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In recent years, a variety of multi-objective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, the performance of most existing MOEAs is sensitive to the Pareto front (PF) shapes of the problem to be solved, and it is difficult for these algorithms to manage diversity on various types of Pareto fronts (PFs) effectively. To address these issues, this paper proposes an evolutionary algorithm based on diversity ranking method for multi-objective and many-objective optimization. The proposed evolutionary algorithm introduces reference vector adaptation method to solve different shapes of Pareto fronts, and proposes the diversity ranking method to manage diversity. The extensive experimental results demonstrate that the proposed algorithm can solve various types of Pareto fronts, surpassing several state-of-the-art evolutionary algorithms for multi-objective and many-objective optimization.

The development of algorithms to solve Many-objective optimization problems (MaOPs) has attracted significant research interest in recent years. Solving various types of Pareto front (PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm (RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector adaptation. Information concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.

Recent studies have shown difficulties in balancing convergence and diversity for many-objective optimization problems with various types of Pareto fronts. This paper proposes an adaptive reference vector based evolutionary algorithm for many-objective optimization, termed as ARVEA. The ARVEA develops a reference vector adaptation method, which can adapt different types of Pareto fronts by adjusting the distribution of reference vectors. Besides, this algorithm adopts Pareto dominance as the first selection criterion, and the achievement scalarizing function (ASF) is introduced as the secondary selection criterion. The empirical results demonstrate that the proposed ARVEA has good performance for solving problems with various types of Pareto fronts, surpassing several state-of-the-art evolutionary algorithms designed for many-objective optimization.

With the rapid development of smart grid, collaborative communication among many subsystems of power system is becoming more and more frequent. With the progress of communication technology, distribution network communication network is gradually developing towards the direction of heterogeneous network integration. Traditional network management system based on equipment status monitoring cannot fully reflect the operation status of communication network, so this paper studies the network and service monitoring technology of intelligent distribution network communication system. Firstly, the system services of the distribution network communication system are analyzed from the typical business requirements. The traffic requirements, security requirements and real-time requirements are introduced respectively. The flow measurement technology based on passive measurement and the network quality monitoring technology based on active measurement are designed. According to different service requirements, different service detection technologies can be provided to monitor the quality of distribution network service communication network in real time.

Indicator based many-objective evolutionary algorithms generally introduce the performance indicator as the selection criterion in environmental selection. In the calculation of some indicators, the reference points as sampled points on Pareto fronts are very important for their calculation. However, it is difficult to obtain good reference points on various types of Pareto fronts. To address this issue, this paper proposes an enhanced-indicator based many-objective evolutionary algorithm with adaptive reference point, termed EIEA. The algorithm proposes a reference point adaptation method to dynamically adapt the reference points for the calculation of indicators. Moreover, the calculation of IGD-NS is enhanced by employing the modified distance calculation to introduce the Pareto compliant which can further comprehensively measure the convergence and diversity. The proposed EIEA adopts Pareto dominance and the enhanced IGD-NS as the first selection criterion and the secondary selection criterion in environmental selection, respectively. The intensive experiments demonstrate that the proposed algorithm has good performance in solving problems with various types of Pareto fronts, surpassing several representative many-objective evolutionary algorithms for many-objective optimization.

Large deployable mesh reflectors for space applications have continuously been developed in the past several decades. A well-designed cable web configuration is essential to the performance of a deployable mesh reflector. To meet the requirements of high precision and uniform distribution of tension forces, this paper proposes a new approach to design the cable web for elliptical ring truss reflectors based on the pseudo-geodesic method. An offset parabolic reflector is used as an example to further discuss and compare different design approaches. Moreover, an optimization method is introduced to systematically determine an initial profile and tension forces for the reflector. The results illustrate that tension forces using the approach developed by this study are more uniform than that using the traditional method. Finally, a prototype is made to test according to design and analysis. By photogrammetry, the surface precision is less than 0.5mm (RMS). The tension level is better than analysis result. The thermal deformation is less than 0.05mm (RMS) for 40 temperature change, which is measured by photogrammetry.