Multi-objective Gaussian particle swarm algorithm optimization based on niche sorting for actuator design

Abstract

With the fast-developing electromagnetic valve actuator, multi-objective optimal methods for actuator problems have been widely concerned in recent years. This article presents a modified multi-objective particle swarm optimization algorithm based on sorting method and employs it to the product design of the actuator. The simulation results show that modified optimization algorithm could obtain a better Pareto front in contrast to classical non-dominated sorting genetic algorithm-II method, meanwhile preserving the high capacity of fast solving of non-dominated solutions.

Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning

“…The eMuPSO algorithm was first implemented together with the LLS for optimizing the observation matrix and dynamic parameter estimation of all the six links of the 6DOF robot manipulator. The ideal parameters of the manipulator are given in (25)(26)(27)(28)(29)(30)(31)(32). Then the proposed algorithm was finally implemented in evaluating thirty-six benchmark functions including twenty-four variable-dimension benchmark functions and twelve constantdimension benchmark functions.…”

“…Likewise, the length d6. • When determining the 6 th joint angle from the excitation trajectory vector with (27), the initial joint position is unknown, so it is recommended to assume the initial joint position at the first segment (@ it = 1) is equal to the joint position at the last time segment to reduce the overshoot in velocity.…”

“…It was established in [2] that even the most intelligent swarm-based solutions are capable of running into stagnation, random mutation was used to push the algorithm out of stagnation and enhance convergence. The Gaussian mutation was used in [27] to improve swarm performance. Reference [28] also used a Gaussian mutation throw point strategy to redistribute the swarm at sparse positions.…”

A Multi-Objective Particle Swarm Optimization Algorithm Based on Gaussian Mutation and an Improved Learning Strategy

“…For the multi-objective problems, Coelho et al [23] used Gaussian mutation to update the velocity update formula, but they only replaced the uniform random number R with a Gaussian random number Gd in the velocity formula. Liang et al [24] also introduced Gaussian mutation, which will have a certain probability to initialize the particle adjacent to the target particle. Meanwhile, this will randomly initialize the particles beyond the range to increase the utilization rate of particles.…”

An improved multi-objective particle swarm optimization and its application in raw ore dispatching

“…Recently, some scholars have improved the MOPSO by introducing chaotic sequences 4 and mutations. 5 There are numerous MOPSO applications, such as actuator designs, 6 train scheduling for urban rail transit, 7 electrical distribution systems, 8 and the design of efficient automatic train operation (ATO) systems' speed profiles for metro lines. 9 An iron mine group contains a number of stopes and concentrating mills with years of development.…”