Showing papers on "Heuristic (computer science) published in 2019"
TL;DR: Simulation results show that the proposed design significantly improves the secrecy communication rate for the considered setup over the case without using the IRS, and outperforms a heuristic scheme.
Abstract: An intelligent reflecting surface (IRS) can adaptively adjust the phase shifts of its reflecting units to strengthen the desired signal and/or suppress the undesired signal. In this letter, we investigate an IRS-aided secure wireless communication system where a multi-antenna access point (AP) sends confidential messages to a single-antenna user in the presence of a single-antenna eavesdropper. In particular, we consider the challenging scenario where the eavesdropping channel is stronger than the legitimate communication channel and they are also highly correlated in space. We maximize the secrecy rate of the legitimate communication link by jointly designing the AP’s transmit beamforming and the IRS’s reflect beamforming. While the resultant optimization problem is difficult to solve, we propose an efficient algorithm to obtain high-quality suboptimal solution for it by applying the alternating optimization, and semidefinite relaxation methods. Simulation results show that the proposed design significantly improves the secrecy communication rate for the considered setup over the case without using the IRS, and outperforms a heuristic scheme.
410 citations
TL;DR: A novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems.
Abstract: In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential The proposed algorithm is simple and easy to implement ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems
359 citations
TL;DR: The main finding is that when the clusters overlap, k-means can be significantly improved using these two tricks, and if high clustering accuracy is needed, a better algorithm should be used instead.
260 citations
TL;DR: A novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), is proposed for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG.
Abstract: Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain–computer interface (BCI) application. The effectiveness of CSP is highly affected by the frequency band and time window of EEG segments. Although numerous algorithms have been designed to optimize the spectral bands of CSP, most of them selected the time window in a heuristic way. This is likely to result in a suboptimal feature extraction since the time period when the brain responses to the mental tasks occurs may not be accurately detected. In this paper, we propose a novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG. Specifically, spectrum-specific signals are first derived by bandpass filtering from raw EEG data at a set of overlapping filter bands. Each of the spectrum-specific signals is further segmented into multiple subseries using sliding window approach. We then devise a joint sparse optimization of filter bands and time windows with temporal smoothness constraint to extract robust CSP features under a multitask learning framework. A linear support vector machine classifier is trained on the optimized EEG features to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI Competition III dataset IIIa, BCI Competition IV datasets IIa, and BCI Competition IV dataset IIb) to validate the effectiveness of TSGSP in comparison to several other competing methods. Superior classification performance (averaged accuracies are 88.5%, 83.3%, and 84.3% for the three datasets, respectively) based on the experimental results confirms that the proposed algorithm is a promising candidate for performance improvement of MI-based BCIs.
233 citations
TL;DR: A novel artificial electric field algorithm (AEFA) which inspired by the Coulomb's law of electrostatic force is proposed, designed to work as a population based optimization algorithm, the concept of charge is extended to fitness value of the population in an innovative way.
Abstract: Electrostatic Force is one of the fundamental force of physical world. The concept of electric field and charged particles provide us a strong theory for the working force of attraction or repulsion between two charged particles. In the recent years many heuristic optimization algorithms are proposed based on natural phenomenon. The current article proposes a novel artificial electric field algorithm (AEFA) which inspired by the Coulomb's law of electrostatic force. The AEFA has been designed to work as a population based optimization algorithm, the concept of charge is extended to fitness value of the population in an innovative way. The proposed AEFA has been tested over a newly and challenging state-of-the-art optimization problems. The theoretical convergence of the proposed AEFA is also established along with statistical validation and comparison with recent state-of-the-art optimization algorithms. The presented study and findings suggests that the proposed AEFA as an outstanding optimization algorithms for non linear optimization.
197 citations
25 Jul 2019
TL;DR: The reward design of the method is well supported by the theory in MP, which can be proved to be maximizing the throughput of the traffic network, i.e., minimizing the overall network travel time and the concise state representation can fully support the optimization of the proposed reward function.
Abstract: Traffic signal control is essential for transportation efficiency in road networks. It has been a challenging problem because of the complexity in traffic dynamics. Conventional transportation research suffers from the incompetency to adapt to dynamic traffic situations. Recent studies propose to use reinforcement learning (RL) to search for more efficient traffic signal plans. However, most existing RL-based studies design the key elements - reward and state - in a heuristic way. This results in highly sensitive performances and a long learning process. To avoid the heuristic design of RL elements, we propose to connect RL with recent studies in transportation research. Our method is inspired by the state-of-the-art method max pressure (MP) in the transportation field. The reward design of our method is well supported by the theory in MP, which can be proved to be maximizing the throughput of the traffic network, i.e., minimizing the overall network travel time. We also show that our concise state representation can fully support the optimization of the proposed reward function. Through comprehensive experiments, we demonstrate that our method outperforms both conventional transportation approaches and existing learning-based methods.
194 citations
TL;DR: An adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran shows its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.
Abstract: This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map...
179 citations
TL;DR: A reliable self-adaptive routing algorithm (RSAR) based on this heuristic service algorithm is proposed and, by combining the reliability parameter and adjusting the heuristic function, RSAR achieves good performance with VANET.
Abstract: As a special MANET (mobile ad hoc network), VANET (vehicular ad-hoc network) has two important properties: the network topology changes frequently, and communication links are unreliable. Both properties are caused by vehicle mobility. To predict the reliability of links between vehicles effectively and design a reliable routing service protocol to meet various QoS application requirements, in this paper, details of the motion characteristics of vehicles and the reasons that cause links to go down are analyzed. Then a link duration model based on time duration is proposed. Link reliability is evaluated and used as a key parameter to design a new routing protocol. Quick changes in topology make it a huge challenge to find and maintain the end-to-end optimal path, but the heuristic Q-Learning algorithm can dynamically adjust the routing path through interaction with the surrounding environment. This paper proposes a reliable self-adaptive routing algorithm (RSAR) based on this heuristic service algorithm. By combining the reliability parameter and adjusting the heuristic function, RSAR achieves good performance with VANET. With the NS-2 simulator, RSAR performance is proved. The results show that RSAR is very useful for many VANET applications.
167 citations
TL;DR: An efficient DP-SH (dynamic programming with shooting heuristic as a subroutine) algorithm for the integrated optimization problem that can simultaneously optimize the trajectories of CAVs and intersection controllers is proposed and a two-step approach is developed to effectively obtain near-optimal intersection and trajectory control plans.
Abstract: Connected and automated vehicle (CAV) technologies offer promising solutions to challenges that face today’s transportation systems. Vehicular trajectory control and intersection controller optimization based on CAV technologies are two approaches that have significant potential to mitigate congestion, lessen the risk of crashes, reduce fuel consumption, and decrease emissions at intersections. These two approaches should be integrated into a single process such that both aspects can be optimized simultaneously to achieve maximum benefits. This paper proposes an efficient DP-SH (dynamic programming with shooting heuristic as a subroutine) algorithm for the integrated optimization problem that can simultaneously optimize the trajectories of CAVs and intersection controllers (i.e., signal timing and phasing of traffic signals), and develops a two-step approach (DP-SH and trajectory optimization) to effectively obtain near-optimal intersection and trajectory control plans. Also, the proposed DP-SH algorithm can also consider mixed traffic stream scenarios with different levels of CAV market penetration. Numerical experiments are conducted, and the results prove the efficiency and sound performance of the proposed optimization framework. The proposed DP-SH algorithm, compared to the adaptive signal control, can reduce the average travel time by up to 35.72% and save the consumption by up to 31.5%. In mixed traffic scenarios, system performance improves with increasing market penetration rates. Even with low levels of penetration, there are significant benefits in fuel consumption savings. The computational efficiency, as evidenced in the case studies, indicates the applicability of DP-SH for real-time implementation.
155 citations
10 May 2019
TL;DR: Neural-Guided RANSAC (NG-RANSAC) as discussed by the authors uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets.
Abstract: We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones.
151 citations
TL;DR: The experimental results demonstrate that ASO can outperform other well-known approaches such as Particle Swarm Optimization, Genetic Algorithm and Bacterial Foraging Optimization and thatASO is competitive to its competitors for parameter estimation problems.
TL;DR: This paper proposes a heuristic planning energy management controller, based on a Dyna agent of reinforcement learning approach, for real-time fuel saving optimization of a plug-in hybrid electric vehicle (PHEV).
Abstract: This paper proposes a heuristic planning energy management controller, based on a Dyna agent of reinforcement learning (RL) approach, for real-time fuel saving optimization of a plug-in hybrid electric vehicle (PHEV). The presented method is referred to as the Dyna- Η algorithm, which is a model-free online RL algorithm. First, as a case study, a detailed vehicle powertrain modeling of the Chevrolet Volt is built, where all the control components have been experimentally validated. Four traction operation modes are allowed by managing the states of two clutches and one brake. Furthermore, the Dyna- Η algorithm is introduced via incorporating a heuristic planning strategy into a Dyna agent. This is the first time to apply the Dyna- H algorithm in the energy management field of PHEVs. Finally, a comparative analysis of the one-step Q-learning, Dyna, and Dyna- Η algorithms is conducted in simulations. Numerous testing results indicate that the proposed algorithm leads to definite improvements in equivalent fuel economy and computational speed.
TL;DR: A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP, and the performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routed heuristic.
Abstract: This paper presents a mathematical formulation and efficient solution methodology for the hybrid vehicle-drone routing problem (HVDRP) for pick-up and delivery services. The problem is formulated as a mixed-integer program, which minimizes the vehicle and drone routing cost to serve all customers. The formulation captures the vehicle-drone routing interactions during the drone dispatching and collection processes and accounts for drone operation constraints related to flight range and load carrying capacity limitations. A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP. The performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routing heuristic. A set of experiments are conducted to evaluate the performance of the developed heuristics and to illustrate the capability of the developed model in answering a wide variety of questions related to the planning of the vehicle-drone delivery system.
TL;DR: In this article, the authors propose a resource allocation architecture which enables energy-aware service function chaining (SFC) for SDN-based networks, considering also constraints on delay, link utilization, server utilization.
Abstract: Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14% optimality-gap) while their execution time makes them usable for real-life networks.
TL;DR: A novel mixed integer linear programming (MILP) optimization model and a novel heuristic solution, Betweenness centrality Algorithm for Component Orchestration of NFV platform (BACON), for small- and large-scale DC networks are provided.
Abstract: Network function virtualization (NFV) has been introduced by network service providers to overcome various challenges that hinder them from satisfying the growing demand for networking services with higher return-on-investment. The association of NFV with the leading technologies of information technology virtualization and software defined networking is paving the way for flexible and dynamic orchestration of the VNFs, but still, various challenges need to be addressed. The VNFs instantiation and placement problems on data center’s (DC) servers are key enablers to achieve the desired flexible and dynamic NFV applications. In this paper, we have addressed the VNF placement problem by providing a novel mixed integer linear programming (MILP) optimization model and a novel heuristic solution, Betweenness centrality Algorithm for Component Orchestration of NFV platform (BACON), for small- and large-scale DC networks. The proposed solution addresses the VNF placement while taking into consideration the carrier-grade nature of the NFV applications and at the same time, minimizing the intra- and end-to-end delays of the service function chain (SFC). Also, the proposed approach enhances the reliability and the quality of service (QoS) of the SFC by maximizing the count of the functional group members. To evaluate the performance of the proposed solution, this paper conducts a comparative analysis with an NFV-agnostic algorithm and a greedy-k-NFV approach, which is proposed in the literature work. Also, this paper defines the complexity and the order of magnitude of the MILP model and BACON. BACON outperforms the greedy algorithms especially the greedy-k-NFV solution and has a lower complexity, which is calculated as $O((n^{3}-n^{2})/2)$ . The simulation results show that finding an optimized VNF placement can achieve minimal SFCs delays and enhance the QoS accordingly.
TL;DR: This work proposes a robust optimization framework to take into account these energy consumption uncertainties in the context of an electric vehicle routing problem and develops a two-phase heuristic method based on large neighbourhood search to solve larger instances of the problem.
Abstract: Compared with conventional freight vehicles, electric freight vehicles create less local pollution and are thus generally perceived as a more sustainable means of goods distribution. In urban areas, such vehicles must often perform the entirety of their delivery routes without recharging. However, their energy consumption is subject to a fair amount of uncertainty, which is due to exogenous factors such as the weather and road conditions, endogenous factors such as driver behaviour, and several energy consumption parameters that are difficult to measure precisely. Hence we propose a robust optimization framework to take into account these energy consumption uncertainties in the context of an electric vehicle routing problem. The objective is to determine minimum cost delivery routes capable of providing strong guarantees that a given vehicle will not run out of charge during its route. We formulate the problem as a robust mixed integer linear program and solve small instances to optimality using robust optimization techniques. Furthermore, we develop a two-phase heuristic method based on large neighbourhood search to solve larger instances of the problem, and we conduct several numerical tests to assess the quality of the methodology. The computational experiments illustrate the trade-off between cost and risk, and demonstrate the influence of several parameters on best found solutions. Furthermore, our heuristic identifies 42 new best solutions when tested on instances of the closely related robust capacitated vehicle routing problem.
TL;DR: A new model, a heuristic, and an exact labeling algorithm for the problem of finding the optimal charging decisions for a given route, and introduces a path-based model which outperforms the classical models in experiments.
TL;DR: In this paper, a wireless powered mobile edge computing (MEC) system with fluctuating channels and dynamic task arrivals over time is studied, and the authors jointly optimize the transmission energy allocation at the energy transmitter and the task allocation at user for local computing and offloading over a particular finite horizon, with the objective of minimizing the total transmission energy consumption at the ET while ensuring the user's successful task execution.
Abstract: This paper studies a wireless powered mobile edge computing (MEC) system with fluctuating channels and dynamic task arrivals over time. We jointly optimize the transmission energy allocation at the energy transmitter (ET) for WPT and the task allocation at the user for local computing and offloading over a particular finite horizon, with the objective of minimizing the total transmission energy consumption at the ET while ensuring the user's successful task execution. First, in order to characterize the fundamental performance limit, we consider the offline optimization by assuming that the perfect knowledge of channel state information and task state information (i.e., task arrival timing and amounts) is known a-priori. In this case, we obtain the well-structured optimal solution in a closed form to the energy minimization problem via convex optimization techniques. Next, inspired by the structured offline solutions obtained above, we develop heuristic online designs for the joint energy and task allocation when the knowledge of CSI/TSI is only causally known. Finally, numerical results are provided to show that the proposed joint designs achieve significantly smaller energy consumption than benchmark schemes with only local computing or full offloading at the user, and the proposed heuristic online designs perform close to the optimal offline solutions.
TL;DR: The proposed model is comprehensive, which aggregates the length, energy consumption, and collision risk into the objective function and incorporates the steering window constraint and develops a nature-inspired ant colony optimization algorithm to search the optimal path.
Abstract: Path planning is a critical issue to ensure the safety and reliability of the autonomous navigation system of the autonomous underwater vehicles (AUVs). Due to the nonlinearity and constraint issues, existing algorithms perform unsatisfactorily or even cannot find a feasible solution when facing large-scale problem spaces. This paper improves the path planning of AUVs in terms of both the path planning model and the optimization algorithm. The proposed model is comprehensive, which aggregates the length, energy consumption, and collision risk into the objective function and incorporates the steering window constraint. Based on the model, we develop a nature-inspired ant colony optimization algorithm to search the optimal path. Our algorithm is named alarm pheromone-assisted ant colony system (AP-ACS), since it incorporates the alarm pheromone in addition to the traditional guiding pheromone. The alarm pheromone alerts the ants to infeasible areas, which saves invalid search efforts and, thus, improves the search efficiency. Meanwhile, three heuristic measures are specifically designed to provide additional knowledge to the ants for path planning. In the experiments, different from the previous works that are tested on synthetic instances only, we implement an interface to retrieve the practical underwater environment data. AP-ACS and the compared algorithms are thus tested on several practical environments of different scales. The experimental results show that AP-ACS can effectively handle the constraints and outperforms the other algorithms in terms of accuracy, efficiency, and stability.
TL;DR: The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system and gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO.
Abstract: This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.
Posted Content•
TL;DR: This work uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets and combines neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible.
Abstract: We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones.
TL;DR: An extension of the VRPD that is called VRPDERO, where drones may not only be launched and retrieved at vertices but also on some discrete points that are located on each arc, is proposed and some valid inequalities that enhance the performance of the MILP solvers are introduced.
TL;DR: A new metaheuristic algorithm, inspired by the behavior of emperor penguins which is called Emperor Penguins Colony (EPC), is proposed, which is controlled by the body heat radiation of the penguins and their spiral-like movement in their colony.
Abstract: A metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Metaheuristic algorithms attempt to find the best solution out of all possible solutions of an optimization problem. A very active area of research is the design of nature-inspired metaheuristics. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing systems to deal with complex computational problems. In this paper, a new metaheuristic algorithm, inspired by the behavior of emperor penguins which is called Emperor Penguins Colony (EPC), is proposed. This algorithm is controlled by the body heat radiation of the penguins and their spiral-like movement in their colony. The proposed algorithm is compared with eight developed metaheuristic algorithms. Ten benchmark test functions are applied to all algorithms. The results of the experiments to find the optimal result, show that the proposed algorithm is better than other metaheuristic algorithms.
TL;DR: The presented mechanism was designed and developed as a core component of an autonomous robotic inspector deployed and validated in the tunnels of Egnatia Motorway in Metsovo, Greece, and suggest a promising potential as a driver of autonomous concrete-lining tunnel-inspection robots.
Abstract: In this paper, a crack detection mechanism for concrete tunnel surfaces is presented. The proposed methodology leverages deep Convolutional Neural Networks and domain-specific heuristic post-processing techniques to address a variety of challenges, including high accuracy requirements, low operational times and limited hardware resources, poor and variable lighting conditions, low textured lining surfaces, scarcity of training data, and abundance of noise. The proposed framework leverages the representational power of the convolutional layers of CNNs, which inherently selects effective features, thus obviating the need for the tedious task of handcrafted feature extraction. Additionally, the good performance rates attained by the proposed framework are acquired at a significantly lower execution time compared to other techniques. The presented mechanism was designed and developed as a core component of an autonomous robotic inspector deployed and validated in the tunnels of Egnatia Motorway in Metsovo, Greece. The obtained results denote the proposed approach’s superiority over a variety of methods and suggest a promising potential as a driver of autonomous concrete-lining tunnel-inspection robots.
TL;DR: In this paper, forecasting strategies are proposed for load related parameters and tested on real data, and an efficient method based on the heuristic procedure (tabu search) is presented for optimization of an IHPS based on solar and wind energy along with a battery.
TL;DR: A novel heuristic method for optimal allocation of distributed generation (DG) and capacitor banks and simulation results demonstrate that the presented heuristic approach is robust in finding the optimal results, very fast and easy to implement, applicable to large distribution systems.
TL;DR: This work proposes a hybrid heuristic that the initial solution is created from the optimal TSP solution reached by a TSP solver, and provides a new set of instances based on well-known TSPLIB instances.
Posted Content•
TL;DR: This work designs near-term quantum algorithms for linear systems of equations based on the classical combination of variational quantum states (CQS), and exhibits several provable guarantees for these algorithms, supported by the representation of the linear system on a so-called ansatz tree.
Abstract: Solving linear systems of equations is essential for many problems in science and technology, including problems in machine learning. Existing quantum algorithms have demonstrated the potential for large speedups, but the required quantum resources are not immediately available on near-term quantum devices. In this work, we study near-term quantum algorithms for linear systems of equations of the form $Ax = b$. We investigate the use of variational algorithms and analyze their optimization landscapes. There exist types of linear systems for which variational algorithms designed to avoid barren plateaus, such as properly-initialized imaginary time evolution and adiabatic-inspired optimization, suffer from a different plateau problem. To circumvent this issue, we design near-term algorithms based on a core idea: the classical combination of variational quantum states (CQS). We exhibit several provable guarantees for these algorithms, supported by the representation of the linear system on a so-called Ansatz tree. The CQS approach and the Ansatz tree also admit the systematic application of heuristic approaches, including a gradient-based search. We have conducted numerical experiments solving linear systems as large as $2^{300} \times 2^{300}$ by considering cases where we can simulate the quantum algorithm efficiently on a classical computer. These experiments demonstrate the algorithms' ability to scale to system sizes within reach in near-term quantum devices of about $100$-$300$ qubits.
TL;DR: A planning algorithm is proposed that jointly specifies the optimal grid topology, namely AC, DC, or hybrid AC/DC, along with the optimal locations and sizes of distributed energy resources, energy storage systems, and AC–DC converters.
Abstract: This paper presents an efficient planning algorithm for microgrids in remote isolated communities. Different from the existing research that assumes a specific microgrid topology, we propose a planning algorithm that jointly specifies the optimal grid topology, namely AC, DC, or hybrid AC/DC, along with the optimal locations and sizes of distributed energy resources, energy storage systems, and AC–DC converters. The planning objective is to ensure reliable power flow with minimum deployment and operational costs. The planning problem is formulated as a mixed integer nonlinear program, and given the complexity of the problem, the proposed algorithm implements a two-stage framework that results in an efficient planning solution. The first stage deals with the specification of the microgrid topology, and allocation and sizing of all the equipment following a heuristic optimization approach. Upon deciding the microgrid topology and equipment installation in the first stage, the second stage of the planning algorithm ensures smooth and reliable operation for the proposed topology over all possible operation scenarios. This is achieved with minimal operational costs by considering the optimal nonlinear scheduling problem for the installed equipment. Test cases are presented to investigate the performance of the proposed planning algorithm at different fuel transportation cost scenarios.
TL;DR: An overview of the heuristic optimization algorithm dragonfly and its variants is presented and its convergence rate is better than the other algorithms in the literature, such as PSO and GA.
Abstract: One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm’s weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.