This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time.

Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.

Reinforcement learning (RL) has gained prominence in healthcare due to its ability to optimize treatment strategies without relying on predefined mathematical models. However, existing approaches face critical challenges: (1) the optimality of learned strategies is assessed without considering treatment risks, potentially leading to unsafe recommendations; (2) distribution shift issues cause learned strategies to diverge from physician decisions; and (3) past observational data and treatment history are often overlooked, leading to suboptimal state representations. To address these limitations, we propose a Dynamic Treatment Strategy Generation Model that integrates Dead Ends with an Offline Supervised Actor-Critic approach (DOSAC-DTR). Our model incorporates Dead Ends into the Actor-Critic framework to evaluate risks associated with recommended treatments. Additionally, physician oversight is embedded to mitigate distribution shift and align the learned strategy with expert decisions while maximizing expected outcomes. To enhance state representation, we employ an LSTM-based encoder-decoder model to capture essential patient history, ensuring robust decision-making. Experimental results on real-world datasets (MIMIC-III) demonstrate that DOSAC-DTR significantly reduces mortality rates (Sepsis: 3.51%, Ventilation: 13.74%) and improves treatment alignment with physicians (Jaccard similarity: 0.362, 0.126) compared to baseline models. These findings underscore the potential of reinforcement learning in personalized healthcare, improving both treatment efficacy and patient safety.

Recently, service composition has gained increased attention as an auspicious paradigm to optimize the data accessibility, integrity, and interoperability of cloud computing. In this work, to solve the cloud service composition (CSC) problem, we introduce an efficient agent-based ant colony optimization (ACO) algorithm. The CSC problem aims to satisfy complex and challenging requirements of enterprises/users in a cloud environment. The challenge of such problem is the proliferation of providing similar services having similar functionality with varying quality of service (QoS) properties from different providers. Several swarm-based algorithms were introduced to solve this problem because the complexity of the problem is characterized as NP-hard, which is high. These algorithms aim to maintain a good balance between exploration and exploitation mechanisms, and to achieve this, a multi-agent based on ACO is proposed and compared with four different algorithms using 25 different real datasets. The computational results on 25 real datasets confirm the effectiveness of the multi-agent distribution of ACO process. Moreover, comparisons against the results of the four algorithms in the literature indicate that the multi-agent ACO approach is competitive with state-of-the-art algorithms.

In service-oriented computing, web services composition is the process of translating user requirements into a workflow. This workflow comprises many tasks, each of which includes an abstract definition for some of the user requirements. Web services can be aggregated to handle a workflow. Many of these services are available from various providers for each task; they are referred to, in aggregate, as the candidate list. The web service selection (WSS) problem centers on selecting the best service from these candidates based on the quality of service (QoS) features. In this work, we propose an enhancement to the ant colony optimization (ACO) algorithm based on a swap concept for the QoS-aware WSS problem. The aim of the enhancement to the ACO is to avoid the trap of local optima and reduce the search duration. We believe that the integration of many potent solutions will help the ACO algorithm yield a better solution and avoid stagnation. Several experiments were conducted to compare the proposed algorithm with ACO and flying ACO (FACO) algorithms. Two different type of experiments using 22 datasets were done with 30 independent repetitions. The first type of experiment’s results show that the proposed algorithm is better than ACO by 12% and FACO by 11% in terms of quality of solutions. The results in the second type of experiment show that the proposed algorithm continuously outperforms both algorithms in terms of quality of solutions.

This paper presents an enhanced artificial bee colony (ABC) algorithm for solving the web service selection problem. The proposed algorithm searches best possible combination of web services to satisfy user requirements. An adapted neighborhood selection and replacement process, and a swapping process, are used to improve ABC behavior. Neighboring nodes are employed to enhance ABC performance by encouraging exploration in early iterations, where bees have no knowledge regarding the search space, and by encouraging exploitation in later iterations to exploit bee knowledge of the search space. The swapping process is used to enhance ABC performance by randomly swapping portions amongst the best two solutions randomly. The idea behind this swap is to exploit the characteristics of the best solutions to generate new solutions. We compared the proposed algorithm with other algorithms in terms of quality and execution time using 60 different datasets. These datasets have different number of tasks and web services. The results indicate that the proposed algorithm finds better solutions compared to other algorithms. In addition, the results summarization on 60 datasets with 30 different executions show that the proposed algorithm outperforms the threshold-based algorithm (TBA) by 6% and the enhanced ABC (EABC) by 3% in terms of solutions quality.