In this paper, a hybrid genetic algorithm is proposed to solve the no-wait flowshop scheduling problem with the makespan objective. The proposed algorithm hybridizes the genetic algorithm and a novel ...local search scheme. The OA-crossover operator is designed to enhance the capability of intensification in the genetic algorithm. The proposed local search scheme combines two local search methods: the Insertion Search (IS) and a novel local search method called the Insertion Search with Cut-and-Repair (ISCR). These two local search methods play different roles in the search process. The Insertion Search is responsible for searching a small neighborhood while the Insertion Search with Cut-and-Repair is responsible for searching a large neighborhood. The experimental results show the advantage of combining the two local search methods. Extensive experiments were conducted to evaluate the proposed hybrid genetic algorithm and the results revealed that the proposed algorithm is very competitive. It obtained the same best solutions that were reported in the literature for all problems in the benchmark provided by
Carlier (1978). Also, it improved 5 out of the 21 current best solutions reported in the literature and achieved the current best solutions for 14 of the remaining 16 problems in the benchmark presented by
Reeves (1995). Furthermore, the proposed algorithm was applied to effectively solve the 120 problems in the benchmark provided by
Taillard (1990).
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Recently, many machine learning techniques have been successfully employed in photovoltaic (PV) power output prediction because of their strong non-linear regression capacities. However, single ...machine learning algorithm does not have stable prediction performance and sufficient generalization capability in the prediction of PV power output. In this work, a hybrid model (SDA-GA-ELM) based on extreme learning machine (ELM), genetic algorithm (GA) and customized similar day analysis (SDA) has been developed to predict hourly PV power output. In the SDA, Pearson correlation coefficient is employed to measure the similarity between different days based on five meteorological factors, and the data samples similar to those from the target forecast day are selected as the training set of ELM. This operation can effectively increase the number of useful samples and reduce the time consumption on training data. In the ELM, the optimal values of the hidden bias and the input weight are searched by GA to improve the prediction accuracy. The performance of the proposed forecast model is evaluated with coefficient of determination (R2), mean absolute error (MAE) and normalized root mean square error (nRMSE). The results show that the SDA-GA-ELM model has higher accuracy and stability in day-ahead PV power prediction.
•An accurate day-ahead prediction model is proposed for photovoltaic power output.•The genetic algorithm is implemented to automatically perform parameter selection.•The similar day analysis is designed to improve the quality of training dataset.•The proposed SDA-GA-ELM model performs well in accuracy, stability and efficiency.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Battery pack performance is the main concern for electric vehicles and energy storage systems. However, charge imbalance is inevitable due to inconsistent manufacturing techniques and environmental ...conditions. Charge imbalance reduces the power performance and available energy of battery packs. Hence, it is necessary to perform battery equalization. This article proposes an active equalization circuit and a novel equalization strategy to achieve energy redistribution. A bidirectional equalization topology consisting of a forward transformer and switch matrix is proposed first. Then, an innovative equalization strategy based on clustering analysis and genetic algorithm (GA) is developed. Clustering analysis is introduced to identify the target cells to be balanced. To further increase the speed of the equalization process, GA is employed to optimize the classification results. Finally, a series of experiments were conducted to confirm the effectiveness of the proposed topology and the strategy. Both the simulation and experimental results validate that the proposed equalization strategy not only improves the inconsistency but also increases the equalization speed. In practical battery pack experiments, the pack capacity is improved by 16.84% after equalization, and the equalization time is decreased by 23.8% using the proposed method.
Military unmanned aerial vehicles (UAVs) are employed in highly dynamic environments and must often adjust their trajectories based on the evolving situation. To operate autonomously and safely, a ...UAV must be equipped with a path planning module capable of quickly recalculating a feasible and quasi-optimal path in flight while in the event a new obstacle or threat has been detected or simply if the destination point is changed during the mission. To allow for a fast path planning, this paper proposes a parallel implementation of the genetic algorithm on graphics processing unit (GPU). The trajectories are built as series of line segments connected by circular arcs resulting in smooth paths suitable for fixed-wing UAVs. The fitness function we defined takes into account the dynamic constraints of the UAVs and aims to minimize fuel consumption and average flying altitude in order to improve range and avoid detection by enemy radars. This fitness function is also implemented on the GPU and different parallelization strategies were developed and tested for each step of the fitness evaluation. By exploiting the massively parallel architecture of GPUs, the execution time of the proposed path planner was reduced by a factor of 290x compared to a sequential execution on CPU. The path planning module developed was tested using 18 scenarios on six realistic three-dimensional terrains with multiple no-fly zones. We found that the proposed GPU-based path planner was able to find quasi-optimal solutions in a timely fashion allowing in-flight planning.
•Genetic Algorithm is used for tri-objective design of hybrid energy system.•The objective is minimizing the Life Cycle Cost, CO2 emissions and dump energy.•Small split diesel generators are used in ...place of big single diesel generator.•The split diesel generators are aggregable based on certain set of rules.•The proposed algorithm achieves the set objectives (LCC, CO2 emission and dump).
In this paper, a Genetic Algorithm (GA) is utilized to implement a tri-objective design of a grid independent PV/Wind/Split-diesel/Battery hybrid energy system for a typical residential building with the objective of minimizing the Life Cycle Cost (LCC), CO2 emissions and dump energy. To achieve some of these objectives, small split Diesel generators are used in place of single big Diesel generator and are aggregable based on certain set of rules depending on available renewable energy resources and state of charge of the battery. The algorithm was utilized to study five scenarios (PV/Battery, Wind/Battery, Single big Diesel generator, aggregable 3-split Diesel generators, PV/Wind/Split-diesel/Battery) for a typical load profile of a residential house using typical wind and solar radiation data. The results obtained revealed that the PV/Wind/Split-diesel/Battery is the most attractive scenario (optimal) having LCC of $11,273, COE of 0.13 ($/kWh), net dump energy of 3MWh, and net CO2 emission of 13,273kg. It offers 46%, 28%, 82% and 94% reduction in LCC, COE, CO2 emission and dump energy respectively when compared to a single big Diesel generator scenario.
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In order to improve the temperature drift modeling precision of a tuning fork micro-electromechanical system (MEMS) gyroscope, a novel multiple inputs/single output model based on genetic algorithm ...(GA) and Elman neural network (Elman NN) is proposed. First, the temperature experiment of MEMS gyroscope is carried out and the outputs of MEMS gyroscope and temperature sensors are collected; then the temperature drift model based on temperature, temperature variation rate and the coupling term is proposed, and the Elman NN is employed to guarantee the generalization ability of the model; at last the genetic algorithm is used to tune the parameters of Elman NN in order to improve the modeling precision. The Allan analysis results validate that, compared to traditional single input/single output model, the novel multiple inputs/single output model can guarantee high accurate fitting ability because the proposed model can provide more plentiful controllable information. By the way, the generalization ability of the Elman neural network can be improved significantly due to the parameters are optimized by genetic algorithm.
•The temperature variation experiment based on temperature change rate is carried out.•Temperature drift model based on T, temperature change rate and △T is proposed;•The genetic-Elman NN is proposed to establish the temperature drift model.
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In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel ...inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching-learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.
With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power ...peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.
The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex ...optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed.
