In adaptive beamforming by compact arrays, the excitation amplitude and phase of each array element require dynamic optimization for controlling the radiation pattern at different angular sectors. If ...the objective function of adaptive beamformer is defined by some properties other than the deterministic properties of arrays such as directivity or signal-to-noise ratio, evolutionary optimization is advantageously implemented to minimize the objective function. Nevertheless, the convergence time of most of the evolutionary optimization methods exponentially increases by the number of array elements Formula Omitted. For large Formula Omitted, the above convergence issue slows down the beamforming agility. To improve the above inefficiency, we first express the array factor by a Schelkunoff polynomial of order Formula Omitted. Second, a discrete optimization method like genetic algorithm optimally factorizes the polynomial into two polynomials of orders Formula Omitted and Formula Omitted, where Formula Omitted. Third, the locations of zeros of these polynomials are optimized across the visible region by a continuous optimization method like particle swarm optimization to attain the desired objective function. When these two polynomials are multiplied, the resultant polynomial of order Formula Omitted demonstrates improved features with respect to the original polynomial of order Formula Omitted in sensitivity, sidelobe level, and convergence. Representative examples include airborne radars.
Energy storage can address the mismatch of the ratio of heat to electricity between a combined cooling, heating, and power (CCHP) system and its users, and thus, it can significantly improve energy ...efficiency. However, energy storage also increases the complexity of the operation optimization of the system. Existing heuristic optimization algorithms such as genetic algorithm (GA) and particle swarm optimization can hardly obtain the optimal scheduling scheme. In this paper, a hybrid optimization method that combines the GA and dynamic programming (DP) is proposed. The GA is the main optimization framework and is used to optimize the hourly set points of the power generation unit in a day. In the optimization process, the GA generates a feasible solution set, and calls the DP to calculate the optimal energy storage set points for each solution. The DP defines an hour as a decision step, and enumerates all energy storage states in each decision step. This process loops until the optimal solution is obtained. To reduce the computing time, the DP is implemented as a vectorized code. Case studies are conducted to verify the effectiveness of the proposed method. The results demonstrate that the overall performance using the proposed method increases by 1.92% in summer and by 1.91% in winter compared with that using the traditional GA method. Furthermore, the computing time is acceptable for the scheduling of the energy system. The proposed method can also be applied to the operation optimization of the CCHP system considering the demand side response.
•A novel operation strategy is proposed for trigeneration systems with energy storage.•Heuristic algorithm and dynamic programming are combined organically.•Vectorized code is used to improve the computational efficiency of the hybrid method.•The proposed method has advantages on both computing time and optimality.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this research, an algorithm is presented for predicting the remaining useful life (RUL) of aircraft engines from a set of predictor variables measured by several sensors located in the engine. RUL ...prediction is essential for the safety of those aboard, but also to reduce engine maintenance and repair costs. The algorithm combines time series analysis methods to forecast the values of the predictor variables with machine learning techniques to predict RUL from those variables. First, an auto-regressive integrated moving average (ARIMA) model is used to estimate the values of the predictor variables in advance. Then, we use the result of the previous step as the input of a support vector regression model (SVM), where RUL is the response variable. The validity of the method was checked on an extensive public database, and the results compared with those obtained using a vector auto-regressive moving average (VARMA) model. Our algorithm showed a high prediction capability, far greater than that provided by the VARMA model.
•A method to forecast the remaining useful life of aircraft engines is proposed.•The predictor variables were obtained from sensors located in the engine.•The proposed method combines ARIMA and SVM models.•Results of our method unsurpassed those obtained using a VARMA model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper presents a distribution generation (DG) allocation strategy for radial distribution networks under uncertainties of load and generation using adaptive genetic algorithm (GA). The ...uncertainties of load and generation are modeled using fuzzy-based approach. The optimal locations for DG integration and the optimal amount of generation for these locations are determined by minimizing the network power loss and maximum node voltage deviation. Since GA is a metaheuristic algorithm, the results of multiple runs are taken and the statistical variations for locations and generations for DG units are shown. The locations and sizes for DG units obtained with fuzzy-based approach are found to be different than those obtained with deterministic approach. The results obtained with fuzzy-based approach are found to be comparatively efficient in working with future load growth. The proposed approach is demonstrated on the IEEE 33-node test network and a 52-node Indian practical distribution network.
•The finite interval cloud model is introduced into the suitability evaluation.•A genetic algorithm is adopted to solve the combination weights.•The fuzziness and randomness of geological indicators ...are addressed.•The discreteness, correlation, and contrast intensity of geological data are considered.
The development and utilization of urban underground space is an inevitable solution for urban sustainable development. Therefore, it is particularly important to perform suitability evaluation of underground space in various regions of a city. To improve the rationality of the evaluation results, this study proposes an evaluation method based on the finite interval cloud model and genetic algorithm combination weighting. The study considers the starting area of the Wuhan Changjiang New Town as the evaluation area. An evaluation index system is established for 12 indexes of five geological factors. The cloud characteristic parameters of each evaluation factor belonging to different classification levels are obtained through the finite interval cloud model. The forward finite interval cloud generator is used to generate cloud drop graphs of the indexes. Further, the certainty degrees of the evaluation factors belonging to different levels are obtained, which provide the mapping of uncertainty and randomness between the semantic variables and the index values. Subjective and objective weights of indexes are calculated by an improved analytic hierarchy process (AHP) and the criteria importance through intercriteria correlation (CRITIC) method improved with the entropy method. The combination weights are solved by the application of the genetic algorithm, which can comprehensively consider the attributes of discreteness, correlation, and contrast intensity of geological data. By following the above-mentioned procedures, the comprehensive certainty degree of the evaluation sample is obtained according to the combination weights and certainty degrees of the evaluation indexes. According to the principle of the maximum membership degree, the suitability grades of the underground space are determined. This evaluation method comprehensively considers the fuzziness of the suitability grade boundary of the underground space and the advantages of the subjective and objective weighting methods. Thus, the method ensures rationality of the evaluation results to the greatest extent, thereby providing a certain guiding significance for the development of underground space.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Heart disease diagnosis is found to be a challenging issue which can offer a computerized estimate about the level of heart disease so that supplementary action can be made easy. Thus, heart disease ...diagnosis has expected massive attention worldwide among the healthcare environment. Optimization algorithms played a significant role in heart disease diagnosis with good efficiency. The objective of this paper is to propose an optimization function on the basis of support vector machine (SVM). This objective function is used in the genetic algorithm (GA) for selecting the more significant features to get heart disease. The experimental results of the GA–SVM are compared with the various existing feature selection algorithms such as Relief, CFS, Filtered subset, Info gain, Consistency subset, Chi squared, One attribute based, Filtered attribute, Gain ratio, and GA. The receiver operating characteristic analysis is performed to evaluate the good performance of SVM classifier. The proposed framework is demonstrated in the MATLAB environment with a dataset collected from Cleveland heart disease database.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Capacitated facility location problem (CFLP) is a well-known combinatorial optimization problem with applications in distribution and production planning that is classified as an NP-Hard problem. The ...aim is to determine where to locate facilities and how to move commodities such that the customers’ demands are satisfied and the total cost minimized. In this paper, a new hybrid optimization method called Hybrid Evolutionary Firefly-Genetic Algorithm is proposed, which is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication. The method combines the discrete Firefly Algorithm (FA) with the standard Genetic Algorithm (GA). It is devoted to the detailed description of the problem, and an adaption of the algorithm. Computational results on random generated problems consisting of 2000 locations and 2000 customers are reported.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
As optical metasurfaces become progressively ubiquitous, the expectations from them are becoming increasingly complex. The limited number of structural parameters in the conventional metasurface ...building blocks, and existing phase engineering rules do not completely support the growth rate of metasurface applications. In this paper, we present digitized-binary elements, as alternative high-dimensional building blocks, to accommodate the needs of complex-tailorable-multifunctional applications. To design these complicated platforms, we demonstrate adaptive genetic algorithm (AGA), as a powerful evolutionary optimizer, capable of handling such demanding design expectations. We solve four complex problems of high current interest to the optics community, namely, a binary-pattern plasmonic reflectarray with high tolerance to fabrication imperfections and high reflection efficiency for beam-steering purposes, a dual-beam aperiodic leaky-wave antenna, which diffracts TE and TM excitation waveguides modes to arbitrarily chosen directions, a compact birefringent all-dielectric metasurface with finer pixel resolution compared to canonical nano-antennas, and a visible-transparent infrared emitting/absorbing metasurface that shows high promise for solar-cell cooling applications, to showcase the advantages of the combination of binary-pattern metasurfaces and the AGA technique. Each of these novel applications encounters computational and fabrication challenges under conventional design methods, and is chosen carefully to highlight one of the unique advantages of the AGA technique. Finally, we show that large surplus datasets produced as by-products of the evolutionary optimizers can be employed as ingredients of the new-age computational algorithms, such as, machine learning and deep leaning. In doing so, we open a new gateway of predicting the solution to a problem in the fastest possible way based on statistical analysis of the datasets rather than researching the whole solution space.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed ...schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.
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