Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-optimized functional quality (i.e., Quality of Semantic Matchmaking) or non-functional quality (i.e., Quality of Service) or both due to the complexity of this problem. With a significant increase in service composition requests, many composition requests have similar input and output requirements but may vary due to different preferences from different user segments. This problem is often treated as a multi-objective service composition so as to cope with different preferences from different user segments simultaneously. Without taking a multi-objective approach that gives rise to a solution selection challenge, we perceive multiple similar service composition requests as jointly forming an evolutionary multi-tasking problem in this work. We propose an effective permutation-based evolutionary multi-tasking approach that can simultaneously generate a set of solutions, with one for each service request. We also introduce a neighborhood structure over multiple tasks to allow newly evolved solutions to be evaluated on related tasks. Our proposed method can perform better at the cost of only a fraction of time, compared to one state-of-art single-tasking EC-based method. We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.

Quality-of-service (QoS)-aware automated semantic Web service composition aims to find a composite service with optimized or near-optimized QoS and quality of semantic matchmaking within polynomial time. To cope with this NP-hard problem with high complexity, a variety of evolutionary computation (EC) techniques has been developed. To improve the effectiveness and efficiency of these techniques, in this article, we proposed a novel memetic estimation of the distribution algorithm-based approach, namely, MEEDA, to tackle this problem. In particular, MEEDA explores four different domain-dependent local search methods that search for effective composite services by utilizing several neighborhood structures. Apart from that, to significantly reduce the computational time of MEEDA, an efficient local search strategy is introduced by combining a uniform fitness distribution scheme for selecting suitable solutions and stochastic local search operators for effectively and efficiently exploiting neighbors. To better demonstrate MEEDA’s effectiveness and scalability, we create a more challenging, augmented version of the service composition benchmark dataset. Experimental results on this benchmark show that MEEDA with newly developed domain-dependent local search operator, i.e., layer-based constrained one-point swaps, significantly outperforms existing state-of-the-art algorithms in finding high-quality composite services.

Cloud computing enables enormous computational resources to be scheduled as parallel workflow applications. Most traditional heuristics can only solve one particular scheduling problem. For example, Heterogeneous Earliest Finish Time (HEFT) and Greedy algorithms allocate resources to given ordered list of tasks using a specific single heuristic, which only caters for a specific scheduling problem, e.g. the fixed number of tasks in a workflow and available resources. Many researchers considered the heterogeneous workflows and cloud resources in scheduling in order to minimize the cost and makespan, but the solutions provided are only for specific workflow pattern. In this paper, we demonstrate a workflow scheduling problem which considers the combination of heterogeneous workflows as well as heterogeneous computing resources. We proposed Flexible Scheduling using Genetic Programming (FSGP) approach to min-imize the total cost and makespan of heterogeneous workflows in the cloud. The performance of our proposed FSGP is regardless of the number of tasks in the workflow, available resources and workflow patterns. We evaluated our proposed approach using a benchmark dataset. Performance evaluation of some well-known algorithms such as HEFT and greedy algorithms exhibit that our FSGP approach perform better than other competing algorithms.