference to the licensed users, while maintaining a high probability of successful transmissions in a cognitive radio ad hoc network. Cognitive radios (CRs) have a great potential to improve spectrum utilization by enabling users to access the spectrum dynamically without disturbing licensed primary radios (PRs). A key challenge in operating these radios as a network is how to implement an efficient medium access control (MAC) mechanism that can adaptively and efficiently allocate transmission powers and spectrum among CRs according to the surrounding environment. Most existing works address this issue via suboptimal heuristic approaches or centralized solutions. In this paper, we propose a novel joint power channel allocation scheme that improves the performance through a distributed pricing approach. In this scheme, the spectrum allocation problem is modeled as a noncooperative game, with each CR pair acting as a player. A price-based iterative water-filling (PIWF) algorithm is proposed, which enables CR users to reach a good Nash equilibrium (NE). This PIWF algorithm can be implemented distributively with CRs repeatedly negotiating their best transmission powers and spectrum. Simulation results show that the social optimality of the NE solution is dramatically improved through pricing. Depending on the different orders according to which CRs take actions, we study sequential and parallel versions of the PIWF algorithm. We show that the parallel version converges faster than the sequential version. We then propose a corresponding MAC protocol to implement our resource management schemes. The proposed MAC allows multiple CR pairs to be first involved in an admission phase, then iteratively negotiate their transmission powers and spectrum via control-packet exchanges. Following the negotiation phase, CRs proceed concurrently with their data transmissions. Simulations are used to study the performance of our protocol and demonstrate its effectiveness in terms of improving the overall network throughput and reducing the average power consumption. Cognitive radio (CR) is a promising technology for increasing the spectrum capacity for ad hoc networks. Based on CR, the unlicensed users will utilize the unused spectrum of the licensed users in an opportunistic manner. Therefore, the average spectrum usage will be increased. However, the sudden appearance of the licensed users forces the unlicensed user to vacate its operating channel and handoff to another free one. Spectrum handoff is one of the main challenges in cognitive ad hoc networks. In this paper, we aim to reduce the effect of consecutive spectrum handoff for cognitive ad hoc users. To achieve that, the licensed channels will be used as operating channels and the unlicensed channels will be used as backup channels when the primary user appears. Therefore, the number of spectrum handoff will be reduced, since unlicensed bands are primary user free bands. A Markov chain model is presented to evaluate the proposed scheme. Performance metrics such as blocking probability and dropping probabilities are obtained. The results show that the proposed scheme reduces all the aforementioned performance metrics. Cognitive radio (CR) is the key enabling technology for an efficient dynamic spectrum access. It aims at exploiting an underutilized licensed spectrum by enabling opportunistic communications for unlicensed users. In this work, we first develop a distributed cognitive radio MAC (COMAC) protocol that enables unlicensed users to dynamically utilize the spectrum while limiting the interference on primary (PR) users. The main novelty in COMAC lies in not assuming a predefined CR-to-PR power mask and not requiring active coordination with PR users. COMAC provides a statistical performance guarantee for PR users by limiting the fraction of the time during which the PR users' reception is negatively affected by CR transmissions. To provide such a guarantee, we develop probabilistic models for the PR-to-PR and the PR-to-CR interference under a Rayleigh fading channel model. From these models, we derive closed-form expressions for the mean and variance of interference. Empirical results show that the distribution of the interference is approximately lognormal. Based on the developed interference models, we derive a closed-form expression for the maximum allowable power for a CR transmission. We extend the min-hop routing to exploit the available channel information for improving the perceived throughput. Our simulation results indicate that COMAC satisfies its target soft guarantees under different traffic loads and arbitrary user deployment scenarios. Results also show that exploiting the available channel information for the routing decisions can improve the end-to-end throughput of the CR network (CRN). Grid computing solves high performance and high-throughput computing problems through sharing resources ranging from personal computers to supercomputers distributed around the world. One of the major problems is task scheduling, i.e., allocating tasks to resources. In addition to Makespan and Flowtime, we also take reliability of resources into account, and task scheduling is formulated as an optimization problem with three objectives. This is an NP-hard problem, and thus, metaheuristic approaches are employed to find the optimal solutions. In this paper, several versions of the Chemical Reaction Optimization (CRO) algorithm are proposed for the grid scheduling problem. CRO is a population-based metaheuristic inspired by the interactions between molecules in a chemical reaction. We compare these CRO methods with four other acknowledged metaheuristics on a wide range of instances. Simulation results show that the CRO methods generally perform better than existing methods and performance improvement is especially significant in large-scale applications. In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches. Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of wellknown benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of optimization problems. Recently, the CR (cognitive radio) technology is gathering more and more attention because it has the capacity to deal with the scarce of the precious spectrum resource. Within the domain of CR technology, channel management of CR is of utmost importance due to its key role in the performance enhancement of the transmission and the minimum interference to the primary users as well. An 802.11 WLAN based ad-hoc protocol using the cognitive radio has been proposed in this paper. It provides the detection and protection for incumbent systems around the communication pair by separating the spectrum into the data channels and common control channel. By adding the available channel list into the RTS and CTS, the communication pair can know which data sub channels are available (i.e., no incumbent signal). We proposed an ENNI (exchanging of neighbor nodes information) mechanism to deal with the hidden incumbent device problem. The simulation results show that by using our protocol the hidden incumbent device problem (HIDP) can be solved successfully. In this paper, we investigate a joint scheduling and power control for an infrastructure-based cognitive radio network (CRN) in coexistence with a cellular primary radio network (PRN). The PRN uses a set of licensed nonoverlapping orthogonal frequency channels for transmission. This set of channels is also accessed in an opportunistic manner by a set of cognitive radio base stations (CR-BSs) to support secondary users (SUs). The problem is formulated to maximize the spectrum utilization of SUs without causing excessive interference to active primary users (PUs) of the PRN. In addition, all the serviced SUs must meet a certain Quality of Service (QoS), such as satisfying a predefined signal to interference noise ratio (SINR). A centralized solution for joint scheduling and power control is derived to make the global accessing decision for all unserved SUs. With the assumption that the knowledge of all subscribers is available, a coordinator of the CRN can use the joint scheduling and power control algorithm to maximize the spectrum utilization of serviced SUs by solving a mixed-integer linear programming (MILP) with an NP-hard complexity. To avoid the NP-hard complexity, we propose a suboptimal heuristic greedy algorithm that can be obtained at a much lower complexity based on the coloring interference graph among unserved SUs effected by serviced SUs and active PUs. Its superior performance over the existing algorithms is demonstrated through simulations. In cognitive radio (CR) networks, the radio access from opportunistic users depends on their capabilities to detect spectrum holes. Typically, the detection problem has been considered separately from the optimization of the transmission strategy. However, in a CR context, the detection phase has an impact on network efficiency as well as on the undesired interference generated towards licensed users. In this work, we consider the joint optimization of detection thresholds and power allocation across multichannel links, in order to maximize the aggregated opportunistic throughput, under a constraint on the interference generated towards the primary users. We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques. In this paper we introduce the Cognitive Compressive Sensing (CCS) problem, modeling a Cognitive Receiver (CR) that optimizes the @math projections of a @math dimensional vector dynamically, by optimizing the objective of correctly detecting the maximum number of idle entries, while updating each time its Bayesian beliefs on the future vector realizations. We formulate and study the CCS as a Restless Multi-Armed Bandit problem, generalizing the popular Cognitive Spectrum Sensing model, in which the CR can sense @math out of the @math sub-channels and propose a novel adaptive Finite Rate of Innovation (FRI) sampling method based on the CCS approach. While in general the optimum policy remains elusive, we provide sufficient conditions in which in the limit for large @math and @math the greedy policy is optimum. Numerical results corroborate our theoretical findings. Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the next-generation multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signal-to-interference-and-noise-ratio) model, which is considered to be a better characterization of interference (but also more difficult to analyze) than disk graph model. The main difficulties of this problem are two-fold. First, SINR is a nonconvex function of transmission powers; an optimization problem in the SINR model is usually a nonconvex program and NP-hard in general. Second, in the SINR model, scheduling feasibility and the maximum allowed flow rate on each link are determined by SINR at the physical layer. To maximize capacity, it is essential to follow a cross-layer approach, but joint optimization at physical (power control), link (scheduling), and network (flow routing) layers with the SINR function is inherently difficult. In this paper, we give a mathematical characterization of the joint relationship among these layers. We devise a solution procedure that provides a (1- ) optimal solution to this complex problem, where is the required accuracy. Our theoretical result offers a performance benchmark for any other algorithms developed for practical implementation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the interaction of power control, scheduling, and flow routing in a CRN. This paper presents an implementation of the Chemical Reaction Optimization (CRO) algorithm to solve the optimal power flow (OPF) problem in power systems with the objective of minimizing generation costs. Multiple constraints, such as the balance of the power, bus voltage magnitude limits, transmission line flow limits, transformer tap settings, etc., are considered. We adapt the CRO framework to the OPF problem by redesigning the elementary reaction operators. We perform simulations on the standard IEEE-14, -30, and -57 bus benchmark systems. We compare the perform of CRO with other reported evolutionary algorithms in the IEEE-30 test case. Simulation results show that CRO can obtain a solution with the lowest cost, when compared with other algorithms. To be more complete, we also give the average result for the IEEE-30 case, and the best and average results for the IEEE-14 and -57 test cases. The results given in this paper suggest that CRO is a better alternative for solving the OPF problem, as well as its variants for the future smart grid.