A niche-elimination operation based NSGA-III algorithm for many-objective optimization

Bi-level hybrid game model for optimal operation of multi-function reservoir considering integrated water resource management

Damming can promote flood control, water supply, power generation, and shipping but often changes the downstream hydrological regimes, producing adverse externality effects. Maximizing downstream social and ecological benefits will inevitably reduce upstream power generation. This study presents two novel bi-level hybrid game models, called the non-cooperative hybrid game model (NCHG) and the cooperative hybrid game model (CHG), to facilitate integrated water resource management in reservoir systems. The performance index of propensity to disrupt is applied to evaluate the stability of CHG, and an improved reliability index and Gini coefficient are adopted to evaluate the reliability and equity of both two models. The Three Gorges Reservoir and its adjacent cities were chosen as a case to inspect the two models' performance. A range of scheduling schemes was derived by proposed bi-level hybrid game models in wet, normal, and dry years. Results reveal that (i) the RI values of the watershed system obtained by the CHG are less than those in NCHG in three typical years (for example, 0.1201 VS 0.1930 in the wet year), showing higher systemic reliability. The Gini coefficients of the watershed system obtained by the CHG are all less than those obtained by the NCHG in all typical years (for example, 0.1016 VS 0.1020 in the wet year), which shows better performance of CHG on fairness for the allocation results; (ii) in the case of a multi-function reservoir system, the CHG generates favorable allocation schemes with higher systemic characteristic values by 32.43, 34.39, and 33.54 in wet, normal, and dry years, respectively, than those in NCHG (32.03, 33.16, and 31.42 in wet, normal, and dry years, respectively); (iii) compared with NCHG, the economic benefits obtained by CHG decreased by 0.98%, 1.04%, and 5.42% in wet, normal, and dry years, respectively; the social negative benefits decreased by 3.49%, 9.84%, and 28.69%; and the ecological negative benefits decreased by 1.77%, 5.65%, and 5.59%, respectively. It indicates that a minor sacrifice of the reservoir benefit could significantly improve the welfare at the system level by the CHG. The developed CHG can provide optimal water scheduling schemes in balancing inter-regional water conflicts and can be widely used to produce an equilibrium management strategy for a multi-function reservoir system.

Multi-objective optimization configuration of redundant electromagnetic actuators in fault-tolerant control of active magnetic bearing system

Fault-tolerant control of active magnetic bearing (AMB) systems with redundant electromagnetic actuators (EMAs) based on generalized bias current linearization has become a practical technique to address EMA/amplifier faults. In this method, the configuration of multi-channel EMAs involves solving a high-dimensional and nonlinear problem containing complex constraints offline. This article develops a general framework for the EMAs multi-objective optimization configuration (MOOC) by combining the non-dominated sorting genetic algorithm III (NSGA-III) and the sequential quadratic programming (SQP) with the designing of objectives, handling of constraints, consideration of the iterative efficiency and the diversity of solutions. The numerical simulation results confirm the feasibility of the framework for searching the non-inferior configurations and reveal the function mechanism that intermediate variables of the nonlinear optimization model on AMB performance. Finally, the best configurations identified using the technique for order preference by similarity to an ideal solution (TOPSIS) are applied to the 4-DOF AMB experimental platform. Experiments further indicate that the work in this paper provides a novel way with good performance and high reliability for solving the EMAs MOOC problem in fault-tolerant control of AMB systems.

Identification and restoration of hydrological processes alteration during the fish spawning period

The hydrological processes play an important role in stimulating fish spawning behavior. Changes in the natural hydrological processes will alter the populations and distribution of fish, which may have a negative impact on the native aquatic organisms. The aim of this study is to identify the alteration of the water rising process during the fish spawning period and to construct an ecological flow optimization model to restore the water rising conditions for fish reproduction. The Mann-Kendall test and the sliding t-test were used to detect the mutation year of the mean daily flow data sets in the fish spawning period in each monitoring year. Then the data sets can be divided into pre-altered and post-altered periods. The water rising process was characterized by the water rising processes count, the duration, the daily flow increase rate, the date of the water rising process, and the initial water rising flow. The changes in hydrological processes in the middle reaches of the Yangtze River were investigated by comparing the post-altered and pre-altered characteristic parameters. Furthermore, we integrated the statistical values of the five characteristic parameters in pre-altered into an ecological flow optimization model to simulate the natural water rising processes for the spawning of the Four Major Chinese Carps (FMCC) and Chinese Sturgeon (CS). The analysis showed that after the hydrological mutation year, the duration and the initial water rising flow in the FMCC spawning season were increased, with hydrological alteration degrees of 63.10% and 70.16%, respectively; however, the daily flow increase rate was significantly decreased, with hydrological alteration of 86.50%. During the CS spawning season, the water rising processes count and the initial water rising flow were dramatically altered parameters, with hydrological alteration degrees of 50.86% and 83.27%, respectively. The former parameter increased, but the latter decreased significantly in the post-altered period. To induce the spawning activity of FMCC and CS, appropriate ecological flows and hydrological parameters were proposed. These results showed that during the spawning seasons of FMCC and CS, the hydrological processes of the middle reaches of the Yangtze River changed significantly. Therefore, ecological flow must be ensured through ecological operation of upstream reservoirs to provide suitable spawning conditions in target fish spawning grounds.

Machine learning approach for corona virus disease extrapolation: A case study

Supervised/unsupervised machine learning processes are a prevalent method in the field of Data Mining and Big Data. Corona Virus disease assessment using COVID-19 health data has recently exposed the potential application area for these methods. This study classifies significant propensities in a variety of monitored unsupervised machine learning of K-Means Cluster procedures and their function and use for disease performance assessment. In this, we proposed structural risk minimization means that a number of issues affect the classification efficiency that including changing training data as the characteristics of the input space, the natural environment, and the structure of the classification and the learning process. The three problems mentioned above improve the broad perspective of the trajectory cluster data prediction experimental coronavirus to control linear classification capability and to issue clues to each individual. K-Means Clustering is an effective way to calculate the built-in of coronavirus data. It is to separate unknown variables in the database for the disease detection process using a hyperplane. This virus can reduce the proposed programming model for K-means, map data with the help of hyperplane using a distance-based nearest neighbor classification by classifying subgroups of patient records into inputs. The linear regression and logistic regression for coronavirus data can provide valuation, and tracing the disease credentials is trial.

Green Landscape Design Based on Niche Genetic Algorithm for E-Business Solutions

In order to solve the unreasonable problems in green environmental protection design, this paper proposes a green building landscape space environment optimization design scheme based on niche genetic algorithm, which optimizes the building landscape size by multi-objective. The target of optimization includes the cost of building and public facilities such as green belt. The purpose of this article is to present an optimization design scheme using genetic algorithm that can optimize the size of landscape and cost reduction and providing facilities by processing the information. The results show that: People's satisfaction value of square and Pedestrian Street is 0.61 and 0.38, respectively. In conclusion it has been analyzed that the scientific planning and design of urban architectural landscape is of great significance to improve the urban appearance. It has been further concluded that the proposed approach enhances the quality of urban environment, development of urban economy and open to the outside world.

An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism

Balancing convergence and diversity has become a key point especially in many-objective optimization where the large numbers of objectives pose many challenges to the evolutionary algorithms. In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems.