A new metaheuristic optimization methodology based on fuzzy logic

Díaz-Cortés, Margarita Arimatea; Cuevas, Erik; Gálvez, Jorge; Camarena, Octavio

doi:10.1016/j.asoc.2017.08.038

Cited by 28 publications

(6 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They define shape of d i . Larger-the-bester : in which r is the desirability index. Single quality index ( D ) corresponds to the weight value ( w i ). In this paper, the lower-the-bester is used for the stress and the larger-the-bester is applied for the displacement. Phase 3: Modeling SCOF by fuzzy logic A fuzzy logic theory can be found in the literature review (Díaz-Cortés et al , 2017). A decision is made by FIS.…”

Section: Description Of the Proposed Hybrid Computational Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid computational method for optimization design of bistable compliant mechanism

Chau

Tran

Dao

2020

View full text Add to dashboard Cite

Purpose Compliant mechanism has been receiving a great interest in precision engineering. However, analytical methods involving their behavior analysis is still a challenge because there are unclear kinematic behaviors. Especially, design optimization for compliant mechanisms becomes an important task when the problem is more and more complex. Therefore, the purpose of this study is to design a new hybrid computational method. The hybridized method is an integration of statistics, numerical method, computational intelligence and optimization. Design/methodology/approach A tensural bistable compliant mechanism is used to clarify the efficiency of the developed method. A pseudo model of the mechanism is designed and simulations are planned to retrieve the data sets. Main contributions of design variables are analyzed by analysis of variance to initialize several new populations. Next, objective functions are transformed into the desirability, which are inputs of the fuzzy inference system (FIS). The FIS modeling is aimed to initialize a single-combined objective function (SCOF). Subsequently, adaptive neuro-fuzzy inference system is developed to modeling a relation of the main geometrical parameters and the SCOF. Finally, the SCOF is maximized by lightning attachment procedure optimization algorithm to yield a global optimality. Findings The results prove that the present method is better than a combination of fuzzy logic and Taguchi. The present method is also superior to other algorithms by conducting non-parameter tests. The proposed computational method is a usefully systematic method that can be applied to compliant mechanisms with complex structures and multiple-constrained optimization problems. Originality/value The novelty of this work is to make a new approach by combining statistical techniques, numerical method, computational intelligence and metaheuristic algorithm. The feasibility of the method is capable of solving a multi-objective optimization problem for compliant mechanisms with nonlinear complexity.

show abstract

Section: Description Of the Proposed Hybrid Computational Methodsmentioning

confidence: 99%

“…A fuzzy logic theory can be found in the literature review (Díaz-Cortés et al , 2017). A decision is made by FIS.…”

Section: Description Of the Proposed Hybrid Computational Methodsmentioning

confidence: 99%

A hybrid computational method for optimization design of bistable compliant mechanism

Chau

Tran

Dao

2020

View full text Add to dashboard Cite

show abstract

“…e fuzzy modeling comprises knowledge base, fuzzification process, inference engine, and defuzzification procedure. Specifics could be concisely illustrated in the literature [58][59][60]. Firstly, the fuzzification process utilizes membership functions (MFs) to fuzzify the S/N proportions.…”

Section: Phase (Ii): Fuzzy Modelingmentioning

confidence: 99%

Optimization for a Flexure Hinge Using an Effective Hybrid Approach of Fuzzy Logic and Moth-Flame Optimization Algorithm

Dang

Chau

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Flexure hinge is a critical element in a positioner of a nanoindentation tester. To effectively work, a suitable flexure hinge should simultaneously meet multiple objectives, including rotation axis shift, safety factor, and angular deflection. The main aim of this article was to illustrate a hybrid method of the Taguchi method, fuzzy logic, response surface method, and Moth-flame optimization algorithm to solve the design optimization of a flexure hinge in order to enhance the three quality characteristics of the flexure hinge. Firstly, four common flexure hinges are compared together to seek the best suitable one. Secondly, numerical experiments are gathered via the Taguchi-based detasFlex software. Thirdly, three objective functions are transferred into signal to noises in order to eliminate the unit differences. Later on, fuzzy modeling is proposed to interpolate these three objective functions into one integrated objective function. An integrated regression equation is built using the response surface method. Finally, the flexure hinge is optimized by the Moth-flame optimization algorithm. The results found that the rotation axis shift is 10.944∗10−5 mm, the high safety factor is 2.993, and the angular deflection is 52.0058∗10−3 rad. The verifications are in a suitable agreement with the forecasted results. An analysis of variance and sensitivity analysis are also performed to identify the effects and meaningful contributions of input variables on the integrated objective function. In addition, employing the Wilcoxon signed rank test and the Friedman test, the results find that the proficiency of the proposed method has more benefits than the ASO algorithm and the GA. The results of this research provide a beneficial approach for conducting complicated multiobjective optimal problems.

show abstract

“…An important fraction of these methods is based on the social behaviour of a group of individuals of a determined live species. One of them considers, as a source of inspiration, human reasoning to make decisions when faced with fuzzy data [28]. Some of these techniques are: grey wolf optimizer (GWO) [29] (it imitates the command hierarchy and hunting strategy of grey wolves), the pity beetle algorithm (PBA) [30] (it was inspired by the grouping behaviour of the beetle Pityogenes chalcographus, looking for food and nests), shark smell optimization (SSO) [31] (it simulates the skill of a shark for finding their prey by using its sense of smell and moving toward the source of the odour), symbiotic organisms search (SOS) [32] (mimics the symbiotic interaction strategies followed by organisms to survive and propagate in the ecosystem), dolphin echolocation (DOE) [33] (it considers the echolocation system used by dolphins in searching for food), the whale optimization algorithm (WOA) [34] (it mimics the social behaviour of humpback whales), and the emperor penguin optimizer (EPO) [35] (it simulates the huddling behaviour of emperor penguins (Aptenodytes forsteri)).…”

Section: Introductionmentioning

confidence: 99%

A New Metaheuristic Inspired by the Vapour-Liquid Equilibrium for Continuous Optimization

et al. 2018

View full text Add to dashboard Cite

In this article, a novel optimization metaheuristic based on the vapour-liquid equilibrium is described to solve highly nonlinear optimization problems in continuous domains. During the search for the optimum, the procedure truly simulates the vapour-liquid equilibrium state of multiple binary chemical systems. Each decision variable of the optimization problem behaves as the molar fraction of the lightest component of a binary chemical system. The equilibrium state of each system is modified several times, independently and gradually, in two opposite directions and at different rates. The best thermodynamic conditions of equilibrium for each system are searched and evaluated to identify the following step towards the solution of the optimization problem. While the search is carried out, the algorithm randomly accepts inadequate solutions. This process is done in a controlled way by setting a minimum acceptance probability to restart the exploration in other areas to prevent becoming trapped in local optimal solutions. Moreover, the range of each decision variable is reduced autonomously during the search. The algorithm reaches competitive results with those obtained by other stochastic algorithms when testing several benchmark functions, which allows us to conclude that our metaheuristic is a promising alternative in the optimization field.

show abstract

A new metaheuristic optimization methodology based on fuzzy logic

Cited by 28 publications

References 44 publications

A hybrid computational method for optimization design of bistable compliant mechanism

A hybrid computational method for optimization design of bistable compliant mechanism

Optimization for a Flexure Hinge Using an Effective Hybrid Approach of Fuzzy Logic and Moth-Flame Optimization Algorithm

A New Metaheuristic Inspired by the Vapour-Liquid Equilibrium for Continuous Optimization

Contact Info

Product

Resources

About