Guimin Chen

Bio: Guimin Chen is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Compliant mechanism & Deflection (engineering). The author has an hindex of 23, co-authored 60 publications receiving 1664 citations. Previous affiliations of Guimin Chen include Xidian University & Brigham Young University.

A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight

Abstract: The inertia weight is often used to control the global exploration and local exploitation abilities of particle swarm optimizers (PSO). In this paper, a group of strategies with multi-stage linearly-decreasing inertia weight (MLDW) is proposed in order to get better balance between the global and local search. Six most commonly used benchmarks are used to evaluate the MLDW strategies on the performance of PSOs. The results suggest that the PSO with W5 strategy is a good choice for solving unimodal problems due to its fast convergence speed, and the CLPSO with W5 strategy is more suitable for solving multimodal problems. Also, W5-CLPSO can be used as a robust algorithm because it is not sensitive to the complexity of problems for solving.

A Comprehensive Elliptic Integral Solution to the Large Deflection Problems of Thin Beams in Compliant Mechanisms

Abstract: The elliptic integral solution is often considered to be the most accurate method for analyzing large deflections of thin beams in compliant mechanisms. In this paper, a comprehensive solution based on the elliptic integrals is proposed for solving large deflection problems. By explicitly incorporating the number of inflection points and the sign of the end-moment load in the derivation, the comprehensive solution is capable of solving large deflections of thin beams with multiple inflection points and subject to any kinds of load cases. The comprehensive solution also extends the elliptic integral solutions to be suitable for any beam end angle. Deflected configurations of complex modes solved by the comprehensive solution are presented and discussed. The use of the comprehensive solution in analyzing compliant mechanisms is also demonstrated by examples.

Kinetostatic and Dynamic Modeling of Flexure-Based Compliant Mechanisms: A Survey

Abstract: Flexure-based compliant mechanisms are becoming increasingly promising in precision engineering, robotics, and other applications due to the excellent advantages of no friction, no backlash, no wear, and minimal requirement of assembly. Because compliant mechanisms have inherent coupling of kinematic-mechanical behaviors with large deflections and/or complex serial-parallel configurations, the kinetostatic and dynamic analyses are challenging in comparison to their rigid-body counterparts. To address these challenges, a variety of techniques have been reported in a growing stream of publications. This paper surveys and compares the conceptual ideas, key advances, and applicable scopes, and open problems of the state-of-the-art kinetostatic and dynamic modeling methods for compliant mechanisms in terms of small and large deflections. Future challenges are discussed and new opportunities for extended study are highlighted as well. The presented review provides a guide on how to select suitable modeling approaches for those engaged in the field of compliant mechanisms.

Inertia Weight strategies in Particle Swarm Optimization

Abstract: Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.

Parameter selection in particle swarm optimisation: a survey

Abstract: Nowadays, particle swarm optimisation (PSO) is one of the most commonly used optimisation techniques. However, PSO parameters significantly affect its computational behaviour. That is, while it exposes desirable computational behaviour with some settings, it does not behave so by some other settings, so the way for setting them is of high importance. This paper explains and discusses thoroughly about various existent strategies for setting PSO parameters, provides some hints for its parameter setting and presents some proposals for future research on this area. There exists no other paper in literature that discusses the setting process for all PSO parameters. Using the guidelines of this paper can be strongly useful for researchers in optimisation-related fields.

A novel stability-based adaptive inertia weight for particle swarm optimization

Abstract: This paper presents "A novel adaptive inertia weight with stability condition for particle swarm optimization (SAIW)". This approach determines the inertia weight in different dimensions for each particle on: (1) its performance and (2) distance from its best position, and by considering the stability condition, the acceleration parameters of PSO are adaptively determined. Presents an adaptive method for finding inertia weight in different dimensions for each particle.The success of the particle and displacement in particle's best position are used as the feedback.Stability analysis of proposed model indicates that its performance is usually optimal.The results clearly show the superiority of the proposed model over the existing methods. Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the PSO algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of PSO are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of SAIW is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the PSO performance in terms of the solution quality as well as convergence speed in static and dynamic environments.