Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Abstract

Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

A techno‐economic assessment and optimization of Dumat Al‐Jandal wind farm in Kingdom of Saudi Arabia

“…The optimization objective is to minimize COE, which is a classic single objective optimization problem. In this study, a well‐known particle swarm‐based‐optimization approach is applied to find the ideal parameters matching to the minimization of COE 34–36 . The fitness function and constraints are presented in Equation ().…”

“…In this study, a well-known particle swarmbased-optimization approach is applied to find the ideal parameters matching to the minimization of COE. [34][35][36] The fitness function and constraints are presented in Equation (12).…”

Optimization for the Redundancy Allocation Problem of Reliability Using an Improved Particle Swarm Optimization Algorithm

“…e classical PSO algorithm can easily become premature and fall into the local extremum and is difficult to functionalize in complex nonlinear problems. is study introduces a new formulation of the inertia coefficient, acceleration coefficient, velocity, and position updating to improve the accuracy and fitness of the technique [33].…”

Research on Improved Particle Swarm Computational Intelligence Algorithm and Its Application to Multi-Objective Optimisation

“…In the real world, optimization problems widely exist in scientific research, engineering practice, production management, system control, structural design, crop layout, economic planning, resource allocation, and many other fields. They are increasingly receiving the attention of researchers at home and abroad [5][6][7][8]. Methods taken to find the optimal solution to a problem are called optimization methods, which are generated under some reasonable ideas and mechanisms and operate according to the corresponding rules [9].…”