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.

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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 the 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 paper, 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 the ACO and flying ACO (FACO) algorithms. Two different types of experiments using 22 datasets were done with 30 independent repetitions. The first type of experiment's results shows 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.INDEX TERMS Service-oriented computing (SOC), web services composition (WSC), web service (WS), web service selection (WSS), ant colony optimization (ACO).

Web Service Composition (WSC) provides a flexible framework for integrating independent web services to satisfy complex user requirements. WSC aims to choose the best web service from a set of candidates. The candidates have the same functionality and different non-functional criteria such as Quality of Service (QoS). In this work, the authors propose an ant-inspired algorithm for such problem. They named it Flying Ant Colony Optimization (FACO). Flying ants inject pheromone not only on the nodes on their paths but also on neighboring nodes increasing their chances of being explored in future iterations. The amount of pheromone deposited on these neighboring nodes is inversely proportional to the distance between them and the nodes on the path. The authors believe that by depositing pheromone on neighboring nodes, FACO may consider a more diverse population of solutions, which may avoid stagnation. The empirical experiments show that FACO outperform Ant Colony Optimization (ACO) for the WSC problem, in terms of the quality of solutions but it requires slightly more 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.