2,028 research outputs found
Optimal Wideband LPDA Design for Efficient Multimedia Content Delivery over Emerging Mobile Computing Systems
An optimal synthesis of a wideband Log-Periodic
Dipole Array (LPDA) is introduced in the present study. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio and the side lobe level, over a
wide frequency range. The LPDA geometry that complies with the above requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called Invasive Weed Optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO method has been chosen to optimize an LPDA for operation in the frequency range
800-3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems
Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Price
The increasing number of electric vehicles (EVs) has led to the need for
installing public electric vehicle charging stations (EVCS) to facilitate ease
of use and to support users who do not have the option of residential charging.
The public electric vehicle charging infrastructures (EVCIs) must be equipped
with a good number of EVCSs, with fast charging capability, to accommodate the
EV traffic demand, which would otherwise lead to congestion at the charging
stations. The location of these fast-charging infrastructures significantly
impacts the distribution system (DS). We propose the optimal placement of
fast-charging EVCIs at different locations in the distribution system, using
multi-objective particle swarm optimization (MOPSO), so that the power loss and
voltage deviations are kept at a minimum. Time-series analysis of the DS and EV
load variations are performed using MATLAB and OpenDSS. We further analyze the
cost benefits of the EVCIs under real-time pricing conditions and employ an
autoregressive integrated moving average (ARIMA) model to predict the dynamic
price. The simulated test system without any EVCI has a power loss of 164.36 kW
and squared voltage deviations of 0.0235 p.u. Using the proposed method, the
results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs
with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and
squared voltage deviations of 0.0182 p.u. in the system. Significant cost
benefits for the EVCIs are also achieved, and an R-squared value of dynamic
price predictions of 0.9999 is obtained. This would allow the charging station
operator to make promotional offers for maximizing utilization and increasing
profits
An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation
Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS),using the t -way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance
A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results
A new two-stage algorithm for solving optimization problems
Optimization seeks to find inputs for an objective function that result in a maximum or minimum. Optimization methods are divided into exact and approximate (algorithms). Several optimization algorithms imitate natural phenomena, laws of physics, and behavior of living organisms. Optimization based on algorithms is the challenge that underlies machine learning, from logistic regression to training neural networks for artificial intelligence. In this paper, a new algorithm called two-stage optimization (TSO) is proposed. The TSO algorithm updates population members in two steps at each iteration. For this purpose, a group of good population members is selected and then two members of this group are randomly used to update the position of each of them. This update is based on the first selected good member at the first stage, and on the second selected good member at the second stage. We describe the stages of the TSO algorithm and model them mathematically. Performance of the TSO algorithm is evaluated for twenty-three standard objective functions. In order to compare the optimization results of the TSO algorithm, eight other competing algorithms are considered, including genetic, gravitational search, grey wolf, marine predators, particle swarm, teaching-learning-based, tunicate swarm, and whale approaches. The numerical results show that the new algorithm is superior and more competitive in solving optimization problems when compared with other algorithms
Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets
The increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in which players (e.g., consumers, prosumers, or producers) at the upper level try to maximize their profits, whereas a market mechanism at the lower level maximizes the energy transacted. However, the strategic bidding in local energy markets is a complex NP-hard problem, due to its inherently nonlinear and discontinued characteristics. Thus, this article proposes the application of a hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem. The proposed CE-CMAES uses cross entropy for global exploration of search space and covariance matrix adaptation evolution strategy for local exploitation. The CE-CMAES prevents premature convergence while efficiently exploring the search space, thanks to its adaptive step-size mechanism. The performance of the algorithm is tested through simulation in a practical distribution system with renewable energy penetration. The comparative analysis shows that CE-CMAES achieves superior results concerning overall cost, mean fitness, and Ranking Index (i.e., a metric used in the competition for evaluation) compared with state-of-the-art algorithms. Wilcoxon Signed-Rank Statistical test is also applied, demonstrating that CE-CMAES results are statistically different and superior from the other tested algorithms.This work has received funding from the EU Horizon 2020 research and innovation program
under project TradeRES (grant agreement No 864276). The authors acknowledge the work facilities
and equipment provided by GECAD research center (UIDB/00760/2020 and UIDP/00760/2020) and
grant CEECIND/02814/2017.info:eu-repo/semantics/publishedVersio
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