Social Network Analysis: List of Notation

Sparse multi-objective optimization problems (SMOPs) frequently exist in a variety of disciplines such as machine learning, economy, and signal processing. Evolutionary algorithms have demonstrated their proficiency in optimizing complex problems in recent years, although their performance often deteriorates significantly on large-scale SMOPs. In an effort to accelerate the convergence, this paper suggests a multi-granularity variable clustering method for evolutionary algorithms. This method estimates the sparse distribution of decision variables at each generation and partitions them into a varying number of layers, each with a distinct probability of being zero. These clustering outcomes inspire the development of a crossover operator and a mutation operator, which prove adept at efficiently generating sparse solutions. Experimental evaluations on both benchmark and real-world SMOPs confirm that an evolutionary algorithm incorporating the new crossover operator and mutation operator converges more rapidly than its state-of-the-art counterparts. 

A multi-granularity clustering based evolutionary algorithm for large-scale sparse multi-objective optimization

The distributed permutation flowshop problem (DPFSP) has been extensively studied in recent years. However, most of the research has overlooked the disturbance factors in the processing environment, such as the arrival of new jobs. To address this issue, a nondominated sorting genetic algorithm-II (NSGA-II) with Q-learning has been developed. First, an iterated greedy algorithm (IG) is proposed to generate an initial solution for the first stage. Then, the NSGA-II algorithm is designed to optimize dual-objective problems in the second stage, and the Q-learning algorithm is used to adjust the algorithm parameters. Next, two local search strategies based on key factories are adopted, including critical factory-based insert and swap operations. Finally, a comprehensive experiment of the proposed algorithm against other advanced multiobjective algorithms is conducted. The results confirm that the proposed algorithm can solve the distributed permutation flowshop rescheduling problem with high efficiency. 

Nondominated sorting genetic algorithm-II with Q-learning for the distributed permutation flowshop rescheduling problem

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. 

A novel evaluation method for renewable energy development based on improved sparrow search algorithm and projection pursuit model

Cancer poses a significant threat to life due to its aggressive nature, high potential for metastasis, and heterogeneity. Globally, both the men and women are mostly affected by lung and colon (LC) cancer. Early and accurate detection is essential to enhance the well-being of patients facing LC cancer. The traditional techniques face challenges such as low accuracy and high execution time. This work combines the pre-trained deep learning (DL) models like ResNet-50, InceptionV3, and DenseNet with Kernel Extreme Learning Machine (KELM) for the accurate and fast diagnosis of LC cancer using histopathology images. The pre-trained DL models provide a strong foundation for feature extraction, capturing intricate patterns indicative of cancerous tissues. Feature fusion combines the complementary strengths of multiple pre-trained models, enhancing the method's ability to capture diverse features and improve classification accuracy. KELM efficiently handles the high-dimensional feature space generated by the DL models, ensuring fast and accurate classification. Mutation Boosted Dwarf Mongoose Optimization Algorithm (MB-DMOA) further optimizes the model parameters, mitigating the risk of getting stuck in local optima and facilitating better convergence towards the global optimum. By leveraging the advantages of each technique and integrating them into a cohesive framework, the proposed approach achieves superior performance in diagnosing LC cancer, ultimately improving patient outcomes. The proposed study attains accuracy (98.9%), F1-score (97.6%), specificity (96.5%), precision (96.7%) and sensitivity (95.8%). The results illuminate the efficacy of this approach within therapeutic environments. 

A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM

A Pearson correlation-based adaptive variable grouping method for large-scale multi-objective optimization

Megaprojects are implemented by different organisations, such as owners, consultants, and contractors. Gradually, these organisations and their connections can form business networks that influence both the market position of individual organisations and project performance. Previous research on large-scale projects mainly focused on static and homogeneous networks that were constructed by one individual project and/or carried out over one-off collaboration. However, this neglected the consideration of project network diversity, as well as repetitive, dynamic, cross-project coopetition relationship (i.e., collaboration and competition) and long-term business networks formed by key actors. Here, we chose Chinese skyscrapers over 300 meters that were built from 1996 to 2015 as typical megaproject cases and analysed the formation and evolution of megaproject business networks from the perspective of interorganisational coopetition. We identified the key actors involved and empirically studied their dynamic network positions over time. The main contributions of this study are threefold. First, we found that past collaboration experience has direct and dynamic impacts on the formation of megaproject business networks. Second, we identified key actors in the interorganisational business network and unveiled their dynamic positions with clear patterns. Third, we highlighted the temporal-spatial effect on the formation and development of business networks, alongside developments in the megaproject market. The findings of this study also provide practical applications for owners to choose collaboration partners and to build high-performance teams and for suppliers to enter and sustain the business in the megaprojects networks.

/pdf/the-formation-and-evolution-of-interorganisational-business-362z2kfrs2.pdf

The Formation and Evolution of Interorganisational Business Networks in Megaprojects: A Case Study of Chinese Skyscrapers

An adaptive variance vector-based evolutionary algorithm for large scale multi-objective optimization

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A Pearson correlation-based adaptive variable grouping method for large-scale multi-objective optimization

The Formation and Evolution of Interorganisational Business Networks in Megaprojects: A Case Study of Chinese Skyscrapers

An adaptive variance vector-based evolutionary algorithm for large scale multi-objective optimization