A Block Storage Optimization Method for Blockchain-Enabled Industrial Internet of Things

A reference direction and entropy based evolutionary algorithm for many-objective optimization

Taking both convergence and diversity into consideration, this paper suggests a vector angle-based evolutionary algorithm for unconstrained (with box constraints only) many-objective optimization problems. In the proposed algorithm, the maximum-vector-angle-first principle is used in the environmental selection to guarantee the wideness and uniformity of the solution set. With the help of the worse-elimination principle, worse solutions in terms of the convergence (measured by the sum of normalized objectives) are allowed to be conditionally replaced by other individuals. Therefore, the selection pressure toward the Pareto-optimal front is strengthened. The proposed method is compared with other four state-of-the-art many-objective evolutionary algorithms on a number of unconstrained test problems with up to 15 objectives. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in keeping a good balance between convergence and diversity. Furthermore, it was shown by the results on two problems from practice (with irregular Pareto fronts) that our method significantly outperforms its competitors in terms of both the convergence and diversity of the obtained solution sets. Notably, the new algorithm has the following good properties: 1) it is free from a set of supplied reference points or weight vectors; 2) it has less algorithmic parameters; and 3) the time complexity of the algorithm is low. Given both good performance and nice properties, the suggested algorithm could be an alternative tool when handling optimization problems with more than three objectives.

© 1997-2012 IEEE. Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of MaOEAs. Unfortunately, it is not easy to constantly maintain a population of solutions with both convergence and diversity. In this paper, an MaOEA based on two independent stages is proposed for effectively solving many-objective optimization problems (MaOPs), where the convergence and diversity are addressed in two independent and sequential stages. To achieve this, we first propose a nondominated dynamic weight aggregation method by using a genetic algorithm, which is capable of finding the Pareto-optimal solutions for MaOPs with concave, convex, linear and even mixed Pareto front shapes, and then these solutions are employed to learn the Pareto-optimal subspace for the convergence. Afterward, the diversity is addressed by solving a set of single-objective optimization problems with reference lines within the learned Pareto-optimal subspace. To evaluate the performance of the proposed algorithm, a series of experiments are conducted against six state-of-The-Art MaOEAs on benchmark test problems. The results show the significantly improved performance of the proposed algorithm over the peer competitors. In addition, the proposed algorithm can focus directly on a chosen part of the objective space if the preference area is known beforehand. Furthermore, the proposed algorithm can also be used to effectively find the nadir points.

In the precursor paper, a many-objective optimization method (NSGA-III), based on the NSGA-II framework, was suggested and applied to a number of unconstrained test and practical problems with box constraints alone. In this paper, we extend NSGA-III to solve generic constrained many-objective optimization problems. In the process, we also suggest three types of constrained test problems that are scalable to any number of objectives and provide different types of challenges to a many-objective optimizer. A previously suggested MOEA/D algorithm is also extended to solve constrained problems. Results using constrained NSGA-III and constrained MOEA/D show an edge of the former, particularly in solving problems with a large number of objectives. Furthermore, the NSGA-III algorithm is made adaptive in updating and including new reference points on the fly. The resulting adaptive NSGA-III is shown to provide a denser representation of the Pareto-optimal front, compared to the original NSGA-III with an identical computational effort. This, and the original NSGA-III paper, together suggest and amply test a viable evolutionary many-objective optimization algorithm for handling constrained and unconstrained problems. These studies should encourage researchers to use and pay further attention in evolutionary many-objective optimization.

X-ray free-electron lasers (XFELs) are cutting-edge scientific instruments for a wide range of disciplines. Conventionally, the narrow bandwidth is pursued in an XFEL. However, in recent years, the large-bandwidth XFEL operation schemes are proposed for X-ray spectroscopy and X-ray crystallography, in which over-compression is a promising scheme to produce broad-bandwidth XFEL pulses through increasing the electron beam energy chirp. In this paper, combining with the beam yaw correction to overcome the transverse slice misalignment caused by the coherent synchrotron radiation, finding out the over-compression working point of the linac is treated as a many-objective (having four or more objectives) optimization problem, thus the non-dominated sorting genetic algorithm III is applied to the beam dynamic optimization for the first time. Start-to-end simulations demonstrate a full bandwidth of 4.6% for Shanghai soft x-ray free-electron laser user facility.

Adaptive decomposition-based evolutionary algorithm for many-objective optimization with two-stage dual-density judgment

ESOEA: Ensemble of single objective evolutionary algorithms for many-objective optimization

Many-objective optimization is a difficulty in the present evolutionary multi-objective optimization community. Integrating decision makers' preferences into multi-objective evolutionary algorithm is considered to be an effective approach. This paper presents a new scheme named bipolar preferences dominance for many-objective optimization problems. In the proposed scheme, the solutions are first sorted by the g-dominance to enhance the efficiency of Pareto sorting, and the non-dominated ones are sorted again based on their similarities to increase the proportion of solutions' comparability in high-dimension space. With bipolar preferences dominance, the race is led to the Pareto optimal area which is close to the positive preference and far away from the negative preference. After combining the proposed scheme with NSGA-II methodology, the effectiveness of 2p-NSGA-II was validated on two to fifteen-objective test problems. Moreover, 2p-NSGA-II provides better result when compared with g-dominance based algorithm g-NSGA-II.

Evolutionary algorithms based on hypervolume have demonstrated good performance for solving many-objective optimization problems. However, hypervolume needs prohibitively expensive computational effort. This paper proposes a simplified hypervolume calculation method which can be used to roughly evaluate the convergence and diversity of solutions. The main idea is to use the nearest neighbors of a particular solution to calculate the volume as the solution’s hypervolume value. Moreover, this paper improves the selection operator and the update strategy of external population according to the simplified hypervolume. Then, the proposed algorithm (SHEA) is compared with some state-of-the-art algorithms on fifteen test functions of CEC2018 MaOP competition, and the experimental results prove the feasibility of the proposed algorithm.

Preference-guided evolutionary algorithms for many-objective optimization

Intelligent data-driven condition monitoring of power electronics systems using smart edge–cloud framework

In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.

Many-objective optimization is a critical research topic in the evolutionary computing community. Many algorithms have been proposed to tackle this problem, with evolutionary algorithms based on the hypervolume being among the most effective ones. However, calculating the hypervolume indicator in high-dimensional objective spaces remains time-consuming. To address this issue, we propose a two-stage hypervolume-based evolutionary algorithm (ToSHV) that separates global search and local search to ensure both convergence and diversity. ToSHV performs a global search in the first stage by generating multiple offspring per generation. We modified the R2HCA method to estimate the overall hypervolume contribution, avoiding the time-consuming nature of updating the hypervolume contribution with the greedy method. In the second stage, only one offspring is produced per generation to emphasize local exploration and enhance population diversity. Furthermore, a stage-switching mechanism is designed to dynamically select the appropriate search mode based on the prevailing population distribution. We evaluate our algorithm on WFG and DTLZ test suites, comparing it with three hypervolume-based algorithms and four state-of-the-art algorithms. Experimental results show that our approach is competitive in most cases.

A region search evolutionary algorithm for many-objective optimization

An adaptive decomposition-based evolutionary algorithm for many-objective optimization