Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the raccoon optimization algorithm (ROA). The ROA is inspired by the rummaging behaviors of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviors exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA’s ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other well-known optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time.

This paper proposes an incremental optimization framework for verifying graph transformation systems to overcome the state space explosion (SSE). SSE refers to the exponential growth of the number of possible states in a system during its verification. The framework maps the verification problem to a search problem and incrementally generates the state space. The generated increments can still be significant in size, thus we use the Raccoon Optimization Algorithm (ROA), to non-exhaustively search through the state space. ROA selects sequences of states with a higher potential of having deadlock in increments, which prevents SSE and ensures that memory capacity is not exceeded. However, there is possibility that the migration method of ROA lead to a loss of diversity in the population, reducing the algorithm’s ability to explore new regions of the search space. To address this issue, we propose a new migration method for ROA, called Improved ROA (IROA), which preserves diversity in the population and reduces execution time and the risk of getting stuck in local optima. Our approach is evaluated using the Groove simulation tool and compared with other relevant meta-heuristic algorithms in terms of computation time and memory consumption. The experimental results show that IROA outperforms both ROA and other relevant meta-heuristic algorithms that we compared in terms of computation time and memory consumption, with total efficiency of 1.043 and 1.02, respectively, demonstrating its effectiveness in verifying massive state spaces without facing state space explosion in a reasonable time.

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

Delivering optimum performance on a parallel computer is highly dependant on the efficiency of the scheduling and mapping procedure. If the composition of the parallel application is known a prior, the mapping can be accomplished statically on the compilation time. The mapping algorithm uses the model of the parallel application and maps its tasks to processors in a way to minimize the total execution time. In this paper, current modelling approaches have discussed. Later, a new modelling schema named Model of Exascale Message-Passing Programs on Heterogeneous Architectures (MEMPHA) has proposed. A comparative study has been performed between MEMPHA and existing models. To exhibit the efficiency of the MEMPHA, experiments have performed on a set of data-set hypergraphs. The results obtained from the experiments show that deploying the MEMPHA helps to optimize metrics, including the congestion, total communication volume and maximum volume of data being sent or received. These improvements vary from 76 % to 1 %, depending on the metric and benchmark model. Moreover, MEMPHA supports the modelling of applications with multiple producers for a single data transmission, where the rest of the approaches fail.

To handle task execution, modern supercomputers employ thousands (or millions) of processors. In such supercomputers, task scheduling has a meaningful impression on system performance. To improve efficiency, task scheduling algorithms aim to decrease the volume of communication and the number of message exchanges. These efforts, however, result in other bottlenecks, such as high-link congestion. In addition, the heterogeneity of processors and networks is another major challenge for schedulers. This paper presents a new algorithm for scheduling called Heterogeneity-Aware Task Scheduling (HATS). The proposed algorithm adopts an updated multi-level hyper-graph partitioning approach. It describes a new method of aggregation in the coarsening step that helps to accurately coarsen the hyper-graph of the task model. The Raccoon Optimization algorithm is then used in the initial partitioning phase, and in the un-coarsening phase, a novel refinement procedure optimises the initial partitions. The experiments on this approach showed that, compared to the other well-known algorithms, the proposed method offers better schedules with lower communication volume and imbalance ratio in a shorter time.

Image deepfakes have advanced rapidly due to continuous improvements in generative models, including generative adversarial networks, diffusion models, and neural rendering techniques. However, whether tested on unseen manipulation techniques or in real-world scenarios like compression, noise, and social media processing, current detection techniques typically show insufficient robustness. Peer-reviewed research on image-based deepfake identification that was published between 2020 and 2025 is examined in this comprehensive literature analysis. It focuses on four key aspects: (1) datasets used for training and evaluation, (2) feature representations, (3) model architectures that reduce reliance on shallow forensic artifacts, and (4) recent advancements in generalization strategies along with the associated challenges that limit adaptability to evolving manipulations. The findings identify trends in dataset design, feature engineering, and model development, while highlighting limitations in current evaluation protocols. This review provides a structured overview of the field and outlines future research directions toward developing robust, generalizable, and practically deployable image deepfake detection systems.