A Comparison of Sequential Quadratic Programming, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization and Hybrid Algorithm for the Design and Optimization of Golinski’s Speed Reducer

Abstract

This article provides information on different optimization methods such as Sequential Quadratic Programming (SQP), Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Hybrid Algorithm (HA). Optimization is a method of designing a system in such a manner that it falls into all limitations on design and satisfies all the design parameters provided. In this particular study, Matlab software is used to perform these optimization methods. It is very helpful software with wide range of applications. One of the such applications is optimization toolbox which is called optimtool. It contains readily written codes for different optimization tools. After conducting optimization on Golinski’s speed reducer with five various optimization method, the results are in kilograms for the weight optimization which are SQP = 2994.355 kg, GA = 2994.914 kg, SA = 2730.74 kg, HA = 2994.355 kg, and PSO = 2905.677. The figure below, which is thought graphically abstract, represents a result of all optimizations.

Keywords

• optimization
• golinski’s speed reducer
• design parameters
• limitation

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