This paper presents an artificial fish swarm algorithm (AFSA) to solve the multicast routing problem, which is abstracted as a Steiner tree problem in graphs. AFSA adopts a 0-1 encoding scheme to represent the artificial fish (AF), which are then subgraphs in the original graph. For evaluating each AF individual, we decode the subgraph into a Steiner tree. Based on the adopted representation of the AF, we design three AF behaviors: randomly moving, preying, and following. These behaviors are organized by a strategy that guides AF individuals to perform certain behaviors according to certain conditions and circumstances. In order to investigate the performance of our algorithm, we implement exhaustive simulation experiments. The results from the experiments indicate that the proposed algorithm outperforms other intelligence algorithms and can obtain the least-cost multicast routing tree in most cases.

Interactive evolutionary computation (IEC) finds diverse applications in the domain of sound. However, there is a lack of methods and operations for combining sound elements into a composite sound during the crossover phase of sound composition. To this end, we propose a novel composite crossover operation using paired comparison-based interactive differential evolution. This operation integrates the target and mutant vectors into a composite sound through linear rescaling, enabling users to synthesize and assess their preferred sounds during the evaluation phase. Moreover, an optimization-stopping mechanism is incorporated to allow users to halt the process when a satisfactory sound is produced. This helps to alleviate user fatigue by eliminating unnecessary candidate sounds and improving the efficiency of the composition process by reducing redundant generations and time consumption. The efficacy of this operation was demonstrated through comparative testing and sound quality analysis. Furthermore, this paper presents an original analytical approach based on four fundamental attributes of sound for analyzing human auditory preferences in composite sounds. This method combines both quantitative and qualitative paradigms, achieved through principal component analysis (PCA) and human subjective auditory analysis. These discoveries have significant implications for both sound composition and the analysis of human auditory perception.

As an effective multicast application mechanism, the application layer multicast (ALM) determines the path of data transmission through a routing tree. In practical applications, multiple multicast sessions often occur simultaneously; however, few studies have considered this situation. A feasible solution is to sequentially optimize each co-existing ALM routing tree. However, this approach can lead to node congestion, and, even if the node out-degree reservation strategy is adopted, an optimal solution may not be obtained. In this study, to solve the problem of routing tree construction for multiple co-existing application layer multicast sessions, an optimization model that minimizes the overall delay and instability is constructed, and a one-off optimization method based on the discrete artificial fish swarm algorithm (DAFSA) is proposed. First, Steiner node sets corresponding to the multicast sessions are selected. Then, the routing trees for each multicast session are obtained through the improved spanning tree algorithm based on the complete graph composed of Steiner node sets. The experimental results show that the proposed method can simultaneously obtain multiple co-existing ALM routing trees with a low total delay and low instability. Even if the input is a single multicast session, it can lead to ALM routing trees with a lower delay and less instability than other algorithms, and the introduction of a penalty function can effectively avoid the problem of excessive replication and forwarding loads on some end-hosts. In addition, the proposed algorithm is insensitive to parameter changes and exhibits good stability and convergence properties for networks of different sizes.

The multiobjective flexible job shop scheduling problem (MOFJSP) seeks to jointly minimize makespan and maximum machine workload under coupled opera-tion-sequencing and machine-assignment decisions. Although the nondominated sorting genetic algorithm II (NSGA-II) is widely used for the MOFJSP, its performance can be constrained by imbalanced machine workloads, low-quality initial populations, and limited adaptation to evolving population states. This study develops a Hy-brid-Initialized Load-Aware Adaptive NSGA-II (HLA-NSGA-II) that integrates load-aware generation of machine-assignment vectors, dynamic bottleneck-aware hy-brid initialization, and multi-operator neighborhood search with closed-loop adaptive operator selection. These mechanisms respectively incorporate current workloads into machine assignment, prioritize jobs according to the evolving partial schedule, and update neighborhood-operator selection probabilities using relative hypervolume im-provement, objective-space diversity, and search stagnation. Experiments on the Ka-cem and Brandimarte benchmark sets show that the best solutions obtained by HLA-NSGA-II attain the known makespan lower bounds on all four Kacem instances. On the ten Brandimarte instances, HLA-NSGA-II achieves the lowest mean makespan on nine and the lowest or tied-lowest mean maximum machine workload on seven. Ablation studies further support the effectiveness of the proposed mechanisms. Over-all, HLA-NSGA-II achieved competitive performance on the tested instances, with workload outcomes varying across instances and higher computational cost than HSGA. This reflects the trade-off between solution quality and computational cost.