A Hybrid Whale Optimization Algorithm for Quality of Service-Aware Manufacturing Cloud Service Composition

Abstract

Cloud Manufacturing (CMfg) has attracted lots of attention from scholars and practitioners. The purpose of quality of service (QoS)-aware manufacturing cloud service composition (MCSC), as one of the key issues in CMfg, is to combine different available manufacturing cloud services (MCSs) to generate an optimized MCSC that can meet the diverse requirements of customers. However, many available MCSs, deployed in the CMfg platform, have the same function but different QoS attributes. It is a great challenge to achieve optimal MCSC with a high QoS. In order to obtain better optimization results efficiently for the QoS-MCSC problems, a whale optimization algorithm (WOA) with adaptive weight, Lévy flight, and adaptive crossover strategies (ASWOA) is proposed. In the proposed ASWOA, adaptive crossover inspired by the genetic algorithm is developed to balance exploration and exploitation. The Lévy flight is designed to expand the search space of the WOA and accelerate the convergence of the WOA with adaptive crossover. The adaptive weight is developed to extend the search scale of the exploitation. Simulation and comparison experiments are conducted on various benchmark functions and different scale QoS-MCSC problems. The QoS attributes of the problems are randomly and symmetrically generated. The experimental results demonstrate that the proposed ASWOA outperforms other compared cutting-edge algorithms.

Comparison of Selected Numerical Methods for Solving Integro-Differential Equations with the Cauchy Kernel

“…The applications of these algorithms are wide, and some of them can be found in the papers [29][30][31][32][33][34]. However, the articles [35][36][37][38] present the applications of ABC and WOA in various types of symmetry problems.…”

Cloud Manufacturing Service Composition Optimization Based on Improved Chaos Sparrow Search Algorithm with Time-Varying Reliability and Credibility Evaluation

“…The optimization of CMfg SvcComp is characterized by its multi-objective nature, nonlinearity, and inherent uncertainty. Consequently, numerous scholars are devoting themselves to addressing the intricate NP problem [5]. Scholars primarily focus on production cost, execution time, and profit as key optimization parameters in their exploration of SvcComp modeling and optimization algorithms.…”

“…5 and Z i,6 represent the six objective function values pertaining to the CMfg SvcComp scheme for the i-th sparrow. Additionally, Z r,1 , Z r,2 , Z r,3 , Z r,4 , Z r,5 and Z r,6 serve as the reference values for the six objective functions, established during the initialization phase of the algorithm.…”