Non-dominated Particle Swarm Optimization Research Articles

Workload Allocation Toward Energy Consumption-Delay Trade-Off in Cloud-Fog Computing Using Multi-Objective NPSO Algorithm

The Internet of Things (IoT) generates massive data from smart devices that demand responses from cloud servers. However, sending tasks to the cloud reduces the power consumed by the users’ devices, but increases the transmission delay of the tasks. In contrast, sending tasks to the fog server reduces the transmission delay due to the shorter distance between the user and the server. However, this occurs at the user end’s expense of higher energy consumption. Thus, this study proposes a mathematical framework for workload allocation to model the power consumption and delay functions for both fog and clouds. After that, a Modified Least Laxity First (MLLF) algorithm was proposed to reduce the maximum delay threshold. Furthermore, a new multi-objective approach, namely the Non-dominated Particle Swarm Optimization (NPSO), is proposed to reduce energy consumption and delay compared to the state-of-the-art algorithms. The simulation results show that NPSO outperforms the state-of-the-art algorithm in reducing energy consumption, while NGSA-II proves its effectiveness in reducing transmission delay compared to the other algorithms in the experimental simulation. In addition, the MLLF algorithm reduces the maximum delay threshold by approximately 11% compared with other related algorithms. Moreover, the results prove that metaheuristics are more appropriate for distributed computing.

The development of a hybrid firefly algorithm for multi-pass grinding process optimization

Industrial grinding processes always involve multiple sequential passes, such as rough, semi-rough, semi-finish, finish, and spark-out, for the surface and geometrical accuracy generation. The design of multi-pass grinding process parameters usually requires in-depth heuristic knowledge or complex process modeling. However, when multiple process output objectives must be achieved, either heuristic knowledge or analytical modeling is incapable in dealing with the large number of process parameters or decoupling the dependency of individual pass with its neighboring passes. In this paper, a hybrid Non-dominated Sorting Firefly Algorithm (NSFA) is proposed by incorporating the non-dominated sorting algorithm with the firefly algorithm. The developed NSFA is capable in searching the optimal whole set of grinding process parameters at improved convergence speed and with less iterations, which is proved by comparing with Non-dominated Genetic Algorithm (NSGA-II) and Non-dominated Particle Swarm Optimization (NSPSO). Finally, a variable-pass internal grinding for engineering ceramics is carried out to verify the efficacy of the proposed NSFA. With the process output objectives of minimal grinding time and geometric error, the optimized process by the NSFA can realize the achievement of all process quality objectives simultaneously with the grinding efficiency increased by 32.4%.