Economic Dispatch Methods for Smart Grid Based on Improved SPEA2 and Improved NSGA2

The severity of the ongoing environmental crisis has prompted the development of renewable energy generation and smart grids integration. The access of enewable energy makes the economic dispatching of smart grid complicated. Therefore, the economic dispatching model for smart grid is very necessary. This paper presents an economic dispatching model of smart power grid, which considers both economy and pollution emission. The smart grid model used for the simulation is construced of wind energy, solar energy, fuel cell, and thermal power, and the use of fuel cell enables the smart grid to achieve multi-energy complementar. To overcome the defect of the traditional centralized communication methods, which are prone to communication jams, this paper adopts a multi-agent inform ation exchange method to improve the stability and efficiency. In terms of the solution method for this model, this paper proposes Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2) and Improved Non-dominated Sorting Genetic Algorithm 2(INSGA2) that solves the economic dispatch problem of a smart grid. The strength Pareto evolutionary algorithm 2(SPEA2),non-dominated sorting genetic algorithm 2(NSGA2) and the improved algorithms are simultaneously applied to the proposed smart grid model for economic dispatching simulation. The simulation results show that ISPEA2 and INSGA2 are effective. ISPEA2 and INSGA2 have shown improvements over SPEA2 and NSGA2 in accuracy or running times.

An Algorithm of Mobile Edge Computing Offloading Based on Improved SPEA2 Algorithm

Internet of things devices can offload some tasks to the edge servers through the wireless network, thus the computing pressure and energy consumption are reduced. But this will increase the cost of communication. Therefore, it is necessary to maintain the balance between task execution energy and experiment when designing the offloading strategy for the edge computing scenario of the Internet of things. This paper proposes an offloading strategy which can optimize the energy consumption and time delay of task execution at the same time. This strategy satisfies different preferences of users. First, the above task is modeled as a multi-objective optimization problem, and the Pareto solution set is found by improving the strength Pareto evolutionary algorithm (SPEA2). Based on the Pareto set, the offloading strategy satisfying the requires of users with different preferences by offloading cost estimation. Second, a simulation experiment is carried out to verify the robustness of the improved SPEA2 algorithm under the influence of different main parameters. By comparing with other algorithms. It is proved that the improved SPEA2 algorithm can minimize the balance between task execution delay and energy consumption.

A New Multiobjective Time-Cost Trade-Off for Scheduling Maintenance Problem in a Series-Parallel System

In this paper, a modified model of Nondominated Sorting Genetic Algorithm 2 (NSGA-II), which is one of the Multiobjective Evolutionary Algorithms, is proposed. This algorithm is a new model designed to make a trade-off between minimizing the cost of preventive maintenance (PM) and minimizing the time taken to perform this maintenance for a series-parallel system. In this model, the limitations of labor and equipment of the maintenance team and the effects of maintenance issues on manufacturing problems are also considered. In the mathematical model, finding the appropriate objective functions for the maintenance scheduling problem requires all maintenance costs and failure rates to be integrated. Additionally, the effects of production interruption during preventive maintenance are added to objective functions. Furthermore, to make a better performance compared with a regular NSGA-II algorithm, we proposed a modified algorithm with a repository to keep more unacceptable solutions. These solutions can be modified and changed with the proposed mutation algorithm to acceptable solutions. In this algorithm, modified operators, such as simulated binary crossover and polynomial mutation, will improve the algorithm to generate convergence and uniformly distributed solutions with more diverse solutions. Finally, by comparing the experimental solutions with the solutions of two Strength Pareto Evolutionary Algorithm 2 (SPEA2) and regular NSGA-II, MNSGA-II generates more efficient and uniform solutions than the other two algorithms.

New hybrid approach HNSGA-III&SPEA/R: Application to optimization of powertrain mount system stiffness parameters

In this study, a new methodology, hybrid NSGA-III with SPEA/R (HNSGA-III&SPEA/R), has been developed to design and achieve cost optimization of powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R is proposed with the integration of Strength Pareto evolutionary algorithm based on reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R, NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated respectively. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem.

A multi-objective evolutionary scheme for control points deployment in intelligent transportation systems

One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the non-dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength Pareto evolutionary algorithm -II (SPEA-II); and the Pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using Pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.