Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph.

The capacitated vehicle routing problem (CVRP) is an NP-hard combinatorial optimization problem that plays a significant role in end distribution in smart logistics. The CVRP solution can be denoted as a permutation of given customers. The solutions in the continuous-valued swarm intelligence (SI) algorithms (i.e., particle swarm optimization, ant colony optimization, HHO, etc.) for permutation problems are represented as continuous-valued vectors. These continuous-valued vectors must be converted into permutations to compute the fitness values. However, there exist potentially infinite continuous-valued vectors corresponding to a single permutation due to cardinality reasons, thus inserting huge plateaus in the fitness landscape navigated by the underlying SI. An algebraic Harris hawks optimization (AHHO) algorithm is proposed for permutation-based CVRP to optimize the total traveled distance. The proposed AHHO algorithm is the algebraic version of Harris hawks optimization (HHO), and it simulates the hunting moves of Harris hawks in permutation-based solution space. The proposed AHHO employs a novel discrete version of Lévy flights based on Lévy distribution and local search operators. The performance of the proposed AHHO algorithm is estimated on 85 well-known CVRP benchmark instances. The proposed AHHO algorithm offers promising results compared to random-key HHO, hybrid firefly, and cuckoo search with Lévy flights algorithms. <!----> <!----> <!----> <!---->

Cloud Service Providers are speedily becoming the target platform for scientific workflow computations due to the massive possible and flexible pay-as-you-go pricing model. Workflow allocation problem in cloud systems is considered NP-hard. A heterogeneous IaaS cloud could be fully effective if the allocation method provides an efficient mapping between virtual machines (VMs) and workflow applications demanding execution. First, we model multiple workflow allocation problem in the cloud environment. Then, we propose a levelized multiple workflow allocation strategy with task merging (LMWS-TM) to optimize turnaround time for multiple workflow applications in the Infrastructure as a Service (IaaS) cloud environment to achieve better performance. The task merging scheme is incorporated into workflows after partitioning and prior to allocation to reduce inter-task communication share and the total number of depth levels for improving the overall completion time. Moreover, it considers inter-task communication and inter-machine distance for estimating communication cost share among tasks on the schedule generated. Furthermore, the scheme is capable enough to use simple and flexible level attributes to tackle precedence constraints. Afterward, we conducted an experimental study to evaluate LMWS-TM by comparative performance analysis with its peers, namely SLBBS, DLS, and HEFT, on quality of service (QoS) parameters, namely, turnaround time, system utilization, flow time, and response time. The study reveals the superior performance of LMWS-TM among its considered peers in almost all the cases for almost all considered parameters under investigation. Finally, we performed statistical testing to test the significance level using SPSS 20, confirming the hypothesis drawn in the experimental study.