An Effective Dynamic Immune Optimization Control for the Wastewater Treatment Process

Aiming at the conflict of multiple performance indicators in the complex wastewater treatment process (WWTPs), an effective optimization control based on dynamic multi-objective immune (DMOIA-OC) is designed. This method first designs a dynamic optimization control scheme. It also divides the control process into a dynamic optimization layer and a tracking control layer. Secondly, based on analyzing the performance of WWTPs, the optimization layer establishes energy consumption and effluent quality models that adapt to the environment. Thirdly, an adaptive dynamic immune optimization algorithm is proposed to optimize complex and conflicting performance indicators. Besides, a suitable preference solution is selected from many Pareto solutions. It is to obtain the best set values of dissolved oxygen and nitrate nitrogen. Finally, the solution will be tested on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem of multiple performance indicators in WWTPs. And it has a competitive advantage in the control effect.

Multi-Objective Optimized Immune Algorithm For Computing Offloading Problem In Edge Computing Scenes of Internet of Vehicles

With the development of 5G technology, the Internet of Vehicles (IoV) has also received worldwide attention. IoV edge computing achieves the goal of low latency by offloading tasks to the Mobile Edge Computing Server (MECS). However, it is still a challenge to reduce the computing delay of mobile terminal devices while ensuring the low energy consumption and load balancing of servers. In order to solve this problem, the system model, delay model, load balancing model, energy consumption model and objective optimization model are established in this paper. A computational unloading scheme based on multi-objective immune optimization algorithm is proposed. Finally, this scheme is compared with the reference scheme and the literature scheme. Simulation experiments show that the proposed scheme can effectively reduce the average unload delay of users, optimize the workload between servers, and effectively reduce energy consumption. Its performance is better than the schemes in the literature.

Application of Optimization Algorithms in Clusters

The structural characterization of clusters or nanoparticles is essential to rationalize their size and composition-dependent properties. As experiments alone could not provide complete picture of cluster structures, so independent theoretical investigations are needed to find out a detail description of the geometric arrangement and corresponding properties of the clusters. The potential energy surfaces (PES) are explored to find several minima with an ultimate goal of locating the global minima (GM) for the clusters. Optimization algorithms, such as genetic algorithm (GA), basin hopping method and its variants, self-consistent basin-to-deformed-basin mapping, heuristic algorithm combined with the surface and interior operators (HA-SIO), fast annealing evolutionary algorithm (FAEA), random tunneling algorithm (RTA), and dynamic lattice searching (DLS) have been developed to solve the geometrical isomers in pure elemental clusters. Various model or empirical potentials (EPs) as Lennard–Jones (LJ), Born–Mayer, Gupta, Sutton–Chen, and Murrell–Mottram potentials are used to describe the bonding in different type of clusters. Due to existence of a large number of homotops in nanoalloys, genetic algorithm, basin-hopping algorithm, modified adaptive immune optimization algorithm (AIOA), evolutionary algorithm (EA), kick method and Knowledge Led Master Code (KLMC) are also used. In this review the optimization algorithms, computational techniques and accuracy of results obtained by using these mechanisms for different types of clusters will be discussed.