A new hybrid method combining genetic algorithm and coordinate search method

This paper presents the results of an exploratory study aimed at investigating the impacts of optimisation of PV plant locations in South Africa in the context of the seasonal and diurnal cycles associated with the system load profiles and solar generation profiles. The distribution of a normalised per-unit generation capacity is optimised across a set of locations chosen to represent the diurnal cycle along a west-east axis and the seasonal cycle along a north-south axis, using local solar irradiance profiles for the candidate sites. Optimisations are conducted for a range of objective functions representing different scenarios defined in terms of seasonal considerations and time-of-use (TOU) periods using both genetic algorithm (GA) and pattern search methods in the MATLAB simulation environment. Comparative analysis of the optimisation results indicate that the latitude of PV plant locations plays a significant role in seasonal performance, while optimisation along a longitude dimension offers higher power generation during daily peak demand periods.

Despite the high level of interpretability of the Classification and Regression Tree (CART) as one of the widely-used data mining models, its results usually are less accurate than other models. To address the low accuracy of the CART model, this paper developed two new and enhanced CART-based algorithms that can provide both high interpretability and high accuracy for flood susceptibility mapping. In the developed models, various hyperparameters of the CART were explicitly tuned using the genetic algorithm (GA) and grid search (GS) methods. Eleven variables and 222 flood locations were used in Sardabroud watershed in Iran for modelling. The results showed area under the receiver operating characteristic curve (AUROC) values of 0.884, 0.927 and 0.908, respectively, for the CART, CART-GS and CART-GA models in the validation run. The results showed that the developed models can produce the output that satisfies both accuracy and interpretability criteria.

Optimization of building thermal design and control by multi-criterion genetic algorithm

In the highly dynamic design environments, task allocation is subject to considerable design changes. The task reallocating to cope with various unexpected design changes becomes an increasingly important issue in the complex product design (CPD). In this paper, we propose a task reallocating model based on an adaptive multi-objective genetic algorithm and Tabu search (AMOGATS) method to study the dynamic task allocating procedure considering design changes, which integrates the advantages of adaptive genetic algorithm and Tabu search algorithm. Three objectives, i.e. completion time, robustness and stability, are considered simultaneously to measure the task allocation performance. A real example is employed to test and evaluate the performance of proposed method. The computational results imply that the proposed AMOGATS performs better than the heuristic algorithms available in the literature, which has more advantages on the convergence rate and running efficiency than the other algorithms, along with a better solution. This work provides a useful decision support to carry out the task reallocating with high levels of flexibility and efficiency during the process of CPD.

The present work proposes an approach to design and optimize the internal geometry of angular contact ball bearings (ACBBs) with the help of the genetic algorithm (GA) and the grid search method (GSM). Rolling bearings have very complex internal geometry and have varied types. In literature among other types of bearings, very few authors attempted optimization of ACBBs, and subjected to very few constraints. Additionally, none of them considered the free contact angle as a design parameter, which is very important. So, the main motivation of the present work is to see the effect of inclusion of free contact angle in the optimum design of ACBBs. To optimize the geometry of ACBBs, in present study, we have taken a set of 11 design variables out of which 5 are geometric design variables and the rest are constraint parameters. The solution space has been bounded using a set of 20 realistic constraints. This way performing the same analysis with more realistic constraint model of the bearing, increases the feasibility and reliability of the optimized solutions. The results obtained from the genetic algorithm and the grid search method for the dynamic capacity of the bearing were compared to make a evaluate performance of two approaches. The simulation is performed on a series of selected ACBBs. Using the GA, for 7006 ACBB 83% improvement in its dynamic capacity over its rated values has been achieved, while using the GSM improvement is 43%. However, time taken by the GA over the GSM is one-sixth. A sensitivity analysis is done to check the most affecting parameters for the selected objective function. The present methodology helps the designers to reduce the design time, and select the optimum values of design parameters for manufacturing the custom bearing by prioritizing a specific objective.

In order to solve the problem of low fault detection rate of combinatorial navigation due to the mismatch of support vector machine parameters, this paper uses genetic algorithm and lattice search method to find the optimal support vector machine penalty parameter C and kernel function parameter g. The result of the search is brought into the support vector machine to obtain the classification model and finally classify the combinatorial navigation data. The results show that the genetic algorithm has a faster search speed and a higher classification accuracy.

Improved Genetic Algorithm with Local Search for Satellite Range Scheduling System and its Application in Environmental monitoring

In this paper, we investigate the performance of intelligent reflecting surface (IRS) aided cooperative non-orthogonal multiple access (NOMA) system in an energy-harvesting (EH) relay network. Particularly, in our proposed system, a base station communicates with two NOMA users with a help of the best EH relay employing a power-splitting (PS) architecture and IRS component. To boost the system performance, an important object is the maximization of sum data rate (SDR) which is proposed by using particle swarm optimization (PSO) based on power allocation (PA) for each destination user and phase shift of IRS. To validate the performance of PSO, another swarm intelligence technique as Genetic Algorithm (GA) and Exhaustive Search (ES) methods are considered. Finally, the outstanding performance of NOMA-supported IRS in comparison with the system without IRS is also shown in this paper.

This study focuses on developing a multi-event simultaneous departure routing for the Amsterdam Airport Schiphol. The aircraft ground tracks are shaped by rational Bezier curve, and the trajectory optimization problem is reduced to a constrained parametric optimization formulation with few unknown optimizers. In order to handle the constraints, the continuous tracks are discretized into a series of data points and approximated by piecewise linear curves. A data-points selection strategy is introduced to improve the calculation efficiency and guarantee the feasibility of the optimized track. The original constrained parameter optimization problem is transformed to an unconstrained one using a penalty function method and subsequently solved by a genetic algorithm and pattern search method. The details of penalty function formulation are given in this study. The feasibility of the proposed method is demonstrated in numerical examples. The results show that both the number of people highly annoyed and track distance are decreased in simultaneous optimization relative to a scenario where departure routes are optimized in isolation.

In the computer simulation of musculoskeletal human model during walking, mathematical optimization is used to estimate muscle parameters, e.g. the muscle force, work and power. Though there are many mathematical optimization methods available, some of them are suitable for the estimate of muscle parameters in simulations and some not. This study aimed to compare several main optimization methods in terms of the feasibility and possibilities in calculating muscle parameters in musculoskeletal modeling. The results showed that different optimization methods can produce different muscle force patterns. Using electromyography obtained from experiment as the criteria, it was found that the min-max minimization or its combination with other methods are better than the genetic algorithm and pattern search methods in terms of the similarity between the simulated muscle forces and the experimental electromyography patterns.

Wireless sensor networks (WSNs) are used in industrial applications and focused on target coverage and node connectivity based WSNs. The set of sensors and targets is placed in optimal position the target coverage and node connectivity achieving maximum with limited senor nodes. To resolve this problem, the proposed hybrid genetic algorithm combined with lifting wavelet multi-resolution principles for recognizing optimal position for sensors to cover entire targets present in the fields. The hybrid genetic algorithm randomly identifies each sensor position and 2D Haar lifting wavelet transform to improve the quality of target coverage by adjusting node position. The 2D Haar lifting decomposes the population matrix into the optimal position of sensors. Experimental results show the performance of the proposed hybrid genetic algorithm and fast local search method compared with available algorithms improves the target coverage and the number of nodes with varying and fixed sensing ranges with a different region.

Distributed Collaborative Complete Coverage Path Planning Based on Hybrid Strategy

The current paper considers the joint precoding and transmit antenna selection to reduce hardware cost, such as radio-frequency chains, associated with antennas in the downlink of multiuser multiple-input multiple-output systems with limited feedback. The joint precoding and transmit antenna selection algorithm requires an exhaustive search of all possible combinations and permutations to find the optimum solution at the transmitter, thus resulting in extremely high computational complexity. To reduce the computational load while still maximizing channel capacity, the cross-entropy (CE) method is adopted to determine the suboptimum solution. Compared with the conventional genetic algorithm and random search method, the CE method provides better performance under the same computational complexity, as shown by the simulation results.

Remarks on the application of genetic algorithm and tabu search method to nonlinear spanning tree problems

High penetration of PV generation is still a challenge predominantly due to the fact that it does not correlate with the normal peak residential demand. In order to address this issue storage is proposed in many cases. Distributed storage also provides an opportunity for customers to control their time of use and energy cost and provides an additional degree of freedom in the optimization of the feeder operation. The rapid expansion of small scale residential PV/storage requires that they become more visible and controllable on the network to avoid any power quality issues and for demand response management. This paper presents an improved algorithm for optimizing the size and cyclic operation of battery storage at a residential level for a grid connected PV/storage system using a hybrid genetic algorithm and pattern search method. The periodic daily or weekly battery state of charge profile is compactly represented by a vector of Fourier coefficients.